The Consumer/Survivors Movement Essay -- Mental health,Psychiatry, Res

Methods This paper focuses on the current initiatives and electronic/ paper resources created to further the claims of the Consumer/Survivors movement. The search of my data included searches including, C/S/X, mental health consumers movement, MAD pride, anti-psychiatry, mental health movements. I chose articles and websites based on their relevance to the Consumer/ Survivor movement which included information provided by consumers themselves and their allies (organizations and/ or individual/ groups that were pro C/S/X movement.) First, I researched articles, both from peer reviewed journals, periodicals, websites written by allies of consumers about the C/S/X, their motives, views etc to get obtain some background information about the movement and look into other sources of information. Next, I collected information from ally organizations such as CAMH and Community Resources Toronto. These site provided information about the activities of some of the C/S./X groups including resources that were available to them and created by them. some of the resources included: community bulletin, community program evaluations ( which looked into the effectiveness of the resources provided to mental health consumers. ) Third, I looked into personal websites, YouTube videos, blogs, and books about survivors and/or consumers experiences within the mental health system. Many of the searches resulted in experiences around psychiatry. Finally, I looked at sources pertaining to the MAD pride movement including their webs ite, bulletins, YouTube channel, MAD ‘zines’ ( MAD pride magazines), newspaper articles written by individuals within the MAD movement. I particularly paid specific attention to their mission statement, activities within t... ...llness. A Report on the Fifth International Stigma Conference . June 4–6, 2012. Ottawa, Canada qldalliance ( Jan21 ,2008. ) Visions Retrieved From : http://www.youtube.com/watch?v=0w89Rh9pCIk Rosen, G. (1968) Madness in Society. New York: Harper Torchbooks, Schrater,S., Jones,N., and Shattell, M. (2013)Mad Pride: Reflections on Sociopolitical Identity and Mental Diversity in the Context of Culturally Competent Psychiatric Care. Issues in Mental Health Nursing, 34. 62–64. Shea, P. B. (1999). Defining madness (No. 12). Hawkins Press. Thornicroft, G., & Tansella, M. (2005). Growing recognition of the importance of service user involvement in mental health service planning and evaluation. Epidemiologia e Psichiatria Sociale, 14(01), 1-3. Wahl, O. F. (1999). Mental health consumers' experience of stigma. Schizophrenia Bulletin, 25(3), 467-478.

Research on Warehouse Design

European Journal of Operational Research 203 (2010) 539–549 Contents lists available at ScienceDirect European Journal of Operational Research journal homepage: www. elsevier. com/locate/ejor Invited Review Research on warehouse design and performance evaluation: A comprehensive review Jinxiang Gu a, Marc Goetschalckx b,*, Leon F. McGinnis b a b Nestle USA, 800 North Brand Blvd. , Glendale, CA 91203, United States Georgia Institute of Technology, 765 Ferst Dr. , Atlanta, GA 30332-0205, United States a r t i c l e i n f o a b s t r a c tThis paper presents a detailed survey of the research on warehouse design, performance evaluation, practical case studies, and computational support tools. This and an earlier survey on warehouse operation provide a comprehensive review of existing academic research results in the framework of a systematic classi? cation. Each research area within this framework is discussed, including the identi? cation of the limits of previous research and of potential future research directions. O 2009 Elsevier B. V. All rights reserved.Article history: Received 5 December 2005 Accepted 21 July 2009 Available online 6 August 2009 Keywords: Facilities design and planning Warehouse design Warehouse performance evaluation model Case studies Computational tools 1. Introduction This survey and a companion paper (Gu et al. , 2007) present a comprehensive review of the state-of-art of warehouse research. Whereas the latter focuses on warehouse operation problems related to the four major warehouse functions, i. e. , receiving, storage, order picking, and shipping, this paper concentrates on warehouse design, performance evaluation, case studies, and computational support tools.The objectives are to provide an all-inclusive overview of the available methodologies and tools for improving warehouse design practices and to identify potential future research directions. Warehouse design involves ? ve major decisions as illustrated in Fig. 1: deter mining the overall warehouse structure; sizing and dimensioning the warehouse and its departments; determining the detailed layout within each department; selecting warehouse equipment; and selecting operational strategies. The overall structure (or conceptual design) determines the material ? ow pattern within the warehouse, the speci? ation of functional departments, and the ? ow relationships between departments. The sizing and dimensioning decisions determine the size and dimension of the warehouse as well as the space allocation among various warehouse departments. Department layout is the detailed con? guration within a warehouse department, for example, aisle con? guration in the retrieval area, pallet block-stacking pattern in the reserve storage area, and con? guration of an Automated Storage/Retrieval System (AS/RS). The equipment selection deci* Corresponding author. Tel. : +1 404 894 2317; fax: +1 404 894 2301. E-mail address: marc. [email  protected] gatech. edu (M. G oetschalckx). 0377-2217/$ – see front matter O 2009 Elsevier B. V. All rights reserved. doi:10. 1016/j. ejor. 2009. 07. 031 sions determine an appropriate automation level for the warehouse, and identify equipment types for storage, transportation, order picking, and sorting. The selection of the operation strategy determines how the warehouse will be operated, for example, with regards to storage and order picking. Operation strategies refer to those decisions about operations that have global effects on other design decisions, and therefore need to be considered in the design phase.Examples of such operation strategies include the choice between randomized storage or dedicated storage, whether or not to do zone picking, and the choice between sort-while-pick or sortafter-pick. Detailed operational policies, such as how to batch and route the order picking tour, are not considered design problems and therefore are discussed in Gu et al. (2007). It should be emphasized that w arehouse design decisions are strongly coupled and it is dif? cult to de? ne a sharp boundary between them. Therefore, our proposed classi? ation should not be regarded as unique, nor does it imply that any of the decisions should be made independently. Furthermore, one should not ignore operational performance measures in the design phase since operational ef? ciency is strongly affected by the design decisions, but it can be very expensive or impossible to change the design decisions once the warehouse is actually built. Performance evaluation is important for both warehouse design and operation. Assessing the performance of a warehouse in terms of cost, throughput, space utilization, and service provides feedback about how a speci? design or operational policy performs compared with the requirements, and how it can be improved. Furthermore, a good performance evaluation model can help the designer to quickly evaluate many design alternatives and narrow down the design space durin g the early design stage. Performance operational cost for each alternative is estimated using simple analytic equations. Gray et al. (1992) address a similar problem, and propose a multi-stage hierarchical approach that uses simple calculations to evaluate the tradeoffs and prune the design space to a few superior alternatives.Simulation is then used to provide detailed performance evaluation of the resulting alternatives. Yoon and Sharp (1996) propose a structured approach for exploring the design space of order picking systems, which includes stages such as design information collection, design alternative development, and performance evaluation. In summary, published research ndco4h lar02. 8659(war,. 0320Td[(pro2k evaluation methods include benchmarking, analytical models, and simulation models.This review will mainly focus on the former two since simulation results depend greatly on the implementation details and are less amenable to generalization. However, this should not obs cure the fact that simulation is still the most widely used technique for warehouse performance evaluation in the academic literature as well as in practice. Some case studies and computational systems are also discussed in this paper. Research in these two directions is very limited. However, it is our belief that more case studies and computational tools for warehouse design and operation will help to bridge the signi? ant gap between academic research and practical application, and therefore, represent a key need for the future. The study presented in this paper and its companion paper on operations, Gu et al. (2007), complements previous surveys on warehouse research, for example, Cormier (2005), Cormier and Gunn (1992), van den Berg (1999) and Rowenhorst et al. (2000). Over 250 papers are included within our classi? cation scheme. To our knowledge, it is the most comprehensive review of existing research results on warehousing.However, we make no claim that it includes all the literature on warehousing. The scope of this survey has been mainly focused on results published in available English-language research journals. The topic of warehouse location, which is part of the larger area of distribution system design, is not addressed in this current review. A recent survey on warehouse location is provided by Daskin et al. (2005). The next four sections will discuss the literature on warehouse design, performance evaluation, case studies, and computational systems, respectively. The ? al section gives conclusions and future research directions. 2. Warehouse design 2. 1. Overall structure The overall structure (or conceptual design) of a warehouse determines the functional departments, e. g. , how many storage departments, employing what technologies, and how orders will be assembled. At this stage of design, the issues are to meet storage and throughput requirements, and to minimize costs, which may be the discounted value of investment and future operating costs. We can identify only three published papers addressing overall structural design.Park and Webster (1989) assume the functions are given, and select equipment types, storage rules, and order picking policies to minimize total costs. The initial investment cost and annual J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 541 Levy (1974), Cormier and Gunn (1996) and Goh et al. (2001) consider warehouse sizing problems in the case where the warehouse is responsible for controlling the inventory. Therefore, the costs in their models include not only warehouse construction cost, but also inventory holding and replenishment cost.Levy (1974) presents analytic models to determine the optimal storage size for a single product with either deterministic or stochastic demand. Assuming additional space can be leased to supplement the warehouse, Cormier and Gunn (1996) propose closed-form solution that yields the optimal warehouse size, the optimal amount of space to lease in each period, and the optimal replenishment quantity for a single product case with deterministic demand. The multi-product case is modeled as a nonlinear optimization problem assuming that the timing of replenishments is not managed.Cormier and Gunn (1999) developed a nonlinear programming formulation for the optimal warehouse expansion over consecutive time periods. Goh et al. (2001) ? nd the optimal storage size for both single-product and multi-product cases with deterministic demand. They consider a more realistic piecewise linear model for the warehouse construction cost instead of the traditional linear cost model. Furthermore, they consider the possibility of joint inventory replenishment for the multi-product case, and propose a heuristic to ? nd the warehouse size.The effects of inventory control policies (e. g. , the reorder point and ordering quantity) on the total required storage capacity are shown by Rosenblatt and Roll (1988) using simulation. Our a bility to answer warehouse sizing questions would be signi? cantly enhanced by two types of research. First, assessing capacity requirements should consider seasonality, storage policy, and order characteristics, because these three factors interact to impact the achievable storage ef? ciency, i. e. that fraction of warehouse capacity that can actually be used effectively.Second, sizing models all employ cost models, and validation studies of these models would be a signi? cant contribution. 2. 2. 