This thesis is aimed at enriching a research theme, focused on exploiting the planning, design and control strategies in the flexible manufacturing systems towards achieving make-span performance improvement. A salient contribution of this research effort is focused on adopting the concept of Flexible System of Integrated Manufacturing (FSIM) as a central theme related to Flexible Manufacturing System (FMS) in Computer Integrated Manufacturing System (CIMS) context. Planning strategies involves system operating under alternative system load conditions namely: Full Balanced Load (FBL), Balanced Machine Load and Unbalanced Processing Time (BMLUPT), Unbalanced Machine Load and Balanced Processing Time (UMLBPT) and Unbalanced Load (UBL). The second planning strategy being considered is system configurations based on the availability of buffers. The design strategies include routing flexibility, pallet flexibility and number of pallets in the systems. Routing flexibility is the capability of processing a part through varying routes. Pallet flexibility is defined as the ability of the pallets to hold and move efficiently several part types from one point to other. The influence of these factors on the performance of FSIM is evaluated. Control strategies manifest itself in the form of dispatching and sequencing rules. The main aim of these studies involves suggesting framework, simulation studies and analyzing the result to highlight important possibilities and implications. The demo simulation models are for deterministic environment, and the use of discrete event simulation approach is adopted to offer a focus on dynamic behavior of FSIM operating under different strategies. The key issues, which are addressed in this thesis, are: How do different levels of routing flexibility impact the system performance under the influence of alternative system load conditions? Is the make-span performance of FSIM significantly superior at different levels of routing flexibility? Is routing flexibility beneficial with increase in number of pallets in the system? What is the suitable number of pallets at which maximum benefit is obtained from the system? Do control strategies play an important role in the performance of FSIM? What is the impact of control strategies under various levels of flexibility under different conditions of buffers? The research shows that for a given system configuration, a given level of flexibility offers maximum benefit. Increasing flexibility from a given level offers marginal benefit and can sometime be counterproductive. There is a definite range of pallets in the system at which the system performs satisfactorily. This range is dependent on routing flexibility, storage configuration, design and control strategies etc. It is important to model and determine the right range of pallets that will offer superior performance for a given set of operating conditions. For a dynamic environment this may be a very useful knowledge for practitioners. Simulation of the flexible system modeled shows that storage capacity and configurations has an important impact on the overall system configuration.

The present work has been done for a telecom company with a focus on cost and flexibility in effectively deals with changing scenario. In this paper, the major problems faced by company at upper end of supply chain and sales outlet are analyzed and a complete inventory analysis on one of a company product is done by developing an Inventory model for the company bound store/distribution center and optimal inventory policy is suggested for the outbound logistics on the basis of simulation analysis. This model is flexible enough to respond to the market fluctuations more efficiently and effectively. The model is developed in Microsoft EXCEL.

An approach for demonstrating the impact of product information and decision synergy on the effectiveness of product recovery decisions is vital. The evaluation approach presented in this paper suggests various actors that can play a part in a product’s lifecycle to quantify the benefit of making relevant product information available for decisions to the product recoverer. The expected benefit of information is positive if and only if at least one of the outcomes of the observation has the ability to change (flexible) the recovery decision maker’s alternative of the recovery option. Therefore, it was observed that availability of information need not always yield a positive benefit in terms of improved decision leading to improved recovery system performance. This led us to identify the conditions of synergy for improving decision by collecting more or appropriate level of information. This paper also proposes a semi or partially flexible decision model that facilitates flexible decision and information interoperability functions from the perspective of an enterprise engaged in or to be engaged in product recovery.

Supply chain excellence has a real huge impact on business strategy. Building supply chains (SCs) as flexible system represents one of the most exciting opportunities to create value (e.g., seamless SCs). This requires integrated decision making amongst autonomous chain partners with effective decision knowledge sharing among them. The key to success lies in knowing which decision has more impact on the supply chains performance. Knowledge sharing has immense potential to create expedient opportunities and thus retain greater value for supply chains. In this context, knowledge management (KM) can be used as an effective approach to achieve knowledge sharing and decision synchronization among supply chain partners. To maximize competitive advantage, concept of seamless supply chains is emerging with KM as key enabler. Thus, there is a need to develop demo models that can encourage chain members towards collaborative-knowledge sharing in the SCs. This paper depicts the application of one such model based on decision knowledge sharing (DKS) for improved supply chains management. We study the impact of DKS (both partial and full DKS configuration in SC) and then compare the performance with information sharing (IS) and forecasting. By exploiting DKS and flexibility in supply chains structures better performance can be achieved. The paper develops the demo models on various supply chains scenario like (1st, 2nd and 3rd stage SCs, forecasting, IS and DKS (full and partial). The partial and full DKS based flexibility configurations of SCs are considered for simulation experimentation. A simulation model of a supply chains based on flexible framework is developed for demo purposes. The key results are highlighted along with the respective industry implications. Our research is continuing in this direction.

