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The purpose of this paper is to investigate the strategic role of plants, in terms of the type and level of site competence, the relationship with the strategic reason for location, and the impact on operational performance.

The authors use a survey of 103 Swedish manufacturing plants that belong to global production networks and analyze patterns within this context to identify potential archetypes of plants with respect to plant roles, based on factor analysis and cluster analysis.

It is found that the areas of site competence can be grouped into three bundles, characterized thematically as production‐related, supply chain‐related and development‐related. The plants fall into three categories: some plants have only production‐related competences, some have competences concerning both production and supply chain, and the third group of plants possesses all three bundles of competences.

The results provide empirical evidence that site competences come in bundles in three steps according to themes rather than individually. No significant relationship was found between the level of site competence and the strategic reason for site location.

The results provide empirical support for the co‐location of product development and production, since plants with full responsibility for all competence bundles significantly outperform plants having only production‐related competences on cost efficiency, quality, and new product introductions.

The authors research patterns of site competence at a more detailed level than before in the related literature, as well as study the impact on performance, which has not been done before.

A NOTED engineer of our acquaintance started his career as an apprentice working under a foreman of Huguenot descent and who was known affectionately—well, generally!—as Papa Poisson. He was very knowledgable and was very fond of telling his juniors how he had worked, many years previously, at Rolls‐Royce when that name really meant something.

Attempts by Greek governments to attract manufacturing industry to problem regions have been shaped by a large number of development laws. Location policy works principally by influencing the decisions of individual firms. The article examines the relevance of Greek government location policy and the local criteria actually used by firms operating in the food industry. Raw materials were the most important determinant and the results otherwise suggest that the use of financial factors as an instrument of regional policy needs re‐examination.

Aims to identify factors which affect the location decisions of companies in the Greek metal industry. Attempts to determine what these factors are, how important they are relative to each other and how important taxes and inducements (financial factor) are in the process of location decision making. The methodology employs: a point allocation system to evaluate the relative importance of the various location factors, experts′ opinion and mail questionnaire for data collection, experience in undertaking new investment for drawing the sample firms, a prospective approach, and non‐parametric statistics. The results are: 16 factors are identified; labour is the first, and financial factor is the second in importance; and the difference in importance between financial factor and other remaining factors is not statistically significant. Concludes that, given the evidence there would be nothing to lose and everything to gain from a much more critical approach to the use of the financial factor as an instrument of regional policy.

The paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.

To optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.

This paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.

Very few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

The purpose of the paper is to present an integrated model for the location of warehouse, the allocation of retailers to warehouses, and finding the number of vehicles to deliver the demand and the required vehicle routing in order to minimize total transportation costs, fixed and operating costs, and routing costs.

The model assumes that the number of plants has already been determined and answers the following questions: what is the number of warehouses to open? How warehouse are allocated to plants? How retailers are allocated to warehouses? Who are the retailers that will be visited and in what order? How many vehicles are required for each route? What are the total minimum costs?

The model was formulated as a mixed integer linear programming model and solved using Lagrange relaxation and sub‐gradient search for the location/allocation module and a traveling salesman heuristic for the routing module. The results for the randomly selected problems show that the deviation in objective function value ranges between 0.29 and 2.05 percent from the optimum value. Also, from the CPU time point of view, the performance was very good.

An attempt is made to integrate location, allocation, and routing decisions in the design of a supply chain network.

The purpose of the paper is to identify the location decision criteria and their relative weights in the context of food manufacturing internationalisation into emerging markets, and to clarify how supply chain related factors are reflected in the location decision criteria. Knowledge of these factors is considered crucial in understanding the entry and location decisions of foreign food manufacturers into emerging markets where a high level of operational uncertainty may be evident.

The AHP method is utilised in the creation of a location/investment target ‐model, with input from a panel of decision makers in the Finnish food industry. A real‐life application from the Russian agribusiness industry is presented.

Seven criteria reach above average values, namely economic activity potential (10 per cent), consumption potential (9 per cent), available acquisition targets (12 per cent), adequate supply of raw materials (13 per cent), competitive situation (16 per cent), favourable level of input costs (11 per cent), and SCM readiness and know‐how (9 per cent). The significant weight of the last item and the supply chain related factors in general underscore the total demand‐supply network‐oriented thinking in the food industry.

Supply chain considerations are a major factor in the food industry location decisions, due to the need to utilise the local sourcing, distribution and service systems in the economy. The downstream, upstream and related service sectors in the supply chain can thereby serve as constraints for the business expansion process.

The research addresses the sparsely researched area of linking a firm's internationalisation decision making with supply chain management issues in the emerging market context.

The purpose of this paper is to focus on selection of a location which can widely influence business strategy planning and operation profit.

In this paper, the relationship between location strategy and clustering is primarily shown by obtaining location decision criteria and the impact of high‐tech firms with the decision‐making trial and evaluation laboratory (DEMATEL) method. Then, analytic network process (ANP) method is used to explain the value and benefits from cluster location.

The authors study decision‐making for location selection of high‐tech firms in Hsinchu Science Park. The example is based on two dimensions including network effect and transaction cost. Network effect plays a more significant role than transaction cost for this example location selection of high‐tech firms in Hsinchu Science Park. The Science Park is surrounded by entrepreneurial spirit, a significant talent pool, and support infrastructure.

This paper explores how high‐tech managers make their decisions on location strategy in Science Park.

The authors found that cost and benefit factors of network effect dominate the major decision‐making in selection of location and has direct impact on strategic intent. They also have shown that the DEMATEL and ANP approaches are valuable for evaluating this situation.

To provide a method for the corporate real estate director to gather information about the quality of life (QOL) desires of key employees at a new corporate location.

The paper presents a discussion of a personal survey technique that can be used to gather information about the QOL concerns and needs of key employees regarding new locations the firm might be considering as possible expansion or relocation sites.

The publicly disseminated QOL rankings in popular magazines about metropolitan areas provides only marginally important information for the corporate officer in charge of making a relocation decision, because the information is not directly linked to the employees who will be affected by the move.

The discussion of the contents of a personal survey will provide the means to discover the significant benefits perceived by the key employees about the new location and a means to discover any major concerns, issues and problems about moving to a new corporate location.

The paper brings the QOL concept from its position as a newsworthy curiosity about metropolitan areas into the corporate decision process as a way to involve the opinions and desires about QOL of key employees in a corporate relocation decision.

A variant of Kohonen's algorithm for the self‐organizing map (SOM) is used to solve a continuous location‐routing problem that can be applied to identify potential sites for subsequent selection by a discrete finite set model. The paper aims to show how the algorithm may be customized to fit the problem structure in a way that allows aspects of location and routing to be integrated into the solution procedure.

A set of test instances is used to compare the solutions of the neural network to those obtained by sequential approaches based on a savings procedure.

Compared to the results of the sequential approaches, the neural network yields good results.

Future work may cover the expansion of the neural approach to multi‐depot and multi‐stage problems. Additionally, application of procedures other than the savings procedure should be evaluated with respect to their potential for further enhancing the solution quality of the sequential approaches.

This paper shows that strategic location decisions in practical applications with long‐term customer relationships can be taken using simultaneously generated routing information on an operational level.

The paper provides a new variety of applications for SOM as well as high quality results for the specific type of problem considered.