2. Warehouse dimensioning The warehouse dimensioning problem translates capacity into ? oor space in order to assess construction and operating costs, and was ? rst modeled by Francis (1967), who used a continuous approximation of the storage area without considering aisle structure. Bassan et al. (1980) extends Francis (1967) by considering aisle con? gurations. Rosenblatt and Roll (1984) integrate the optimization model in Bassan et al. 1980) with a simulation model which evaluates the s torage shortage cost, a function of storage capacity and number of zones. They assume single-command tours in order to evaluate the effect of warehouse dimension on the operational cost, and therefore their approach is not applicable to warehouses that perform multi-command operations (e. g. , interleaving put-away and retrieval, or retrieving multiple items per trip). The work discussed so far has approached the sizing and dimensioning problem assuming the warehouse has a single storage department.In reality, a warehouse might have multiple departments, e. g. , a forward-reserve con? guration, or different storage departments for different classes of Stock Keeping Units (SKUs). These different departments must be arranged in a single warehouse and compete with each other for space. Therefore, there are tradeoffs in determining the total warehouse size, allocating the warehouse space among departments, and determining the dimension of the warehouse and its departments. Research stud ying these tradeoffs in the warehouse area is scarce.Pliskin and Dori (1982) propose a method to compare alternative space allocations among different warehouse departments based on multi-attribute value functions, which explicitly capture the tradeoffs among different criteria. Azadivar (1989) proposes an approach to optimally allocate space between two departments: one is ef? cient in terms of storage but inef? cient in terms of operation, while the other is the opposite. The objective is to achieve the best system performance by appropriately allocating space between these two departments to balance the storage capacity and operational ef? iency tradeoffs. Heragu et al. (2005) consider a warehouse with ? ve functional areas, i. e. , receiving, shipping, cross-docking, reserve, and forward. They propose an optimization model and a heuristic algorithm to determine the assignment of SKUs to the different storage areas as well as the size of each functional area to minimize the total material handling and storage costs. A key issue with all research on the dimensioning problem is that it requires performance models of material handling; these models are often independent of the size or layout of the warehouse.Research is needed to either validate these models, or develop design methods that explicitly consider the impact of sizing and dimensioning on material handling. 2. 3. Department layout In this section we discus layout problems within a warehouse department, primarily a storage department. The storage problems are classi? ed as: (P1) pallet block-stacking pattern, i. e. , storage lane depth, number of lanes for each depth, stack height, pallet placement angle with regards to the aisle, storage clearance between pallets, and length and width of aisles; (P2) storage department layout, i. . , door location, aisle orientation, length and width of aisles, and number of aisles; and (P3) AS/RS con? guration, i. e. , dimension of storage racks, number of cranes. These layout problems affect warehouse performances with respect to: (O1) construction and maintenance cost; (O2) material handling cost; (O3) storage capacity, e. g. , the ability to accommodate incoming shipments; (O4) space utilization; and (O5) equipment utilization. Each problem is treated in the literature by different authors considering a subset of the performance measures, as summarized in Table 1. 2. 3. 1.Pallet block-stacking pattern (P1) In the pallet block-stacking problem, a fundamental decision is the selection of lane depths to balance the tradeoffs between space utilization and ease of storage/retrieval operations, considering the SKUs’ stackability limits, arriving lot sizes, and retrieval patterns. Using deep lane storage could increase space utilization because fewer aisles are needed, but on the other hand could also cause decreased space utilization due to the ‘‘honeycombing† effect that creates unusable space for the storage of other i tems until the whole lane is totally depleted.The magnitude of the honeycombing effect depends on lane depths as well as the withdrawal rates of individual products. Therefore, it might be bene? cial to store different classes of products in different lane depths. A careful determination and coordination of the lane depths for different products is necessary in order to achieve the best storage space utilization. Besides lane con? guration, the pallet block-stacking problem also determines such decisions as aisle widths and orientation, stack height, and storage clearance, which all affect storage space utilization, material handling ef? iency, and storage capacity. 542 J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 Table 1 A summary of the literature on warehouse layout design. Problem P1 Citation Moder and Thornton (1965) Berry (1968) Marsh (1979) Marsh (1983) Goetschalckx and Ratliff (1991) Larson et al. (1997) Roberts and Reed (1972) Bassan et al. (1980) Rosenblatt and Roll (1984) Pandit and Palekar (1993) P3 Karasawa et al. (1980) Ashayeri et al. 1985) Rosenblatt et al. (1993) Objective O4 O2, O4 O3, O4 O4 O2, O4 O1, O2 O1, O2 O1, O2, O3 O2 O1, O2, O3 O1, O2 O1, O2, O3 O1, O5 O1, O5 O1 Method Analytical formulae Analytical formulae Simulation models Heuristic procedure Heuristic procedure Dynamic Programming Optimal design using analytical formulation Optimal two-dimensional search method Queuing model Nonlinear mixed integer problem Nonlinear mixed integer problem Nonlinear mixed integer problem NotesMainly on lane depth determination For class-based storage Consider the con? guration of storage bays (unit storage blocks) Consider horizontal and vertical aisle orientations, locations of doors, and zoning of the storage area Based on Bassan et al’s work with additional costs due to the use of grouped storage Include not only the ordinary travel time, but also waiting time when all vehicles are busy The model is so lved by generalized Lagrange multiplier method Given rack height, the model can be simpli? d to a convex problem System service is evaluated using simulations, if not satisfactory, new constraints are added and the optimization model is solved again to get a new solution A more elaborated variation of Zollinger’s rules that consider explicitly operational policies For the design of an automated carousel system. The model is solved with a simple search algorithm P2 Zollinger (1996) Malmborg (2001) Lee and Hwang (1988) Rule of thumb heuristic Rule of thumb heuristic Nonlinear integer program A number of papers discuss the pallet block-stacking problem.Moder and Thornton (1965) consider ways of stacking pallets in a warehouse and the in? uence on space utilization and ease of storage and retrieval. They consider such design factors as lane depth, pallet placement angle with regards to the aisle, and spacing between storage lanes. Berry (1968) discusses the tradeoffs between stor age ef? ciency and material handling costs by developing analytic models to evaluate the total warehouse volume and the average travel distance for a given storage space requirement.The factors considered include warehouse shape, number, length and orientation of aisles, lane depth, throughput rate, and number of SKUs contained in the warehouse. It should be noted that the models for total warehouse volume and models for average travel distance are not integrated, and the warehouse layout that maximizes storage ef? ciency is different from the one that minimizes travel distance. Marsh (1979) uses simulation to evaluate the effect on space utilization of alternate lane depths and the rules for assigning incoming shipments to lanes.Marsh (1983) compares the layout design developed by using the simulation models of Marsh (1979) and the analytic models proposed by Berry (1968). Goetschalckx and Ratliff (1991) develop an ef? cient dynamic programming algorithm to maximize space utilizati on by selecting lane depths out of a limited number of allowable depths and assigning incoming shipments to the different lane depths. Larson et al. (1997) propose a three-step heuristic for the layout problem of class-based pallet storage with the purpose to maximize storage space utilization and minimize material handling cost. The ? st phase determines the aisles layout and storage zone dimensions; the second phase assigns SKUs to storage con? gurations; and the third phase assigns ? oor space to the storage con? gurations. The research addressing the pallet block-stacking problem suggests different rules or algorithms, usually with restrictive assumptions, e. g. , the replenishment quantities and retrieval frequencies for each SKU are known. In reality, not only do these change dynamically, but the SKU set itself changes, and pallet block-stacking patterns that are optimized for current conditions may be far from optimum in the near future.Research is needed that will identify a robust solution in the face of dynamic uncertainty in the storage and retrieval requirements. 2. 3. 2. Storage department layout (P2) The storage department layout problem is to determine the aisle structure of a storage department in order to minimize the construction cost and material handling cost. The decisions usually include aisle orientations, number of aisles, length and width of aisles, and door locations.In order to evaluate operational costs, some assumptions are usually made about the storage and order picking policies; random storage and single-command order picking are the most common assumptions. By assuming a layout con? guration, or a small set of alternative con? gurations, models can be formulated to optimize each con? guration. Roberts and Reed (1972) assume storage space is available in units of identical bays. Bassan et al. (1980) consider a rectangular warehouse, and aisles that are either parallel or perpendicular to the longest walls.In addition, they also discuss the optimal door locations in the storage department, and the optimal layout when the storage area is divided into different zones. Roll and Rosenblatt (1983) extend Bassan et al. (1980) to include the additional cost due to the use of grouped storage policy. Pandit and Palekar (1993) minimize the expected response time of storage and/or retrieval requests using a queuing model to calculate the total response time including waiting and processing time for different types of layouts. With these response times, an optimization model is solved to ? nd the optimal storage space con? urations. Roodbergen and Vis (2006) present an optimization approach for selecting the number and length of aisles and the depot location so as to minimize the expected length of a picking tour. They developed models for both S-shaped tours and a largest gap policy, and concluded that the choice of routing policy could, in some cases, have a signi? cant impact on the size and layout of the department . The conclusion from Roodbergen and Vis (2006) is quite significant, since it calls into question the attempt to optimize storage department layout without knowing what the true material handling performance will be.There is a need for additional research that helps to identify the magnitude of the impact of layout (for reasonably shaped departments) on total costs over the life of the warehouse, considering changing storage and retrieval requirements. J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 543 2. 3. 3. AS/RS con? guration (P3) The AS/RS con? guration problem is to determine the numbers of cranes and aisles, and storage rack dimension in order to minimize construction, maintenance, and operational cost, and/or maximize equipment utilization.The optimal design models or rule-ofthumb procedures summarized in Table 1 typically utilize some empirical expressions of the costs based on simple assumptions for the operational policies, and known s torage and retrieval rates. Karasawa et al. (1980) present a nonlinear mixed integer formulation with decision variables being the number of cranes and the height and length of storage racks and costs including construction and equipment costs while satisfying service and storage capacity requirements. Ashayeri et al. 1985) solve a problem similar to Karasawa et al. (1980). Given the storage capacity requirement and the height of racks, their models can be simpli? ed to include only a single design variable, i. e. , the number of aisles. Furthermore, the objective function is shown to be convex in the number of aisles, which allows a simple one-dimensional search algorithm to optimally solve the problem. Rosenblatt et al. (1993) propose an optimization model that is a slight modi? cation of Ashayeri et al. (1985), which allows a crane to serve multiple aisles.A combined optimization and simulation approach is proposed, where the optimization model generates an initial design, and a simulation evaluates performance, e. g. , service level. If the constraints evaluated by simulation are satis? ed, then the procedure stops. Otherwise, the optimization model is altered by adding new constraints that have been constructed by approximating the simulation results. Zollinger (1996) proposes some rule of thumb heuristics for designing an AS/RS. The design criteria include the total equipment costs, S/ R machine utilization, service time, number of jobs waiting in the queue, and storage space requirements.Closed form equations compute these criteria as functions of the number of aisles and the number of levels in the storage rack. Malmborg (2001) uses simulation to re? ne the estimates of some of the parameters which then are used in the closed form equations. The design of automated carousel storage systems is addressed by Lee and Hwang (1988). They use an optimization approach to determine the optimal number of S/R machines and the optimal dimensions of the carousel sy stem to minimize the initial investment cost and operational costs over a ? ite planning horizon subject to constraints for throughput, storage capacity, and site restrictions. Some other less well-discussed AS/RS design problems include determining the size of the basic material handling unit and the con? guration of I/O points. Roll et al. (1989) propose a procedure to determine the single optimal container size in an AS/RS, which is the basic unit for storage and order picking. Container size has a direct effect on space utilization, and therefore on the equipment cost since the storage capacity requirement needs to be satis? ed. Randhawa et al. 1991) and Randhawa and Shroff (1995) use simulations to investigate different I/O con? gurations on performance such as throughput, mean waiting time, and maximum waiting time. The results indicate that increased system throughput can be achieved using I/O con? gurations different from the common one-dock layout where the dock is located at the end of the aisle. There are two important opportunities for additional research on AS/RS con? guration: (1) results for a much broader range of technology options, e. g. , double deep rack, multi-shuttle cranes, etc. ; and (2) results demonstrating the sensitivity of con? urations to changes in the expected storage and retrieval rates or the effects of a changing product mix. 2. 