Dr. Saleeshya P. G.

This paper is an outcome of a study carried out in five manufacturing industries. Based on the literature review, field study and industry study a multi-level model which constitute the enablers of agile supply chain, the factors which are identified as the sources of driving force of these enablers has been developed. This model also identifies the business performance indicators (BPIs) of a manufacturing system. The objective of this paper is to identify the most important enablers of agile supply chain and the driving forces behind these enablers in an organisational system. By identifying these factors and forces, an organisation can channel its resources in the most effective way in order to enhance the achievement potential of its different BPIs. Multiple regression and Gauss elimination method are identified as suitable tools for analysis in this study. Copyright © 2015 Inderscience Enterprises Ltd.

The paper presents a case study conducted at a surgical equipment manufacturing company, where productivity improvement can be achieved by reducing the lead time required to manufacture their product. Using an important lean tool value stream mapping (VSM) the various wastes that are held up at each stage of manufacturing is identified, from which the most critical waste has been selected using a cost-time profile (CTP). Then a model is developed and solved using regression analysis. Based on the values obtained a cost-time profile is drawn again and compared, which reveals that there is nearly a 50% increase in the overall productivity.

Changing customer and technological requirements force manufactures to develop lean capabilities. Many organisations have adopted lean thinking paradigm in their drive to optimise performance and improve competitive position. In this paper, the application of lean production systems is described in an automotive context. The results reported are based on the data collected from a survey covering around 15 manufacturing companies in India using a standard questionnaire. The study was driven by a conceptual model which relates lean practices to competitive objectives. The method of analytic hierarchy process (AHP) was used to analyse and identify which among the various lean production tools and enablers are likely to have greater impact on realising a lean production system. © 2013 Inderscience Enterprises Ltd.

Lean manufacturing is a philosophy of eliminating waste through continuous improvement. This paper is an outcome of a case study conducted in a textile industry in south India. Though the concept of lean manufacturing has shown good results in continuous process industry, it has not been extensively used as compared to discrete manufacturing industries. Process industries, especially textile industries, have automatic machinery which are highly inflexible and have high volume/low variety products. This nature of the textile industry makes implementing lean manufacturing techniques a challenge; hence implementing lean techniques in a textile industry has been taken up as a challenge. We have chosen a combination of value stream mapping (VSM), 5S, kanban, kaizen, poka-yoke, and visual controls to improve the processes. The achievement potential scores before and after lean implementation has been highlighted using radar diagrams. The findings of this study reveal that a thorough analysis of the process, setup, and changeover time (CO), use of colour coding for identification of volume-mix, use of kaizen and quality circles which empower the workforce, are some of the various keys to a successful lean implementation in a textile industry.

This paper is related to a real life study carried out in Indian manufacturing organisations to assess the extent to which the agile manufacturing paradigm is recognised and deployed, as this paradigm has been a major objective of many companies. A multi-level framework developed by the authors, along with a questionnaire was used to identify various enabling factors of agility in Indian manufacturing industries. This model helps to determine the measure of agility called ‘Effectiveness Index’ (EI) at the various hierarchical levels of an organisation. In this paper we propose an approach to measure the agility of manufacturing systems by combining the analytical hierarchy process (AHP) and the data envelopment analysis (DEA) methodologies. The method of AHP was used to identify important agility enablers by deriving appropriate weights (Importance Index). DEA was used to compare and find out the priorities of various units of organisations by making use of the values of EI and Importance Index for agility.