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Manufacturing Strategy, Capabilities and Performance
Linköping University, Department of Management and Engineering. Linköping University, The Institute of Technology.
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This dissertation addresses the topic of manufacturing strategy, especially the manufacturing capabilities and operational performance of manufacturing plants. Manufacturing strategy research aims at providing a structured decision making approach to improve the economics of manufacturing and to make companies more competitive.

The overall objective of this thesis is to investigate how manufacturing companies make use of different manufacturing practices or bundles of manufacturing practices to develop certain sets of capabilities, with the ultimate goal of supporting the market requirements. The thesis aims to increase the understanding of the role of operations management and its immediate impact on manufacturing performance. Following the overall research objective three areas are identified to be of particular interest; to investigate (i) the relationship among different dimensions of operational performance, (ii) the way different performance dimensions are affected by manufacturing practices or bundles of manufacturing practices, (iii) whether there are contingencies that may help explain the relationships between dimensions of manufacturing capabilities or the effects of manufacturing practices or bundles of manufacturing practices on operational performance.

The empirical elements in this thesis use data from the High Performance Manufacturing (HPM) project. The HPM project is an international study of manufacturing plants involving seven countries and three industries.

The research contributes to several insights to the research area of manufacturing strategy and to practitioners in manufacturing operations. The thesis develops measurements for and tests the effects of several manufacturing practices on operational performance. The results are aimed at providing guidance for decision making in manufacturing companies. The most prominent implication for researchers is the manifestation of the customer order decoupling point as an important contingency variable to consider when studying manufacturing operations.

Place, publisher, year, edition, pages
Linköping: Institutionen för ekonomisk och industriell utveckling, Linköpings universitet , 2007. , 33 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1108
Keyword [en]
Manufacturing Strategy, Manufacturing Capabilities, Operational Performance, Empirical Research, Contingency Factors
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:liu:diva-8962ISBN: 978-91-85831-72-2 (print)OAI: oai:DiVA.org:liu-8962DiVA: diva2:23684
Public defence
2007-06-08, A1, Hus A, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2007-05-25 Created: 2007-05-25 Last updated: 2013-04-11
List of papers
1. Quantification in manufacturing strategy: a methodology and illustration
Open this publication in new window or tab >>Quantification in manufacturing strategy: a methodology and illustration
2006 (English)In: International Journal of Production Economics, ISSN 0925-5273, Vol. 104, no 1, 113-124 p.Article in journal (Refereed) Published
Abstract [en]

Strategic decision-making is often based on conceptual and qualitative models. Considering the vast amount of quantitative models in the literature, it is most interesting and important to explore the possibilities to expand the modelling base for decision-making with quantitative models that can provide deeper analysis, new insights and allow for finer sensitivity analysis. The purpose of this paper is to explore various aspects of quantification in manufacturing strategy-related issues. We review current approaches to quantitative modelling and study how quantitative models are being used and can be used for strategic decision-making in manufacturing. We create a framework and methodology for quantitative modelling for manufacturing strategy, based on market requirements, manufacturing capabilities, manufacturing actions within decision categories, and quantitative modelling approach. The framework methodology includes seven stages of quantification, for the purpose of measuring, linking, comparing, and modelling. The aim of the paper is to provide a structure that can aid in the modelling of strategic manufacturing decisions to improve the capabilities to meet market requirements.

Keyword
Manufacturing strategy; Operations; Quantitative modelling
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-14542 (URN)10.1016/j.ijpe.2005.09.004 (DOI)
Available from: 2007-05-25 Created: 2007-05-25 Last updated: 2010-06-14
2. Differentiating manufacturing focus
Open this publication in new window or tab >>Differentiating manufacturing focus
2006 (English)In: International Journal of Production Research, ISSN 0020-7543, Vol. 44, no 18-19, 3863-3878 p.Article in journal (Refereed) Published
Abstract [en]

In order for a manufacturing firm to be competitive, by supporting the market requirements through the manufacturing function, manufacturing should focus on a narrow set of tasks. Focused manufacturing is concerned with the perspectives when designing a manufacturing facility, be it a factory, plant, or plant within a plant. Traditionally, focus has been on the product, the process, or the manufacturing task based on competitive priorities (order winners and qualifiers). So far, the literature implies that a certain facility should have only one focus. In this paper, we present a framework that differentiates focus with respect to different parts of the manufacturing value chain. The point around which focus needs to be differentiated is the customer order decoupling point. We associate alternative types of focus relative to the customer order decoupling point, separating the upstream and downstream parts, and create a framework for choosing focus and how to differentiate manufacturing focus.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-14543 (URN)10.1080/00207540600702290 (DOI)
Available from: 2007-05-25 Created: 2007-05-25
3. Competitive strategies and manufacturing focus: an empirical analysis
Open this publication in new window or tab >>Competitive strategies and manufacturing focus: an empirical analysis
2007 (English)In: International Journal of Production EconomicsArticle in journal (Refereed) Submitted
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-14544 (URN)
Available from: 2007-05-25 Created: 2007-05-25 Last updated: 2010-05-31
4. Competitive capabilities: a contingency perspective
Open this publication in new window or tab >>Competitive capabilities: a contingency perspective
2007 (English)In: Journal of Operations Management, ISSN 0272-6963, E-ISSN 1873-1317Article in journal (Other academic) Submitted
Abstract [en]

In this paper we present and test an alternative model for competitive capabilities.Traditionally, a cumulative model has been viewed as having one sequence of buildingoperations capabilities in a firm in support of market needs, including quality,delivery, cost efficiency and flexibility. Although appealing as a conceptual model,empirical testing has not been able to fully support the cumulative model. This paperacknowledges the need for differentiated approaches to managing capability indifferent operating environments. The competitive capability model that is presented istested empirically using data from the High Performance Manufacturing (HPM) study,including three industries and seven countries – a total of 211 plants. The results showthat there is empirical support for differentiating the competitive capabilities; firmsproducing to stock follow a path of quality, delivery and cost, whereas those producingto customer order exhibit a capability path in the order of quality, delivery andflexibility. Thus, while quality and delivery are common, cost and flexibility acts asdifferentiators contingent upon the manufacturing environment.