4. Equipment selection The equipment selection problem addresses the level of automation in a warehouse and what type of storage and material han- dling systems should be employed. These decisions obviously are strategic in nature in that they affect almost all the other decisions as well as the overall warehouse investment and performance. Determining the best level of automation is far from obvious in most cases, and in practice it is usually determined based on the personal experience of designers and managers.Academic research in this category is extremely rare. Cox (1986) provides a methodology t o evaluate different levels of automation based on a cost-productivity analysis technique called the hierarchy of productivity ratios. White et al. (1981) develop analytical models to compare block stacking, single-deep and doubledeep pallet rack, deep lane storage, and unit load AS/RS in order to determine the minimum space design. Matson and White (1981) extend White et al. (1981) to develop a total cost model incorporating both space and material handling costs, and demonstrate the effect of handling requirements on the optimum storage design.Sharp et al. (1994) compare several competing small part storage equipment types assuming different product sizes and dimensions. They considered shelving systems, modular drawers, gravity ? ow racks, carousel systems, and mini-load storage/retrieval systems. The costs they considered include operational costs, ? oor space costs, and equipment costs. In summary, research on equipment selection is quite limited and preliminary, although it is very important in the sense that it will affect the whole warehouse design and the overall lifetime costs.There are two fundamental issues for equipment selection: (1) how to identify the equipment alternatives that are reasonable for a given storage/retrieval requirement; and (2) how to select among the reasonable alternatives. A very signi? cant contribution would be to develop a method for characterizing requirements and characterizing equipment in such a way that these two issues could be addressed in a uni? ed manner. 2. 5. Operation strategy This section discusses the selection of operation strategies in a warehouse.The focus is on operation strategies that, once selected, have important effects on the overall system and are not likely to be changed frequently. Examples of such strategies are the decision between randomized and dedicated storage, or the decision to use zone picking. Two major operation strategies are discussed: the storage strategy and the order picking strat egy. Detailed operation policies and their implementations are discussed in Gu et al. (2007). 2. 5. 1. Storage The basic storage strategies include random storage, dedicated storage, class-based storage, and Duration-of-Stay (DOS) based storage, as explained in Gu et al. 2007). Hausman et al. (1976), Graves et al. (1977) and Schwarz et al. (1978) compare random storage, dedicated storage, and class-based storage in single-command and dual-command AS/RS using both analytical models and simulations. They show that signi? cant reductions in travel time are obtainable from dedicated storage compared with random storage, and also that class-based storage with relatively few classes yields travel time reductions that are close to those obtained by dedicated storage.Goetschalckx and Ratliff (1990) and Thonemann and Brandeau (1998) show theoretically that DOS-based storage policies are the most promising in terms of minimizing traveling costs. Historically, DOS-based policies were dif? cult to implement since they require the tracking and management of each stored unit in the warehouse, but modern WMS’s have this capability. Also the performance of DOS-based policies depends greatly on factors such as the skewness of demands, balance of input and output ? ows, inventory control policies, and the speci? cs of implementation. In a study by Kulturel et al. (1999), class-based 544 J. Gu et al. European Journal of Operational Research 203 (2010) 539–549 storage and DOS-based storage are compared using simulations, and the former is found to consistently outperform the latter. This conclusion may have been reached because the assumptions of the DOS model rarely hold true in practice. All the results on operational strategies are for unit-load AS/RS. Studies on other storage systems are rarely reported. Malmborg and Al-Tassan (1998) develop analytic models to evaluate the performance of dedicated storage and randomized storage in lessthan-unit-load warehouses, but no general conclusions comparable to the unit-load case are given.A strong case can be made that additional research is needed, especially to clarify the conditions under which the storage policy does or does not have a signi? cant impact on capacity or travel time. 2. 5. 2. Order picking In a given day or shift, a warehouse may have many orders to pick. These orders may be similar in a number of respects; for example, some orders are shipped using the same carrier, or transportation mode, or have the same pick due date and time.If there are similarities among subsets of orders that require them to be shipped together, then they also should be picked roughly during the same time period to avoid intermediate storage and staging. Thus, it is common practice to use wave picking, i. e. , to release a fraction of the day’s (shift’s) orders, and to expect their picking to be completed within a corresponding fraction of the day (shift). In addition to wave picking, two ot her commonly used orderpicking strategies are batch picking and zone picking.Batch picking involves the assignment of a group of orders to a picker to be picked simultaneously in one trip. In zone picking, the storage space is divided into picking zones and each zone has one or more assigned pickers who only pick in their assigned zone. Zone picking can be divided into sequential and parallel zone picking. Sequential zone picking is similar to a ? ow line, in which containers that can hold one or more orders are passed sequentially through the zones; the pickers in each zone pick the products within their zone, put them into the container, and then pass the container to the next zone. Bartholdi et al. (2000) propose a Bucket Brigades order picking method that is similar to sequential zone picking, but does not require pickers to be restricted to zones). In parallel zone picking, an order is picked in each zone simultaneously. The picked items are sent to a downstream sorting system to be combined into orders. The organization and planning of the order picking process has to answer the following questions: 1. Will product be transported to the picker (part-to-picker) or will the picker travel to the storage location (picker-to-part)? . Will orders be picked in waves? If so, how many waves of what duration? 3. Will the warehouse be divided into zones? If so, will zones be picked sequentially or concurrently? 4. Will orders be picked in batches or separately? If they are batched, will they be sorted while picking or after picking? Depending on the operating principles selected, the order picking methods will be:        Single order picking. Batching with sort-while-pick. Batching with sort-after-pick. Sequential zoning with single order picking. Sequential zoning with batching.Concurrent zoning without batching. Concurrent zoning with batching. Research on the selection of an order picking strategy is very scarce, which might be a result of the complexity of the problem itself. Lin and Lu (1999) compare single-order picking and batch zone picking for different types of orders, which are classi? ed based on the order quantity and the number of ordered items. Petersen (2000) simulates ? ve different order-picking policies: singleorder picking, batch picking, sequential zone picking, concurrent zone picking, and wave picking.Two control variables in the simulation study are the numbers of daily orders and the demand skewness, while the other factors such as warehouse layout, storage assignment, and zone con? guration (when zone and wave picking are used) are ? xed. The performance measures used to compare the different policies include: the mean daily labor, the mean length of day, and the mean percentage of late orders. For each order picking policy, the simplest rules regarding batching, routing, and wave length are used. It also should be noted that the performance measures are mainly related to order picking ef? iencies and service quality; additional costs caused by downstream sorting with batch, zone, and wave picking are not considered. Furthermore, comparison of these policies are made mainly with regards to the order structures, while other important factors such as storage assignment and detailed implementations of the order picking policies are assumed to be ? xed. Therefore, the results should not be considered generic and more research in this direction is required to provide more guidance for warehouse designers. Order picking strategy selection remains a largely unresolved design problem.Additional research would be valuable, especially if it could begin to characterize order picking alternatives in ways that were easy to apply in design decision making. As an example, could researchers develop performance curves for different order picking strategies? 3. Performance evaluation Performance evaluation provides feedback on the quality of a proposed design and/or operational policy, and more importantl y, on how to improve it. There are different approaches for performance evaluation: benchmarking, analytic models, and simulations. This section will only discuss benchmarking and analytic models. 3. 1.Benchmarking Warehouse benchmarking is the process of systematically assessing the performance of a warehouse, identifying inef? ciencies, and proposing improvements. Data Envelopment Analysis (DEA) is regarded as an appropriate tool for this task because of its capability to capture simultaneously all the relevant inputs (resources) and outputs (performances), to construct the best performance frontier, and to reveals the relative shortcomings of inef? cient warehouses. Schefczyk (1993), Hackman et al. (2001), and Ross and Droge (2002) shows some approaches and case studies of using DEA in warehouse benchmarking.An Internet-based DEA system (iDEAS) for warehouses is developed by the Keck Lab at Georgia Tech, which includes information on more than 200 warehouses (McGinnis, 2003). 3. 2. Analytical models Analytic performance models fall into two main categories: (1) aisle based models which focus on a single storage system and address travel or service time; and (2) integrated models which address either multiple storage systems or criteria in addition to travel/service times. J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 545 3. 2. 1.Aisle based models Table 2 summarizes research on travel time models for aislebased systems. A signi? cant fraction of research focuses on the expected travel time for the crane in an AS/RS, for either single command (SC) or dual command (DC) cycles. For both, there is research addressing three different storage policies: in randomized storage, any SKU can occupy any location; in dedicated storage, each SKU has a set of designated locations; and in class based storage, a group of storage locations is allocated to a class of SKUs, and randomized storage is allowed within the group of storage locati ons.The issue with DC cycles is matching up storages and retrievals to minimize the dead-head travel of the crane, which may involve sequencing retrievals, and selecting storage locations. The results in this category usually assume in? nite acceleration to simplify the travel time models, although some develop more elaborate models by considering acceleration for the various axes of motion (see, e. g. , Hwang and Lee, 1990; Hwang et al. , 2004b; Chang and Wen, 1997; Chang et al. , 1995).There are a few papers that attack the more mathematically challenging issue of deriving the distribution of travel time (see Foley and Frazelle (1991) and Foley et al. (2002)). The research on carousel travel time models generally parallels corresponding AS/RS research. Given some knowledge of travel time, AS/RS service time models can be developed, considering the times required for load/unload and store/retrieve at the storage slot. Queuing models have been developed assuming various distribution s for travel time, see e. g. Lee (1997), Chow (1986), Hur et al. (2004), Bozer and White (1984), Park et al. (2003a) for AS/RS, Chang et al. (1995) for conventional multi-aisle systems, and for end-of-aisle picking systems, see Bozer and White (1991, 1996), Park et al. (2003a), and Park et al. (1999). Stochastic optimization models have been developed for estimating AS/RS throughput, with constraints on storage queue length and retrieval request waiting time (Azadivar, 1986). The throughput of carousel systems is modeled by Park et al. (2003b) and Meller and Klote (2004).The former consider a system with two carousels and one picker, and derive analytic expressions for the system throughput and picker utilization assuming deterministic and exponential pick time distributions. Meller and Klote (2004) develop throughput models for systems with multiple carousels using an approximate two-server queuing model approach. For conventional multi-aisle storage systems (bin shelving, e. g. ), two kinds of travel time results have been developed: (1) models which estimate the expected travel time; and (2) models of the pdf of travel times.These models require an assumption about the structure of the tour, e. g. , traversal (Hall, 1993), return (Hall, 1993 or Caron et al. , 1998), or largest gap (Roodbergen and Vis, 2006). As long as these models are parameterized on