Increasing complexity of products and processes with simultaneous high performance requirements place unprecedented challenges and opportunities on the sustainable development of organizations. Any enterprise is required to regularly review its activities and planning to deal with the fiercely competitive, rapidly changing and dynamically shrinking world market. Owing to these changes, focus has now been shifted from selling the product or services to capturing the voice of the customer most satisfactorily. The information about customer requirements may come from different sources, in different forms and patterns. To incorporate the changes, driven by such information, it is prerequisite to establish a corporate culture to continuously look for the means and mediums to improving their productivity and operating performances. Elimination of unnecessary activities, procedures, flow lines, man power deployment, machine sequencing, etc., are some of the factors governing and providing thrust to any firm's competitiveness. These correspond to emblematic features of ‘Lean or Agile Production Systems’ which has a common goal of designing, developing, manufacturing, and distributing products or services to meet customer requirements with minimum resources.
In today's high technology, quality and cost driven, competition-orientated business world, lean and six sigma methodologies play a prominent role in enhancing the performance, productivity and profitability of manufacturing firms. It alleviates defects, waste, lead time, and inventory simultaneously through improving quality, reliability, availability and by building consensus amongst employees for improvement initiatives. Basic tenets of lean and six sigma philosophies encompass a mixed approach that deals with mindset as well as a range of techniques to assess the work processes. Whenever these approaches are contemplated to encourage a culture of innovation and creativity, they can be termed as a part of the mindset. Various techniques are also part of implementing these philosophies to support automated decision making and in developing better benchmarking and performance management standards. In retrospect, many attempts have been addressed in the literature to enhance the performance of manufacturing firms through implementation of lean and six sigma; however, their effective implementation are time consuming due to scarcity of knowledge.
Hence, it is highly desirable to develop a decision support system (DSS) for an effective implementation of lean and six sigma methodologies to tackle problems in the real world. A properly designed DSS is an interactive system, intended to help decision makers to compile useful information from preliminary information, documentation, personal knowledge, and experience to recognize and solve problems and arrive at intelligent decisions. The current trends in implementation of lean and six sigma methodologies, difficulties confronted by the researchers as well as the industry practitioners to arrive at appropriate decision demands a knowledge base and research material at one place. Accordingly, this special issue is aimed at meeting the challenges posed and overcoming the existing gaps. It includes state-of-the-art decision support systems on some critical research issues pertaining to lean and six sigma approaches. Reviewers are accomplished research professionals in this area.
The papers included in this issue address prominent concepts and techniques in the context of lean and six sigma methodology, namely:
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Wan and Chen propose an integrated and quantitative model for leanness measurement of a manufacturing firm. It is inspired by data envelopment analysis (DEA) and extracts the value-adding investment from a production process. A slack based measurement (SBM) model of DEA directly measures the slack of input and output variables and determines the efficiency score. Quantified lean initiatives help in estimating the decision support information for decision support unit (DSU) and fulfil the firm's strategic purposes.
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Biswas and Sarker deal with a manufacturing cycle where rework is carried out. To overcome the shortage due to scrap production, a finished product buffer has been considered. For a single stage production process different inventory models have been developed based on the shop floor observations. The total cost function formulated finds the optimum operational policy for manufacturing batch sizes and economic storage of inventories. The waste minimization thus achieved ameliorates the basic need of lean philosophy.
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Hu et al. develop a unique DSU based on a multi-objective formulation for project portfolio selection in manufacturing companies. Performance scores of lean and six sigma sub-goals are evaluated and a benefit index is calculated for a particular project. Thereafter the Pareto optimal chart is developed to assist in decision making. The proposed model helps in successful implementation of lean and six sigma concepts in manufacturing firms.
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Barad and Dror evolved a strategy map for an enterprise using the quality function deployment methodology. It consists of four hierarchical levels viz. business objectives, competitive priorities, core processes and components of the organizational profile. Weakest links (the most urgent improvement needs) are identified by the strategy map at different hierarchical levels. This information is useful as input for the application of six sigma and lean methodology.
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Jain et al. exemplify the vagueness in the information perceived for the implementation of lean or six sigma methodologies. They model agility (which includes leanness) and propose dynamic agility index (DALi) through fuzzy intelligent agents. It provides consensus in the fuzzy opinions viz. flexibility, profitability, quality, innovativeness, pro-activity, speed of response, cost, robustness, etc. for integrated supply chains, and successfully simulate dynamic agility.
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Shah et al. discover the implementation pattern of 15 lean practices and six sigma programs through the study of 2511 plants. They find that implementation of any practice from a broader set of lean practices improves the likelihood of implementing six sigma. Moreover they explicate a significant difference in the performance levels of the six sigma implementers group compared with the non-implementer group.
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Bunce et al. integrate the six sigma concepts with the industrial engineering tools within a quality framework. This helps in improving the crateless retort production system in view of the damaged cans. Close loop feedback system is implemented to revise the Ishikawa diagram. The DMAIC approach is successfully applied to reduce the can damage.
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Thawesaengskulthai and Tannock suggest a multi-criterion decision aid technique to help the managers in deciding the business improvement strategy. The multiple criteria decision-making (MCDM) used for decision aid is supported by a selection framework that promote rational decision-making and assists decision-makers to frame their evaluation process, compile useful information and reach a consensus decision with confidence.
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Van Iwaarden et al. investigate the meaning of the six sigma improvement approach, perceived by practitioners around the world. The detailed survey carried out and inferences made indicate that a certain level of quality management is a prerequisite for successful implementation of six sigma methodology. Well guided and clear cut strategy is required by an organization to seek the long term benefits with this tool.
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Vinodh et al. discuss a decision support system called DESSAC (Decision Support System for quantifying Agile Criteria). It helps in quantifying the current agility level of a company so that the refined 20 level agility model can be adopted by the organization.
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Gowen III et al. explore the applicability of knowledge management in juxtaposition with six sigma. Six sigma initiatives, knowledge acquisition, knowledge dissemination, knowledge responsiveness, quality program results, and sustainable competitive advantage are key constructs of the suggested framework. The responses to a questionnaire related to each construct are studied and effect of knowledge dissemination, knowledge responsiveness and knowledge acquisition is investigated. It is found that knowledge management improves the six sigma implementation by improving the knowledge responsiveness and lends sustainable competitive advantages. Considering the quality and relevance of the topic, this article has been included in this special issue to highlight the importance of decision support system for lean and six sigma in the service sector.
‘A research field is defined by the genuine phenomenon rather than an academic ghetto’ can easily be tracked down from the variety of articles included in this issue. Most of the articles selected here have potential for further extension and it can also be tested by the practitioner to resolve the field problems encountered at different stages during implementation of continuous improvement methodologies.
We wish to place on record our special thanks to Editor-in-Chief John E. Middle for his valuable guidance and support during the entire process of editing the special issue. We acknowledge the contribution of Camela Valentine for promptly communicating the guidelines and suggestions given by the Editor-in-Chief. We offer our thanks to the Taylor & Francis editorial team for their active role and support.
Finally, we would like to take this opportunity to thank the authors who contributed to this special issue. We express our special gratitude to the referees, who evaluated the papers and made valuable comments for improving their quality. We extend our special appreciation to research scholars who participated at different stages in this project.
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