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The purpose of this research was to construct a quantitative, objective metric for agility performance that assesses agility as a performance outcome, capturing both organizational success and environmental turbulence, and applicable to manufacturing organizations of all types.

The agility performance metric was developed by creating a theoretical model and then operationalizing the model through literature review, case studies, and pilot survey data. It was subsequently refined, based on input from an expert panel and survey responses.

The agility performance metric is demonstrated using data from four manufacturing plants, which represent the four possible combinations of success and turbulence.

The agility metric developed is consistent with the theoretical model, as well as empirical evidence from the demonstration companies. Further validation of the metric is necessary to fully establish this approach as a valid and reliable assessment tool.

This approach could be used by manufacturing managers to get a “snapshot” of an organization's agility performance level and to systematically consider the influence of environmental entities.

Consideration of agility as a performance outcome, rather than a structural or operational characteristic is a novel approach. The resulting quantitative performance index, which closely matches the theoretical definition of agility, is applicable to any type of manufacturing organization, can be used to make direct comparisons between manufacturers in different industries, and will automatically be updated over time as success is relative to industry medians.

To propose a theoretical model of leagile manufacturing as it applies to a single corporate enterprise with multiple business units and to generate research questions stemming from the model that should be addressed in the future.

A case study company was analyzed to determine whether the concept of leagility could be applied to a single corporation with multiple business units and whether a decoupling point would be necessary to distinguish the lean and agile portions of the enterprise. The case study findings are used as the basis for describing a theoretical corporate leagile infrastructure and for stimulating new research questions.

It is possible for a corporation to simultaneously pursue both lean and agile manufacturing strategies by adopting a leagile infrastructure. The organizational structure consists of three main levels: a corporate headquarters, a sales and service group, and multiple lean production units. There is a decoupling point that separates the lean and agile portions of the enterprise. This organizational structure matches the front‐back approach, one of the large/small strategies defined by Lawler in 1997.

A single company was examined. Studying a broader range of companies would make the described theoretical leagile corporate infrastructure more robust.

Manufacturing corporations might find the infrastructure described to be a beneficial way to structure their own organizations in order to capitalize on the benefits of both the lean and agile manufacturing strategies.

This paper expands on the concept of leagility, previously discussed in the literature with respect to supply chains and individual manufacturing plants, by applying it to a single corporation with multiple business units. Similar to other characterizations of leagile manufacturing, it was found that the corporation operates with a decoupling point between the agile and lean portions of the business. Several new avenues for further research are outlined.

The purpose of this paper is to introduce a hybrid framework (suppliers, inputs, process, output and customers+define, measure, analyze, improve and control (SIPOC+DMAIC)) aimed at improving supply chain management (SCM) process dimensions in a supply chain (SC) network.

Based upon the critical review of literature, process dimensions (average outgoing quality limit (AOQL), average outgoing quality (AOQ), process Z, defect per million opportunity) critical to SCM performance were identified. A framework consisting of three phases, i.e., design, implementation and results has been conceptualized and a case from paint industry is investigated. Implementation framework makes use of SIPOC model and Six Sigma DMAIC methodology. The goals of the study were achieved by using Six Sigma tools such as brainstorming sessions; root cause analysis, histograms, statistical tools such as control charts and process capability analysis.

Authors made an attempt to propose a conceptual framework for improving process dimensions in a SC network. It is observed from the results that selection of appropriate strategies for improving process performance based upon experiences, and use of statistical tools by cross-functional teams with an effective coordination, guarantees success. Metrics such as AOQL shows the maximum worst possible defective or defect rate for the AOQ. Process Z helps to know about sigma capability of the process.

The framework so developed is tested in a single company manufacturing batches of paint. The study has important implications for the industry since it tries to integrate SCM process dimensions which would help in successful implementation of SCM practices in firm by following the DMAIC process. The framework enables the practitioners to investigate the process and demonstrate improvements using DMAIC which makes use of statistical tools.

Although process dimensions related to SCM are critical to organization competitiveness, research so far has tended to focus on supply chain operations and reference model, balanced scorecard, total quality management, activity-based costing, just in time, etc., but in literature hardly any description of the SIPOC-DMAIC model to improve SCM process performance is provided. The use of statistics in DMAIC provides better insight into the process performance, and process control.

The purpose of this paper is to develop a set of key performance indicators (KPIs) for agile manufacturing (AM) and to propose a methodology for its performance evaluation.

The proposed methodology was developed using fuzzy analytic hierarchy process (FAHP) and performance value analysis (PVA) to evaluate and analyze the AM performance. The FAHP is applied to determine the importance of KPIs, and PVA is used to evaluate AM performance.

The proposed methodology is applied to an Indian auto component manufacturer, and it is observed that there is an improvement of performance along the timeline.

The proposed approach is generic in nature and can be applied to different agile business environments for performance evaluation.

This study provides insights into the AM performance evaluation. The managers can establish the impact of each significant area (SA) on AM and each KPI on its corresponding SA by capturing their manufacturing environments.

Although many issues related to AM have been widely researched, only a few studies have been carried out to quantify, analyze and evaluate the AM performance in the Indian manufacturing environment. The proposed model has the ability to capture the performance of AM along the KPIs to draw fruitful conclusions.