attributes of the storage system design, they can be used to support design by searching over the relevant parameters. As with AS/RS and carousels, there has been research to incorporate travel time models into performance models. Chew and Table 2 Literature of travel time models for different warehouse systems. Randomized storage Unit-load AS/RS Single-command Hausman et al. 1976) Bozer and White (1984) Thonemann and Brandeau (1998) Kim and Seidmann (1990) Hwang and Ko (1988) Lee (1997) Hwang and Lee (1990) Chang et al. (1995) Chang and Wen (1997) Koh et al. (2002) Lee et al. (1999) Graves et al. (1977) Boze r and White (1984) Kim and Seidmann (1990) Hwang and Ko (1988) Lee (1997) Han et al. (1987) Hwang and Lee (1990) Chang et al. (1995) Chang and Wen (1997) Koh et al. (2002) Lee et al. (1999) Meller and Mungwattana (1997) Potrc et al. (2004) Hwang and Song (1993) Bozer and White (1990) Bozer and White (1996) Foley and Frazelle (1991) Park et al. 1999) Han and McGinnis (1986) Han et al. (1988) Su (1998) Hwang and Ha (1991) Hwang et al. (1999) Hall (1993) Jarvis and McDowell (1991) Chew and Tang (1999) Hwang et al. (2004a) Caron et al. (1998) Caron et al. (2000) Jarvis and McDowell (1991) Chew and Tang (1999) Hwang et al. (2004a) Park et al. (2003a) Dedicated storage Hausman et al. (1976) Thonemann and Brandeau (1998) Kim and Seidmann (1990) Class-based storage Hausman et al. (1976) Thonemann and Brandeau (1998) Rosenblatt and Eynan (1989) Eynan and Rosenblatt (1994) Kouvelis and Papanicolaou (1995) Kim and Seidmann (1990) Pan and Wang (1996) Ashayeri et al. 2002) Dual-command Graves et al. (1977) Kim and Seidmann (1990) Graves et al. (1977) Kouvelis and Papanicolaou (1995) Kim and Seidmann (1990) Pan and Wang (1996) Ashayeri et al. (2002) Multi-shuttle Man-on-board AS/RS End-of-aisle AS/RS Carousel and rotary racks Ha and Hwang (1994) Conventional multi-aisle system Jarvis and McDowell (1991) Chew and Tang (1999) Hwang et al. (2004a) 546 J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 Tang (1999) use their model of the travel time pdf to analyze order batching and storage allocation using a queuing model.Bhaskaran and Malmborg (1989) present a stochastic performance evaluation model for the service process in multi-aisle warehouses with an approximated distribution for the service time that depends on the batch size and the travel distance. de Koster (1994) develops queuing models to evaluate the performance of a warehouse that uses sequential zone picking where each bin is assigned to one or more orders and is transported using a conveyer. If a bin needs to be picked in a speci? c zone, it is transported to the corresponding pick station.After it is picked, it is then put on the conveyor to be sent to the next pick station. The proposed queuing network model evaluates performance measures such as system throughput, picker utilization, and the average number of bins in the system based on factors such as the speed and length of the conveyor, the number of picking stations, and the number of picks per station. Throughput analysis of sorting systems is addressed in Johnson and Meller (2002). They assume that the induction process is the bottleneck of the sorting process, and therefore governs the throughput of the sorting system.This model is later incorporated into a more comprehensive model in Russell and Meller (2003) that integrates order picking and sorting to balance the tradeoffs between picking and packing with different order batch sizes and wave lengths. Russell and Meller (2003) also demonstrate th e use of the proposed model in determining whether or not to automate the sorting process and in designing the sorting system. 3. 2. 2. Integrated models Integrated models combine travel time analysis and the service quality criteria with other performance measures, e. g. storage capacity, construction cost, and operational cost. Malmborg (1996) proposes an integrated performance evaluation model for a warehouse having a forward-reserve con? guration. The proposed model uses information about inventory management, forward-reserve space allocation, and storage layout to evaluate costs associated with: storage capacity and space shortage; inventory carrying, replenishing, and expediting; and order picking and internal replenishment for the forward area. Malmborg (2000) evaluates several performance measures for a twin-shuttle AS/RS.Malmborg and Al-Tassan (2000) present a mathematical model to estimated space requirements and order picking cycle times for less than unit load order pick ing systems that uses randomized storage. The inputs of the model include product parameters, equipment speci? cations, operational policies, and storage area con? gurations. Malmborg (2003) models the dependency of performance measures such as expected total system construction cost and throughput on factors such as the vehicle ? eet size, the number of lifts, and the storage rack con? gurations for warehouse systems that use rail guided vehicles.Table 3 A Summary of the literature on warehouse case studies. Citation Cormier and Kersey (1995) Yoon and Sharp (1995) Zeng et al. (2002) Kallina and Lynn (1976) Brynzer and Johansson (1995) Burkard et al. (1995) van Oudheusden et al. (1988) Dekker et al. (2004) Luxhoj and Skarpness (1986) Johnson and Lofgren (1994) Problems studied Conceptual design Analytic travel time and performance models of storage systems represent a major contribution to warehouse design related research, and a rich set of models is available. Yet despite this wea lth of prior results, there is no uni? d approach to travel time modeling or performance modeling for aisle based systems – every system and every set of assumptions leads to a different model. A signi? cant research contribution would be to present a uni? ed theory of travel time in aisle-based systems. 4. Case studies There are some published industrial case studies, which not only provide applications of the various design and operation methods in practical contexts, but more importantly, also identify possible future research challenges from the industrial point of view. Table 3 lists these case studies, identifying the problems and the types of warehouse they investigated.It is dif? cult to generalize from such a small set of speci? c cases, but one conclusion is that substantial bene? ts can achieved by appropriately designing and operating a warehouse, see for example Zeng et al. (2002), van Oudheusden et al. (1988), and Dekker et al. (2004). On the other hand, one mig ht conclude from these cases that there are few generic simple rules. As just one example, the COI-based storage location assignment rule proposed by Kallina and Lynn (1976) ignores many practical considerations, such as varying weights, item-dependent travel costs, or dependencies between items.Some of these complications have been addressed in the academic research (for example see Table 3 in Section 5. 2 of Gu et al. (2007)), but many others remain unexplored. What these cases illustrate is the gap between the assumption-restricted models in research publications and the complex reality of most warehouses. There is a signi? cant need for more industrial case studies, which will assist the warehouse research community in better understanding the real issues in warehouse design. In turn, research results that have been tested on more realistic data sets will have a more substantial impact on practice.A warehouse design problem classi? cation, such as we have proposed here, might be used to structure such future case studies. 5. Computational systems There are numerous commercial Warehouse Management Systems (WMS) available in the market, which basically help the warehouse manager to keep track of the products, orders, space, equipment, and human resources in a warehouse, and provide rules/algorithms for storage location assignment, order batching, pick routing, etc. Detailed review of these systems is beyond the scope of this paper.Instead, we focus on the academic research addressing computational systems for warehouse design. As previous sections show, research on various warehouse design and Type of warehouse A warehouse for perishable goods that requires Just-In-Time operations An order picking system A distribution center A distribution center Kitting systems that supply materials to assembly lines An AS/RS where a S/R machine can serve any aisle using a switching gangway A man-on-board AS/RS in an integrated steel mill A multi-aisle manual order picking system A distribution center A distribution centerConceptual design Storage location assignment; warehouse dimensioning; storage and order picking policies Storage location assignment using the COI rule Process ? ow; batching; zone picking; Vehicle routing Storage location assignment; batching; routing Storage and routing policies Manpower planning Simulation by decomposition J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 547 operation problems has been conducted for almost half a century, and as a result, a large number of methodologies, algorithms, and empirical studies have been generated.However, successful implementations of these academic results in current commercial WMS systems or in engineering design software are rare. The prototype systems discussed in this section might shed some light on how academic research results could be utilized to develop more sophisticated computer aided warehouse design and operation systems. Perlmann and Bai ley (1988) present computer-aided design software that allows a warehouse designer to quickly generate a set of conceptual design alternatives including building shape, equipment selection, and operational policy selection, and to select from among them the best one based on the speci? d design requirements. To our knowledge, this is the only research paper addressing computer aided warehouse design. There are several papers on the design of warehouse control systems. Linn and Wysk (1990) develop an expert system for AS/ RS control. A control policy determines decisions such as storage location assignment, which item to retrieve if multi-items for the same product are stored, storage and retrieval sequencing, and storage relocation.Several control rules are available for each decision and the control policy is constructed by selecting one individual rule for each decision in a coherent way based on dynamically changing system state variables such as demand levels and traf? c intensi ty. A similar AS/RS control system is proposed by Wang and Yih (1997) based on neural networks. Ito et al. (2002) propose an intelligent agent based system to model a warehouse, which is composed of three subsystems, i. e. , agent-based communication system, agent-based material handling system, and agent-based inventory planning and control system.The proposed agent-based system is used for the design and implementation of warehouse simulation models. Kim et al. (2002) present an agent based system for the control of a warehouse for cosmetic products. In addition to providing the communication function, the agents also make decisions regarding the operation of the warehouse entities they represented in a dynamic real-time fashion. The absence of research prototypes for computer aided warehouse design is particularly puzzling, given the rapid advancement in computing hardware and software over the past decade.Academic researchers have been at the forefront of computer aided design i n other disciplines, and particularly in developing computational models to support design decision making. Warehousing design, as a research domain, would appear to be ripe for this kind of contribution. 6. Conclusions and discussion We have attempted a thorough examination of the published research related to warehouse design, and classi? ed papers based on the main issues addressed. Fig. 1 shows the numbers of papers in each category; there were 50 papers directly addressing warehouse design decisions.There were an additional 50 papers on various analytic models of travel time or performance for speci? c storage systems or aggregates of storage systems. Benchmarking, case studies and other surveys account for 18 more papers. One clear conclusion is that warehouse design related research has focused on analysis, primarily of storage systems rather than synthesis. While this is somewhat surprising, an even more surprising observation is that only 10% of papers directly addressing w arehouse design decisions have a publication date of 2000 or later.Given the rapid development of computing hardware and solvers for optimization, simulation, and general mathematical problems, one might reasonably expect a more robust design-centric research literature. We conjecture two primary inhibiting factors: 1. The warehouse design decisions identi? ed in Fig. 1 are tightly coupled, and one cannot be analyzed or determined in isolation from the others. Yet, the models available are not uni? ed in any way and are not ‘‘interoperable†. A researcher addressing one decision would require a research infrastructure integrating all the other decisions.The scope and scale of this infrastructure appears too great a challenge for individual researchers. 