Keyword
Operations strategy; Empirical research; Competitive capabilities; Decoupling point
Identifiers
urn:nbn:se:liu:diva-53024 (URN)
Available from: 2010-01-14 Created: 2010-01-14 Last updated: 2017-12-12
5. Flexibility configurations: Empirical analysis of volume and product mix flexibility
Open this publication in new window or tab >>Flexibility configurations: Empirical analysis of volume and product mix flexibility
2009 (English)In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 37, no 4, 746-756 p.Article in journal (Refereed) Published
Abstract [en]

In this paper we address flexibility and investigate the relationship between volume and product mix flexibility. One view of flexibility is that of being a capability in itself: another view is that of flexibility as an enabler, providing the manufacturing system with properties on which other competitive capabilities are built. In this research, the latter view of flexibility is used, where flexibility acts as a second order competitive criterion. The aim is to differentiate between two dimensions of flexibility important to the manufacturing value chain, i.e., volume and product mix flexibility, and to investigate how different flexibility configurations are related to Various manufacturing practices. A clustering research approach is used to identify groups of companies based on flexibility configurations. The groups are then analyzed with respect to characteristics and impact on operational performance. For the empirical investigation, we use empirical data from the high performance manufacturing (HPM) study, including three industries and seven countries-a total of 211 plants. We find that flexibility configurations based on high or low levels of volume and mix flexibility combinations show significant differences both in terms of operational performance, and in terms of emphasis put into different flexibility source factors.

Place, publisher, year, edition, pages
Kidlington, Oxford, United Kingdom: Pergamon Press, 2009
Keyword
Empirical research, Flexible manufacturing, Operations management, Survey, Value chain
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-16407 (URN)10.1016/j.omega.2008.07.004 (DOI)000262063700002 ()
Note

Original Publication: Mattias Hallgren and Jan Olhager, Flexibility configurations: Empirical analysis of volume and product mix flexibility, 2009, OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, (37), 4, 746-756. http://dx.doi.org/10.1016/j.omega.2008.07.004 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/

Available from: 2009-02-09 Created: 2009-01-23 Last updated: 2017-12-14Bibliographically approved
6. Lean and agile manufacturing: external and internal drivers and performance outcomes
Open this publication in new window or tab >>Lean and agile manufacturing: external and internal drivers and performance outcomes
2009 (English)In: International Journal of Operations & Production Management, ISSN 0144-3577, E-ISSN 1758-6593, Vol. 29, no 10, 976-999 p.Article in journal (Refereed) Published
Abstract [en]

Purpose - Lean and agile manufacturing are two initiatives that are used by manufacturing plant managers to improve operations capabilities. The purpose of this paper is to investigate internal and external factors that drive the choice of lean and agile operations capabilities and their respective impact on operational performance. Design/methodology/approach - Lean and agile manufacturing are each conceptualized as a second-order factor and measured through a bundle of distinct practices. The competitive intensity of industry and the competitive strategy are modeled as potential external and internal drivers, respectively, and the impact on quality, delivery, cost, and flexibility performance is analyzed using structural equations modeling. The model is tested with data from the high performance manufacturing project comprising a total of 211 plants from three industries and seven countries. Findings - The results indicate that lean and agile manufacturing differ in terms of drivers and outcomes. The choice of a cost-leadership strategy fully mediates the impact of the competitive intensity of industry as a driver of lean manufacturing, while agile manufacturing is directly affected by both internal and external drivers, i.e. a differentiation strategy as well as the competitive intensity of industry. Agile manufacturing is found to be negatively associated with a cost-leadership strategy, emphasizing the difference between lean and agile manufacturing. The major differences in performance outcomes are related to cost and flexibility, such that lean manufacturing has a significant impact on cost performance (whereas agile manufacturing has not), and that agile manufacturing has a stronger relationship with volume as well as product mix flexibility than does lean manufacturing. Research limitations/implications - Cross-sectional data from three industries and seven countries are used, and it would be interesting to test this model for more industries and countries. Practical implications - The results provide insights into the factors that influence the choice of lean or agile manufacturing for improving operations, and the results that can be obtained. Originality/value - To the authors knowledge, this is the first large-scale empirical survey of leanness and agility simultaneously, using data from manufacturing firms in Europe, Asia, and North America. The model incorporates a wide perspective on factors related to lean and agile manufacturing, to be able to identify similarities and differences.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2009
Keyword
Lean production, Agile production, Operations management
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-51776 (URN)10.1108/01443570910993456 (DOI)000271206100001 ()
Available from: 2009-11-18 Created: 2009-11-17 Last updated: 2017-12-12Bibliographically approved

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