2. To properly evaluate the impact of changing one of the design decisions requires estimating changes in the operation of the warehouse. Not only are future operating scenarios not speci? ed in detail, even if they w ere, the total warehouse performance assessment models, such as high ? delity simulations, are themselves a considerable development challenge. From this, we conclude that the most important future direction for the warehouse design research community is to ? d ways to overcome these two hurdles. Key to that, we believe, will be the emergence of standard representations of warehouse elements, and perhaps some research community based tools, such as open-source analysis and design models. Other avenues for important contributions include studies describing validated or applied design models, and practical case studies that demonstrate the potential bene? ts of applying academic research results to real problems, or in identifying the hidden challenges that prevent their successful implementation.Finally, both analytic and simulation models are proposed to solve warehouse problems and each has its respective advantages and disadvantages. Analytic models are usually design-oriented in the sense that they can explore many alternatives quickly to ? nd solutions, although they may not capture all the relevant details of the system. On the other hand, simulation models are usually analysis-oriented – they provide an assessment of a given design, but usually have limited capability for exploring the design space. There is an important need to integrate both approaches to achieve more ? exibility in analyzing warehouse problems.This is also pointed out by Ashayeri and Gelders (1985), and its applicability has been demonstrated by Rosenblatt and Roll (1984) and Rosenblatt et al. (1993). There is an enormous gap between the published warehouse research and the practice of warehouse design and operations. Cross fertilization between the groups of practitioners and researchers appears to be very limited. Effectively bridging this gap would improve the state-of-the-art in warehouse design methodology. Until such communication is established, the prospect of meaningfu l expansion and enhancement of warehouse design methodology appears limited.Warehousing is an essential component in any supply chain. In the USA, the value of wholesale trade inventories is approximately half a trillion dollars (BEA, 2008), and 2004 inventory carrying costs (interest, taxes, depreciation, insurance, obsolescence and warehousing) have been estimated at 332 billion dollars (Trunick, 2005). To date the research effort focusing on warehousing is a very small fraction of the overall supply chain research. There are many challenging research questions and problems that have not received any attention.The challenge for the academic research community is to focus on the integrated design and operation of warehouses, while the challenge for industrial practitioners is to provide realistic test cases. References Ashayeri, J. , Gelders, L. F. , 1985. Warehouse design optimization. European Journal of Operational Research 21, 285–294. 548 J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 Goh, M. , Ou, J. , Teo, C. -P. , 2001. Warehouse sizing to minimize inventory and storage costs. 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Hur, S. , Lee, Y. H. , Lim, S. Y. , Lee, M. H. , 2004. A performance estimating model for AS/ RS by M/G/1 queuing system. Computers and Industrial Engineering 46, 233– 241. Hwang, H. , Ha, J. -W. , 1991. Cycle time models for single/double carousel system. International Journal of Production Economics 25, 129–140. Hwang, H. , Ko, C. S. , 1988. A study on multi-aisle system served by a single storage/ retrieval machine. International Journal of Production Research 26 (11), 1727– 1737.Hwang, H. , Lee, S. B. , 1990. Travel-time models considering the operating characteristics of the storage and retrieval machine. International Journal of Production Research 28 (10), 1779–1789. Hwang, H. , Song, J. Y. , 1993. Sequencing picking op erations and travel time models for man-on-board storage and retrieval warehousing system. International Journal of Production Economics 29, 75–88. Hwang, H. , Kim, C. -S. , Ko, K. -H. , 1999. Performance analysis of carousel systems with double shuttle. Computers and Industrial Engineering 36, 473–485. Hwang, H. , Oh, Y. H. , Lee, Y. K. , 2004a.An evaluation of routing policies for orderpicking operations in low-level picker-to-part system. International Journal of Production Research 42 (18), 3873–3889. Hwang, H. , Song, Y. -K. , Kim, K. -H. , 2004b. The impacts of acceleration/deceleration on travel time models for carousel systems. Computers and Industrial Engineering 46, 253–265. Ito, T. , Abadi, J. , Mousavi, S. M. , 2002. Agent-based material handling and inventory planning in warehouse. Journal of Intelligent Manufacturing 13 (3), 201–210. Jarvis, J. M. , McDowell, E. D. , 1991. Optimal product layout in an order picking warehouse.IIE Trans actions 23 (1), 93–102. Johnson, M. E. , Lofgren, T. , 1994. Model decomposition speeds distribution center design. Interfaces 24 (5), 95–106. Johnson, M. E. , Meller, R. D. , 2002. Performance analysis of split-case sorting systems. 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Determination of Factors Contributing to Success in Strategic Alliances - Free Essay Example

Sample details Pages: 26 Words: 7847 Downloads: 8 Date added: 2017/09/16 Category Business Essay Type Argumentative essay Did you like this example? DETERMINATION OF FACTORS CONTRIBUTING TO SUCCESS IN STRATEGIC ALLIANCES By CLAIRE REVELL University of Groningen Faculty of Economics and Business Bsc International Business and Management June 2010 Version 3 Bachelor Thesis Supervisor: C. Quispel Group:3 Sint Lucasstraat 8 9718 LR Groningen (06) 47820628 c. a. [emailprotected] rug. nl student number: 1538276 DETERMINATION OF FACTORS CONTRIBUTING TO SUCCESS IN STRATEGIC ALLIANCES Author: Claire Revell (Rijksuniversity Groningen 2005 student) Abstract In this research study the emphasis is drawn toward determining the factors contributing to success of strategic alliances. These factors will be uncovered by analyzing the internal and external factors influencing strategic alliances and the phases through which these alliances evolve. In order to provide this research study with a practical element two case studies within the airline industry have been incorporated, namely the Swissair Qualiflyer Alliance and Star Alliance. These case studies represent a successful and an unsuccessful alliance, which are analyzed on a basis of the provided literature study, in this case the phases through which these alliances evolved and the internal and external factors affecting the alliances. After evaluation of the case studies numerous supportive results were identified, contributing toward establishing determinant factors, which emphasize the importance of a successful implementation of the different phases, however limitations affect the reliability of this study, due to the lack of evidence found in various different phases. Keywords: strategic alliances, internal and external factors, strategic alliance phases Introduction In past years a visible increase in the amount of strategic alliances, concerning firms with varying economic objectives, was observed (Das, Teng 2000). Strategic alliances are the relatively enduring inter-firm cooperative arrangements, involving flows and linkages that utilize resou rces and/or governance structures from autonomous organizations, for the joint accomplishment of individual goals linked to the corporate mission of each sponsoring firm (Parkhe 1991, p. 2). The amount of strategic alliances has recently doubled, predicting additional raise in the future (Booz, Allen, Hamilton 1997). Especially alliances in the form of non-equity based, which are defined as two or more firms developing a contractual-relationship in order to establish competitive advantage by combining resources and capabilities (Globerman 2007), have increased in importance which is visible in non equity alliances accounting for 80 per cent (Hagedoorn 1996). Strategic alliances provide firms with the opportunity to recognize synergies through combining operations, such as in research and development, manufacturing etc (Aaker 1995; Addler 1966). The growth of strategic alliances is related to growing competition and globalization (Das, Teng 2000). This is in alignment with Doz and Hamels (1998) view which states that globalization as well as changes in economic activities is a consequence for the growth in strategic alliances, which is visible in various different industries (Hagedoorn 1993). The primary reasons for the growth of the number of alliances is 1) the ability of cost savings in executing operations 2) the ability to access particular markets 3) the reducing of financial and political risk in addition to cheapest labor and production costs (Wheelen, Hungar 2000). A strategic alliance by definition is a hybrid organizational form which Jensen and Meckling (1991) refer to as a network organization. Harbison and Pekar (1998) highlight numerous common characteristics visible within strategic alliances, namely a required commitment of at least ten years, the connection of the partners is based on equity or on shared capabilities, a complementary relationship based on a shared strategy, increasing companies’ value in the market place, the p ressuring of competitors and the willingness of sharing and leveraging core capabilities. Nevertheless, strategic alliances have noticeable high instability rates (Das, Teng 2000); furthermore, according to Kalmbach and Roussel (1999) the failure rates are approximately as high as 70 per cent. Studies conducted by Das and Teng (2000) reportedly state that encountered problems are witnessed in the first two years of two thirds of all alliances. This study is going to provide a more in-depth analysis on the factors that are necessary for determining success in all strategic alliances. Starting with an analysis of strategic alliances based on the different phases in which the alliance evolves. Thereafter a conclusion will be drawn as to which extent these factors play a crucial role in the determination of success rate of strategic alliances. In order to incorporate a practical view on the strategic alliances, this study will additionally implement two case studies to the analysi s. Conceptual Model [pic] This conceptual model starts the literature study on strategic alliances as a central concept. From this central concept, emphasis is drawn on internal and external factors influencing strategic alliances, as well as on the different phases through which alliance evolve. Additionally, strategic alliances lead to either successful or unsuccessful alliances. Based on the research from Bronder and Pritzl (1992), Hoffmann and Schlosser (2001), Waddock (1989) and Wolhstetter, Smith and Malloy (2005), a framework of seven phases is established. Within these seven phases the most important activities and processes are analyzed, including reasoning behind strategic alliances, potential intensions for forming strategic alliances, partner selection, external factors influencing the design of the strategic alliance, negotiation methods, followed by the structuring of the alliance. Furthermore, implementation and management of the strategic alliance is examined. Finally, the last two phases concerning the evaluation of the formation of strategic alliances and the termination of the partnership are discussed. Resulting from this literature study are two outcomes, namely a successful implementation of the phases and an unsuccessful implementation. In order to apply a practical element to this thesis, two case studies will be analyzed, those of Qualiflyer, which turned out to be an unsuccessful alliance and Star Alliance, which was able to incorporate a success strategic alliance in the airline industry. After analyzing the cases the findings compared to the literature analysis, will hopefully correlate to each other and the determinants that influence more success in alliances can be established. Problem Statement Based on past literature research study’s the outcomes of implementing strategic alliances as a change strategy in organizations is unfavorable, especially when looking at the failure rates. Nevertheless, the adoption of s trategic alliances is a customary implemented firm strategy (Gulati 1998), as a means of securing their competitive position. Much research is conducted in order to provide more guidance in determining factors that achieve sustainable strategic alliances, therefore in this thesis the main research question is; What factors determine the success of strategic alliances? This research question will be addressed by the help of analyzing and answering these various sub-questions; Why do firms choose strategic alliances as a change process? What are the potential intentions of a strategic alliance? What are the phases through which strategic alliances evolve? What strategic alliance activities and processes occur in which phase? Preview of the organization of the thesis This report begins by indicating the problem that strategic alliances are a favorable organizational change strategy in the business world today, however the failure rate is extremely high. Secondly, by applying lite rature analysis the main determinants influencing more success in strategic alliances will be uncovered, which will be coupled to the case study part of the thesis where the determinants will be compared to the specific cases. Finally, the thesis will conclude on the part if the determinants uncovered in the literature study correlate to the findings in the case study. Methodology and Research Design In this thesis the methodology contained two specific approaches, including a literature study as well as evaluating two case studies. Firstly, the literature analysis was conducted; with as primary focus an in-depth analysis of academic articles. The findings of the literature study are compared to two case studies, those of the Qualiflyer alliance and Star Alliance. These two cases were chosen because they represent the different outcomes an alliance can hold, namely the successful implementation the alliance strategy at Star Alliance and the unsuccessful outcome of an alliance strategy of the Qualiflyer alliance. In addition, even though these two examples vary substantially in size, which provides difficulty when comparing the two alliances, they both started off at reasonably the same size; therefore this thesis incorporated these two examples anyway. This evaluation will be conducted by means of desk research, exploring the different implementations of this strategy. The time frame of the case studies is from the first phase up until the last phase, through which they evolved, in order to identify dependent unsuccessful and successful aspects. The significance of implementing case studies in this thesis is relating the findings from the literature analysis to real life cases of both a successful alliances as well as a non-successful alliance. Furthermore, comparing if the determinants of success found in the literature analysis correlate with the factors observed in the cases. Internal versus External factors Influencing Strategic Alliances Our i nternal tensions perspective framework (Figure 2, Appendices) of strategic alliances comprises three pairs of competing forces-namely, cooperation versus competition, rigidity versus flexibility, and short-term versus long-term orientations (Das, Teng 2000). Competition is defined as pursuing ones own interest at the expense of others, while cooperation is the pursuit of mutual interests and common benefits in alliances. This tension of cooperation versus competition is most salient in selecting alliance partners, the first of three major stages in the alliance making process, along with structuring and managing an alliance (Das, Teng 1997). In conclusion, the stability and success of strategic alliances will be inversely related to the difference between the cooperation level and the competition level. Rigidity refers to the characteristics of mutual dependence and connectedness, whereas flexibility enhances the ability of partners to adapt, unencumbered by rigid arrangements. The dominance of either flexibility or rigidity may change the status quo and trigger the evolution of a new structure, which leads to unsuccessful alliances. Therefore, the stability of strategic alliances will be inversely related to the difference between the rigidity level and the flexibility level. Short-term orientation views strategic alliances as transitional in nature, with a demand for quick and tangible results, whereas long-term orientation regards alliances as at least semi permanent entities, so that more patience and commitment are exercised. A strategy that reflects only one temporal orientation is not compatible with the foundation for a sustainable strategic alliance, in other words the stability of strategic alliances will be inversely related to the difference between the short-term orientation and the long-term orientation. Furthermore, the three internal pairs of contradictory forces are interrelated within an evolving system, resulting in the following pr opositions, namely that the levels of rigidity and cooperation will be positively related when the partners have a short-term orientation in strategic alliances. However, a negative relatedness at a high level of rigidity, cooperation and rigidity (Das, Teng 2000). will be negatively related when the partners have a long-term orientation in strategic alliances (Das, Teng 2000). According to Das and Teng (2000) the contradictions and tensions in these force-pairs may lead to an overthrow of the status quo namely, the strategic alliance. Strategic alliances can nevertheless be sustained and successful if a careful balance between these competing forces can be maintained. According to Todeva and Knoke (2005) external factors influence alliance formation, due to differing economic condition and organizational frameworks in partnering countries; these can include legal requirements, price controls, distribution channels and contract enforcement. Furthermore, these regulative state activities comprehend the freedom when firms are forming alliances. Moreover, the formation of an alliance necessitates the authorization of national governments. Additionally, of influence to the formation of alliances is the complicated collection of relations visible with firms, such as business associations, local governments and elite universities. On an industrial note alliances are influenced on an interfirm basis by direct impacts, where the decision on which activities to internalize is based on severity of competition within the industry and the organization of ad hoc product markets, in the challenge for increased market share, the cooperation for specific advantages and the process of internationalization (Todeva, Knoke 2005) . The partner under consideration for the formation of an alliance is in a certain sense an external factor. Firms are susceptible in the case of partnering with a dominant firm (Pennings 1994), due to technical and economic rationales. Thus, technology is a specific part of the process to establishing organizational boundaries as well as intrinsic structures. Of importance to alliances is obtaining research and development advantages, which to certain extent differs across industries on terms of expenses and the sources provided by the government (Todeva, Knoke 2005). Every alliance design commences with negotiations, thereafter the phase of structuring the alliance in which various aspects are aligned, such as the objectives of both parties, organizational structures, functional operations and cultures (Ring, van de Ven 1994). Strategic Alliances Phases The distinguishing of phases through which strategic alliances evolve plays an essential role in the development toward successful alliances, which according to Bronder and Pritzl (1992) evolves through the three stages, which are categorized as strategic decision, configuration of strategic alliance and partner selection. Where Bronder and Pritzl terminate their research on the establishment of phases other researchers continue in identifying essential phases, for the reason that partner selection as final phase represents an incomplete evolution of strategic alliances. With regard to the research conducted by Hoffmann and Schlosser (2001), the identification of strategic alliance phases resulted in a five phase path through which strategic alliances evolve, namely strategic analysis and decision to cooperate, search for a partner, designing the partnership, implementation and management of the partnership and finally termination. When comparing both Bronder and Pritzls (1992) and Hoffman and Schlossers (2001) phases, a comparison is visible in the primary phases of strategic alliances, namely the strategic analysis and decision to cooperate (Hoffmann, Schlosser 2001) which corresponds with the strategic decision phase from Bronder and Pritzl (1992). Furthermore, the partner selection phase is visible in both frameworks on strategic alli ance phase. The main difference between the two studies is the more detailed approach from Hoffmann and Schlosser (2001) also distinguishing phases after the partner selection process. Finally, a study building on Waddock’s (1989) work, which suggests that strategic alliances progress through three phases, which are identified as initiation, establishment and maturity, Wohlstetter, Smith and Malloy (2005) consistently debated that the strategic alliances process is organized into three similar phases namely initiation, operations and evaluation. When comparing these views with the earlier stated reasoning on strategic alliance phases merely a figuration is enabled as to which phases from Bronder and Pritzl (1992) and Hoffmann and Schlosser (2001) are in comparison with Wohlstetters et al (2005) view and could be placed within their views, for example the partner selection phase distinguished the above stated views is probably placed within the initiation phase identifie d by Wohlstetter et al. (2005). In order to provide this thesis with an in-depth view on the phases through which strategic alliances evolve a combination of the three above stated views is implemented. Phase 1: Strategic Decision According to Bronder and Pritzl (1992) a clarification of the firms’ position is to be analyzed, refer ably because this is identified as the first direction toward alliance formation. Pumpin (1987), states that the evaluation of the actual situation of the firm is identified by exploring its mission, possible values and core competencies. Additionally, the firm identifies the reasoning behind incorporating an alliance strategy. According to Eisenhardt and Schoonhoven (1996), Harrigan (1985), Link and Bauer (1989), Pisano (1991) and Teece (1992) technological change faced by firms is related to the favorability toward flexible organizational forms like alliances. Additionally, Ciborra (1991) and Oster (1992) state that high-tech industries, in which learning and flexibility are key characteristics, will preferably choose alliances, whereas in the low-tech industries, with visibly less emphasis on learning and flexibility, firms favorably adopt a merger and acquisition strategy. The flexibility of strategic alliances is suitable as organizational structure due to the fast expiring of new knowledge and the lengthy learning time from partners (Eisenhardt, Schoonhoven, 1996; Hagedoorn 1993). Furthermore, these flexible organizational structures appear more effectively in uncertain environmental situations when adjusting to changes (Lawrence, Lorsch 1967; Pffeffer, Salancik 1978). In continuation of Powells (1996) view, Hagedoorn and Duysters (2002) predict that strategic alliance experience positively contribute to choosing alliances as instrument for obtaining external innovative capabilities. This view is aligned with that of Kogut et al. (1992) and Gulati (1993) who accentuate the relationship between actual allianc e formation and past alliances, however emphasize on a more social basis. Therefore, the formation of strategic alliances is dependent on both strategic as well as social factors. According to Eisenhardt and Schoonhoven (1996), an extension of the resource-based view provides a basis for examining the relationship through which alliances form by means of strategic and social resources. This research study contributed numerous outcomes on strategic alliances to existing literature, namely that increasingly challenging market conditions and jeopardous organizational strategies result into an increase of alliance formations as an organizational change process. Additionally, of importance to the rate of formation of alliances are managerial characteristics, visible when large, experienced teams were implemented through previous employers, the rates of alliances increased (Eisenhardt, Schoonhoven 1996). In conclusion of their research Eisenhardt and Schoonhoven (1996) state that in cases of either a vulnerable strategic situations or a strong social situation the likelihood of the formation of strategic alliances increase. Phase 2: Initiation Phase The initiation phase is characterized by informal structures and communication channels as the critical issue is the development and understanding of the purpose for strategic alliances (Waddock 1989). According to Hitt et al. 1997), the potential intentions to be realized behind entering into strategic alliances are categorized into three market types 1) namely markets characterized by slow cycle, which adopt strategic alliances for original intentions such as the gaining of access to restricted markets, establishing franchises in a new market and maintaining market stability 2) in markets characterized by a standard cycle amongst the intentions able to be achieved are the gaining of market power and access to complementary resources, overcoming trade barriers, gaining knowledge and learning about new business tec hniques 3) in the final market, the fast cycle, the achievable goals are the speeding up of the entry of new products and services in addition to new markets, maintaining the market leadership position, sharing the risky Research and Development expenses and overcoming uncertainty. Furthermore, several internal conditions drive the initiation phase including, a champion taking responsibility, complementary needs and assets, compatible goals and trust. According to Waddock (1989), the main responsibility of the champion is the guidance of the organization through the initiation phase, especially visible in the process of partner selection. Stated in the research by Wohlstetter, Smith and Malloy (2005) the existence of a champion in the initiation phase is essential for identifying needs in addition to the process of partner selection. Complementary needs and assets appear in various different forms, however is one of the main reasons for partnering (Oliver 1990; Robertson 1998). A dditionally, the main goal of partnering is achieving compatible goals among the partners, which might not have been achieved otherwise (Austin 2000; Das, Teng 1998; Kanter 1994; Oliver 1990; Robertson 1998; Spillett 1999). Finally, the initiation phase stands no chance without trust, which is mainly established through existing networks (Austin, 2000; Waddock 1989; Waide 1999), within these networks similar interests are the main characteristic. Phase 3: Partner Selection The purpose behind strategic alliance partnering is to initiate and prolong a long-term partnership, which enables more effective competition with others firms which are positioned outside the partnership (Jarillo 1988; Walker, Poppo 1991). The crucial decision toward the correct partner selection is the primary focus after pursuing this alliance strategy (Hitt, Tyler, Hardee, Park 1995). According to Koot (1988) the selecting of a partner is a complex process however crucial to the success of an alliance. In t he partner selection process perspectives of both resource-based and organizational learning provide an explanation as to why certain partners are selected (Barkema, Bell, Pennings 1996). In explanation, firms own certain resource endowments (Barney 1991) however, in order to obtain a competitive position in a specific market supplementary resources are necessary (Hitt, Nixon, Clifford, Coyne 1999), which is the main objective for engaging in strategic alliances. Hitt et al. (2000) argues that of importance to the partner selection process is the firms’ embeddedness in both emerging markets and developed markets. Furthermore, the access to necessary resources for leveraging as well as the obtaining of capabilities for learning are primary reasons for the selection of partners. Table 1 in the Appendices, state the concluding outcomes on the selection of partners by Hitt et al. (2000), which explains the fundamental elements of the process toward partner selection. Eisenh ardt and Schoonhoven (1996) and Dacin and Olivers’ (1997) view state that legitimacy enhancements are an additional intention for establishing alliances, therefore the partner selection process is focused on those providing strong intangible assets, for example strong reputations. According to Bronder and Pritzl (1992) critical to the partner selection process is the establishment of fundamental, strategic and cultural fit. This fundamental fit is achieved if a win-win situation for both parties is established and potential value is increased. The strategic fit is realized when the alliance involves partners with harmony of the business plans. Finally, the cultural fit is an essential success factor for partner selection, which is accomplished after acceptance of cultural differences among the partners. Phase 4: Designing the Partnership Niederkofler (1991) argues that the negotiation process must essentially interpret clearly understandable resources and interests of the partners involved, in order for the creation of strategic and organizational fit to be achieved, which will direct the partnership toward a concrete foundation. The achievement of this foundation is accomplished through open and detailed communication, circumventing hidden agendas of any sort. The consequence of this open communication translates into a coherent attitude of sincerity toward the different partners, which demands trust. In addition to strategic fit, the negotiation process also initiates a solid basis for the enforcement of an operational fit within the partnership, which can be viewed in Figure 1 of the Appendices. An important aspect of the negotiation process is the creation of flexibility, which is increased through contract provisions in addition to developing and prolonging of trust. The process of conquering complexity in operations embarks with the communication of the discovered complexity, followed by a tracking and solving of this difficulty, which resul ts in the avoidance of any operational unalignments. The flexibility within the partnering arrangement, in addition to trust, permits renegotiation processes within the partnership; however a coherent basis must be accomplished (Niederkofler 1991). The success of alliances is highly dependent on a competent and effective alignment, therefore of importance is the designing of the partnership, thus the structure implemented. This structure is in need of a fine constructed collection of strategy, procedures and management views, which can be viewed as the internal alignment (Miles, Snow 1994). In the process of obtaining internal alignment interests as well as environmental aspects must be balanced between the partners, enabling a profitable situation (Douma, Bilderbeek, Idenburg, Looise 2000). Additionally, their framework, Figure 3, Appendices, stress the fact that the five features must sufficiently be aligned to prevent failure. One of the features, namely strategic fit, is e stablished when expected advantages and possible risks are weighed against that of the individual interests in the alliance. Various driver of strategic fit can be identified, starting with a shared vision. Further conditions necessary for strategic fit are compatibility of strategies (Brouthers, Brouthers, Wilkinson 1993), strategic importance (Doz 1988), acceptance into the market and mutual dependency. In addition to strategic fit, organizational fit is a necessity, however due to the differences in many aspects, such as market position, organizational structure and views, management style, this is a complex task. By clarifying these differences an understanding between partners is achieved. Numerous drivers toward organizational fit are identified, namely as stated above the addressing of organizational differences (Doz 1988) furthermore, essential drivers are facilitating strategic and organizational flexibility, minimal complexity to enhance manageability (Killing 1988), ef ficient management control, enhancing long-term stability by investigating possible strategic conflicts and finally, the achievement of the strategic objective. Of influence, however to lesser extent are the three remaining features in the framework, which are human, operational and cultural fit. Human fit is particularly of importance in alliances processes (Boersma 1999) and according to Lewis (1990) the cultural fit is specifically an issue among employers and employees, which translates to their functioning in for example boardrooms. Finally, operational fit, also relates to the functioning of the alliance and is often susceptible to various contingencies, therefore must be aligned. Research and Development activities have gradually evolved since the 1980s (Peterson, 1991). Creamer (1976) and Pearce (1989) identified three primary types of Research and Development activities, namely basic research, applied research, and development activities. For basic research the purpos e is an understanding of the inherent and fundamental scientific development, however disregarding commercial applications. Furthermore, applied research employs knowledge conceived from the basic research to certain dimensions such as technical problems or related commercial technology aspects. In conclusion, basic research generates new facts and theories which are thereafter proven through applied research. These proven facts are generated into products and processes in the development stadium. The intention of development activities is the configuration of applied research contributions into commercially feasible products, processes and technologies (Jansen 1995; Jones, Davis 2000). Phase 5: Implementation and Management of the Partnership The role of the management of strategic alliances is valuable for the progression of the alliance toward a successful outcome, however it is complex to manage (Koza, Lewin 2000). An important aspect in serving this complexity is the acquiri ng of knowledge from past engaging in alliances, which provides meaningful know-how to be leveraged (Kale, Dyer, Singh 2001). The framework of the four C’s of learning and leveraging alliance know how provides a tool for obtaining valuable knowledge. The four components in the framework are, capture, codify, communicate and create, and coach (Kale, Dyer, Singh 2001), also visible in Figure 4, Appendices. Capture refers to managements’ role of accessing and obtaining of valuable alliance insights and past experiences. To codify past experiences and practices contributes to the accomplishing of alliance specific needs. In order to have a common thread through the organization on these past knowledge practices, communication is essential in sharing experiences. Additionally, the creation of networks within the alliance facilitates the distribution of these valuable experiences and knowledge. Intrinsically executed coaching and education programs increase the ability to obtain alliance skills. An additional benefit from coaching is the establishment of informal social networks, which provides assistance in key situations. Furthermore, networks are critical to the development of opportunities, the assessing of concepts and obtaining resources in order to construct the new partnership (Aldrich, Zimmer 1986). The incorporation of social networks within a firm improves communication between partners, which in turn results in improved decision making processes (Gulati 1993). Various intentions for the implementation of networks can be identified, one specific is the preserving of advantages (Lorenzoni, Baden- Fuller 1995). According to Madhaven, Koka and Prescott (1998) the initiation of inter-organizational networks is created by exogenous factors, which could include competition background and specific industrial activities. Building on this theory, Gulati et al. 1997) argues that the initiation of these inter-organizational networks is dependent on two aspects, namely exogenous resource dependencies, which achieve motivation of the cooperation and an â€Å"endogenous embeddedness† dynamic, which in turn familiarizes toward partner selection. According to Stinchcombe (1990), in flows of network information meaningful views are discovered, which in turn influences alliances. Of importance is the understanding of those valuable insights influencing new alliance formation because they have numerous valuable implications, namely providing in-depth views on path dependent processes and enhancing informational capabilities (Gulati et al. 1997). Although successful management of alliances depends on a great number of factors (Das, Teng 1999), we submit that the tension in short-term versus long-term orientation is a critical one. A long-term orientation provides needed commitment to a good working relationship, whereas a short-term orientation stresses prompt results that vitalize the alliance. . Phase 6: Evaluation Phase Finally, the evaluation phase, in which all the impacts of the alliance are evaluated, and the main activities are the evaluation of the goals against the outcomes of the alliance (Waddock 1989). The outcomes of the evaluation are either identifying areas of improvement (Smith, Wohlstetter 2001) or â€Å"death† (Waddock 1989). The areas of improvement are identified by feedback loop, which implies returning back to either the initiation phase or the operations phase (Smith, Wohlstetter 2001; Waddock 1989). Evaluating the performance of strategic alliances is rooted in the overall issue of organizational effectiveness (Olk 2002). With regard to the studies on effectiveness of the organization, Olk (2002) builds on two dimensions in order to create a framework which distinguishes numerous approaches to the evaluation of performance. Firstly, a dimension is implemented with a view on which perspective is taken when evaluating performance, which is of importance due to the pr esence of numerous shareholders, in this case a distinction is made between the alliance and the partners as perspective. Secondly, a dimension on the purpose of the evaluation is implemented into the framework, which in Olks (2002) case are the following four approaches; optimization, strategic intent, multi-interest and sequential. These two dimensions, with their identification of numerous approaches to the evaluation of performance are displayed in Table 2, Appendices. Phase 7: Termination of Partnership Preparation for the termination of strategic alliances is significant, because all alliance terminate in the end (Hoffmann, Schlosser 2001). This process of termination is challenging and necessitates skills and subtleness. In order to avoid future disruptions, the relationship between the partners must be treated considerately and information distributed among all the parties. There a different types of termination, these include the cooperation is dissolved, the parent f irm regains its resources back, the cooperation unit is acquired by one of the parties, a new owner is appointed however continues with the cooperation. Whichever type of termination, of importance is the determination of conditions in the designing phase of the partnership, in order to avert any future disagreements (Hoffmann, Schlosser 2001). Strategic Alliances within the Airline Industry in General According to Oum, Taylor and Zhang (1993), in order to survive in the airline industry it is necessary for airlines to participate in an alliance. Operationally, airlines cooperate across a wide range of activities, which can be generally categorized as: customer service, flights, and operations support (Oum et al. 2000). Studies engaged in by Park and Cho (1997) and Oum et al. s (2000), project that alliances have the ability to improve a carrier’s performance on a variety of economic measures, including productivity, pricing, profitability, and share price. Common in the airl ine industry is the so called â€Å"code sharing† alliances, in which agreements are made allowing both parties to use each other’s designator codes (Power 2003). This phenomenon reportedly accounted the 50 largest commuter carriers by 1985 to have joined these code-sharing alliances (Oster, Pickerell 1986). By 2002, the airline industry accounted four alliances, which are Star Alliance, One World Alliance, the Sky Team Alliance and the Qualiflyer Group (Morrish, Hamilton 2002). As in every other industry, strategic alliances in the airline industry are complex and subjective to instability, unsatisfied performance and premature termination (Parkhe, 1993). One of the main alliance failures within the airline industry is the collapse of the Alcazar Alliance (Cameron 1994; Chambers 1994; Reed 1994). This was an alliance with partnering between Austrian, KLM, SAS and Swissair, which failed due to disagreement between the members of the alliance. Furthermore, the Qualif lyer Group will reportedly be disbanded, due to the collapse of main airline Swissair. Analyzing the Swissair Qualiflyer Case Study In 1989, Swissair (SAirGroup) was the first European airline to form an alliance with an overseas commuter carrier, namely Delta. From that moment many other alliances followed including, Singapore Airlines, SAS, who in return had alliances of their own, however all contributing to Swissair’s gain in access to the European Union market (Knorr, Arndt 2003). Continually, Finnair and Austrian Airlines joined to form the European Quality Alliance (EQA). The Alcazar Alliance, between Swissair, SAS, Austrian and KLM, was Swissair’s reaction to the veto against the European Economic Area (EEA) Treaty, however its existence was short lived due to negative media publicity, political pressures and unconquerable differences (Knorr, Arndt 2003). Nevertheless, Swissair continued the search for substitute partners, finding in Belgium’s Sabena the best candidate and forming the Qualiflyer Alliance in 1998, which was an equity-based alliance (Knorr, Arndt 2003). Evaluating the Qualiflyer alliance Suen (2002) argues that the Swissair Group’s bankruptcy is a direct consequence of mistakes made in the initiation phase (Smith, Wohlstetter 2001; Waddock 1989), in its alliance strategy. However, Knorr and Arndt (2003) hold that the company’s alliance strategy was more than just badly implemented, it was a fundamentally flawed approach to adapt the airline to the realities of the deregulated European airline market. In addition, they failed to integrate its regional subsidiary Crossair fully into its own Zurich-based operation. Suen (2002) argues with regard to Swissair and its early alliances that one significant recurring aspect is the defections visible in the alliance causing it to collapse. While various causes were the consequence of exiting the alliances, in all the cases the interdependence within the alliance was clearly visible resulting in not being powerful enough to maintain their partnering within the alliance (Suen 2002). However, according to Knorr and Arndt (2003), the fundamental flaws with respect to the Qualiflyer alliance are divided into two respects, namely 1) the Swissair brand was weakened by the Qualiflyer Alliance, due to the selection of second and third ranked commuter carriers. Above all these carriers were often in a financially leak state, 2) as a result of the above mentioned, Swissair was unable to acquire premium tariffs from passengers, which resulted in a decrease of financial results. Additionally, Knorr and Arndt’s (2003) view on the failure of Swissair’s earlier alliances including Qualiflyer Alliance finds its explanation in the self-confidence of the management within Swissair, expressed by the ignoring of the home market and the exclusion from the European aviation market, therefore taking on the role of â€Å"respective allian ces’ undisputed leader† (2003, p. 17). Due to this behavior the alternative toward joining one of the existing major alliances was never opted (Suen 2002). Finally, causes for failure in the Swissair alliances can be found in the ignoring of a competitive relationship with regional carrier Crossair, which was chosen rather than adopting a cooperative and integrative relationship (Knorr, Arndt 2003). Even though Swissair acquired a minority stake in Crossair, it allowed for Crossair to take on more routes, increasing its load factor which resulted in Crossairs’ implementation of the Eurocross strategy, which enabled the regional carrier to build a large-scale operation (Knorr, Arndt 2003). Analyzing the Star Alliance Case Study The formation of the Star Alliance was initiated in 1997, counting five members in the alliance, namely Air Canada, Thai, Lufthansa, United and SAS. The structure of the alliance united all airlines under one network, however individual identities of the airlines were preserved (Vinhais 2005). Even though the partnering airlines formed the alliance with a variation of intentions, the aspiration of an effective expansion of geographic networks is the combined intention visible in all the airlines (Vinhais 2005). Due to the fact that the Star Alliance experiences intense competition from rival alliances, such as Sky Team, OneWorld and Northwest Alliance, their present challenge is that of preserving their leading position as well as the expansion necessary to obtain leadership within the airline industry, which is accomplished through the management of an integrated network (Bruch, Sattelberger 2001). Since 2000, Star Alliance dispenses priority to the coordination and incorporation of strategic activities, which include establishing joint global branding, the joint evolvement of a technology platform and finally combined training and Human Resource development. In order to efficiently manage the above stated acti vities management was obliged to integrate the structure of the Star Alliance as a whole, which was achieved by implementing a specific team for the daily management of the alliance (Bruch, Sattelberger 2001). Within the Star Alliance, Lufthansa adopted the strategy of developing a federative network in addition to outsourcing on an internal basis, whereas different airlines opted for a strategy implementing to outsource externally. Lufthansa emphasized the importance of the examination of both internal defaults as well as the external environment for causes contributing to the crisis occurring in the airline industry. According to Bruch and Sattelberger (2001) in order to accomplish this objective an organizational restructuring was necessary 1) the adoption of an operational recovery process implementing both cost-cutting as well as downsizing 2) the key business activities were in need of reorganization, which was a complex structural process, emphasizing the necessity of broa dening its scope and evolving toward an aviation group instead of simply an airline company. This aviation group adopted a strategy with focus on various organizational areas 3) the realignment of a global network, in order to establish a higher quality network, which has proven sustainable when investigating the alliances’ reaction to the financial crisis, the alliance facilitated the disregarding of low-density routes meanwhile protecting the global network, which remained unharmed (Bartlett, Ghoshal, Birkinshaw 2005) With regard to the management of â€Å"soft† aspects, for example communication, in which investments were constantly visible, Star Alliances’ Lufthansa communicational process adopts five principles, namely 1) concentrating on multipliers, in order to reach employees 2) winning the crucial employees, seeing as they supply the customer with the product, workshops etc are organized in order to view progress among employee understanding 3) Devel oping a location where customers â€Å"meet, eat and greet†, including incorporating time for formal meetings as well as establishing relationships and networks 4) the creation of ‘by pass’ solutions, incorporating and combining communication flows of hierarchy nature as well as non-hierarchical nature, providing faster transfer of knowledge 5) communication as main change driver, taking various aspects nto account including informing, listening in addition to communicating cultural changes (Bruch, Sattelberger 2001). In favor of Star Alliance is its ability to combine competition with cooperation, regarding that the airlines within the alliance seem to recognize the gains toward more effectiveness to be achieved by depending on partners for support (Bartlett, Ghoshal, Birkinshaw 2005). Conclusion According to Hoffmann and Schlosser (2001) factors of crucial importance to the success of strategic alliances were identified in the primary phases of the alliance evo lution, especially in the phases, strategic decision and analysis and the designing of the strategic alliance. In explanation, the initiation of cooperation itself is determined in the strategic decision phase, implementing strategic alliances for inaccurate reasons, will probably result in unsuccessful alliances, thereafter a common thread between the partners is of importance in the partner selection phase in addition to the designing and structuring of the alliance. United with this view is that of Douma, Bilderbeek, Idenburg and Looise (2000), also emphasizing the importance of alignment between the partners, which enhances managements observation in distinguishing important drivers contributing to this alignment. In order to secure this alignment, continuous evaluation of the processes are indispensible (Douma, Bilderbeek, Idenburg, Looise 2000). In conclusion, these stated outcomes highlight the necessity of systematic preparation and cautious development of the primary phases of the strategic alliance (Hoffmann, Schlosser 2001). Bronder and Pritzl (1992) state that emphasizing organized analysis is an essential factor toward success in strategic alliances within the development stages of such alliances. Various success factors are identified from Hoffmann and Schlossers’ (2001) research, namely â€Å"precise definitions of rights and duties†, â€Å"contributing specific strengths†, â€Å"deriving alliance objectives from business strategy† and â€Å"speedy implementation and fast results† (Hoffmann, Schlosser 2001, p 275-376). When analyzing the two cases provided in this study on a basis of the phases presented numerous conclusion can respectively be drawn. Commencing with the SwissAir Qualiflyer alliance, which turned out to be an unsuccessful alliance, due to deficiencies within various phases through which alliances evolve. Visible within the partner selection phase was the choice toward forming an allianc e with second and third ranked commuter carriers, which above all were often in a financially leak state. This strategy resulted in a weak formation, with these second and third ranked airlines satisfied with not going bankrupt, however SwissAir weakening its brand image with this association with these vulnerable airlines. Additionally, the implementation and management phase contributed to the failing of the alliance by noticing the interdependence within the alliance, which was clearly visible and resulted in not being powerful enough to maintain their partnering within the alliance (Suen 2002). Finally, within the Qualiflyer, SwissAir case external factors played a significant role, which in this situation was the national airline Crossair. By allowing Crossair to take on more routes, which in turn increased its load factor, resulting in Crossairs’ implementation of the Eurocross strategy, which enabled the regional carrier to build a large-scale operation (Knorr, Arnd t 2003), concluding in disadvantageous consequences for SwissAir and Qualiflyer. In contrast the Star Alliance was a successful implementation of a strategic alliance, which is visible in various phases of the alliance evolution. In comparing the Qualiflyer and Star Alliance case the difference is found in the partner selection phase, Star Alliance valued a common view among its partner, which was the aspiration of an effective expansion of geographic networks is the combined intention visible in all the airlines (Vinhais 2005). All airlines enjoying a common vision resulted in a sound and stable formation of the alliance. Furthermore, the designing phase of the Star Alliance realized advantageous outcomes by efficiently managing activities by integrating the structure of the alliance as a whole, which was achieved by implementing a specific team for the daily management of the alliance (Bruch, Sattelberger 2001). Additionally, the management phase, even though management occu rs in every phase of the strategic alliance contributed by providing the ability to combine competition with cooperation, regarding that the airlines within the alliance seem to recognize the gains toward more effectiveness to be achieved by depending on partners for support (Bartlett, Ghoshal, Birkinshaw 2005). Moreover, management emphasized on the importance of the â€Å"soft† aspects, for example communication, which also contributed to a preserved common thread within the alliance. Finally, the realignment of a global network, in order to establish a higher quality network has proven sustainable (Bartlett, Ghoshal, Birkinshaw 2005), resulting in the achievement of their stated challenge, the preserving their leading position as well as the expansion necessary to obtain leadership within the airline industry (Bruch, Sattelberger 2001). Limitations The main limitations within this study is based on the fact that numerous variables were chosen on which to base the resear ch on strategic alliances, however even though careful evaluations as to which variables to include was exercised, the possibility of disregarding various variables is present. Furthermore, the variables incorporated into the study essentially should be analyzed within the context of the case studies implemented, however due to lack of research material for example not all distinguished phases were able to be evaluated. More specifically, within both case studies the limit of evaluation of the phases was considerable and approximately three to four phases were identified and able to be analyzed. Recommendations and Reliability In order for this study to gain more reliability, which is lacking due to the possible absent variables which are of additional importance to the success of strategic alliances and the absent analysis of the literature study within the case studies, extensive in-depth research is necessary within the strategic alliance spectrum in international business. Fu rthermore, the two examples, Qualiflyer and Star Alliance, of strategic alliances within the airline industry require additional evaluation in order to analyze the progress and outcomes of these alliances in the context of strategic alliance phases, which is key within this study. About the Author Claire Revell is an International Business and Management 2005 student at the Rijksuniversity Groningen. Within my bachelor degree I studied one semester at the Corvinus University in Budapest, where I attended business related courses. 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L. , Strategic Alliances in Action: Toward a Theory of Evolution, The Policy Studies Journal, Vol 33 (2005) pp. 419-442 Word Count: 7041 APPENDICES Table 1 [pic] Source: Hitt M. A, Dacin M, Levitas E, Arregle J. L, Borza A, Partner Select ion in Emerging and Developed Market Contexts: Resource-Based and Organizational Learning Perspectives, The Academy of Management Journal, Vol. 43, No. 3 (Jun. , 2000), pp. 449-467 Figure 1 [pic] Source: Niederkofler M, The evolution of strategic alliances: Opportunities for managerial influence, Journal of Business Venturing Volume 6, Issue 4, July 1991, Pages 237-257 Figure 2 [pic] Source: Das T. K. Teng B-S, Instabilities of Strategic Alliances: An Internal Tensions Perspective, Organization Science, Vol 11 (2000), pp. 77-101 Figure 3 [pic] Source: Douma M. U, Bilderbeek J, Idenburg P. J, Looise J. K, Strategic Alliances: Managing the Dynamics of Fit, Long Range Planning Vol 33, Issue 4 (2000), pp 579-598 Figure 4 [pic] Source: Kale P, Dyer J, Singh H, Value Creation and Success in Strategic Alliances: Alliancing skills and the Role of Alliance Structure and Systems, European Management Journal, Vol 19, Issue 5 (2001) pp. 463-471 Table 2 [pic] [pic] Source: Contractor F. J, Loran ge P, Cooperative Strategies and Alliances, Elsevier Science Ltd (2002), pp. 119 Don’t waste time! Our writers will create an original "Determination of Factors Contributing to Success in Strategic Alliances" essay for you Create order