This paper is aimed at providing a broad review of the main approaches to supplier-related issues especially supplier selection, supplier-buyer relationships, supplier-buyer flexibility in relationships in a dynamic supply chain, through the description of the main characteristics, techniques, ongoing developments and research activities. Also, the issues relating to integration of core processes across organisational boundaries through improved communication, partnerships, alliances and cooperation are addressed, with its inherent focus on Web-enabled collaboration among the suppliers and the buyers. The aim of this paper is to consolidate the existing research efforts concerning the supplier-related issues, and to identify promising emerging issues for further study in this area. More specifically, this work analyses how diverse modelling techniques such as agent technology, petri nets, fuzzy logic and data mining can be applied to support dynamic supply chain configurations with reference to supplier-related issues.

The faster parts, information and decisions flow through supply chains, the faster they can respond to customer needs and to the demands of the market. In the last decade, interest has been focused on the volatile market conditions and thus, the issue of demand information sharing is paid much attention. The principal objective of this study is to understand the potential values of sharing timely supply information with random demands at retail outlets. This paper proposes a theoretically rigorous framework for the modelling, qualitative analysis and computation of solutions to supply chain network problems within an equilibrium context in the case of non-stationary demands and information associated with supply chains. More specifically, a mathematical model to understand the potential values of sharing timely supply information with random demands is presented in this research. In dynamic supply chain networks, an unforeseen change may occur at any material and gradually influence the material flow at downstream stages with time. Therefore, in this paper, the impact of the change from the time aspect and the expenditure aspect is quantified respectively. By implementing the proposed algorithm, a participant at the downstream stage can figure out how long and how much the change may impact on its own performance just when the change occurs on the upstream stage. The aim of this research is not only to demonstrate the existence of bullwhip effect, but also to quantify it, i.e. to quantify the increase in variability at each stage of the supply chain and derive the market equilibrium conditions. Efficacy of the proposed approach is demonstrated with an illustrative numerical example.

A typical Flexible Manufacturing System (FMS) has been studied under Planning Design and Control (PDC) strategies. The chief objective is to test the impact of design strategy (routing flexibility) on system performance under planning strategy (alternate system load condition) with control strategies (sequencing and dispatching rules). A computer simulation model is developed to evaluate the effects of aforementioned strategies on the make-span time, which is taken as the system performance measure. Shortest Processing Time (SPT), Maximum Balance Processing Time (MBPT) are the sequencing rules for selecting the part from the input buffer whereas for machine selection the dispatching rules are Minimum Number of parts in the Queue (MINQ), and Minimum queue with Minimum Waiting Time of all parts in the Queue (MQMWT). In this paper, the same manufacturing system is modeled under four different system load conditions. These load conditions are Full Balanced Load (FBL), Balanced Machine Load and Unbalanced Processing Time (BMLUPT), Unbalanced Machine Load and Balanced Processing Time (UMLBPT) and Unbalanced Load (UBL) with respect to machine load and processing time. The result of the simulation shows that there is continuous reduction in make-span with increase in routing flexibility when both machine load and processing times are unbalanced i.e., under UBL system condition.

As a result of rapid progress in technology and the shrinking product lifecycles faster than ever before, has led creeping realization of additional profits by performing the effective and efficient product recovery operations at a world-class level quintessential. Realization of these motives is complex due to the multidimensional relationships associated with the quality, variety, timeliness, demand changes, and logical processing of product returns and inherent complexity of recovery process. Therefore an Enterprise System (ES) perspective will give us a scope to develop a profit oriented recovery process as a flexible system that can handle products with various options and greater return volume and structural variability. This paper proposes generic model to enterprises engaged in or to be engaged in product recovery processes. A semi or partially flexible decision process model that facilitates flexible decision and information sharing (DIS) functions in product returns. This DIS model leads us to conceptualize the evolution of information associated with a product returns and how it might be encapsulated by Reverse Enterprise System (RES) to improve its profit and system performance.

The most favorable reverse manufacturing alternative arriving to collection centers has always been a key strategic consideration of any product recovery system. The nature of these decisions usually is considered to be multidimensional, interdisciplinary, complex, and unstructured due to lack of certainty in environment and information regarding time, quantity and quality of returns, etc. Fuzzy decision methodology provides an alternative framework to handle these reverse logistics system (RLS) complexities and to determine the decision strategies for best alternative selection for reprocessing. Designing a decision-making model for the same requires quantitative and qualitative evaluation based on criteria such as cost/time, legislative factors, environmental impact, quality, market, etc. Performance must be considered on the basis of these criteria to determine a suitable reverse manufacturing option depending on the expert opinion in this domain. In this paper, we propose a multiple criteria decision-making (MCDM) model based on fuzzy-set theory. The proposed model can help in designing effective and efficient flexible return policy depending on the various criteria. Further, companies can use this analysis as a strategic decision-making tool to develop fresh reprocessing facilities or efficiently use the already exiting facility. Finally, an example has been illustrated to highlight the procedural implementation of the proposed model. Further, this paper also makes an attempt to bring fuzzy-based flexible MCDM and reverse logistics together as a well-suited group decision support tool for alternative selections.

This article is aimed at providing a broad review of the main approaches to information related issues in a dynamic supply chain, through the description of the main characteristics, techniques, ongoing developments and research activities. The literature is examined from the perspective of the existing information systems that support supply chain dynamics at operational and strategic levels, with its inherent focus on web-enabled collaboration among supply chain partners. Various issues related to integration of core processes across organisational boundaries through improved communication, partnerships, alliances and cooperation in vague and uncertain conditions are also addressed. We review the recent trends in supply chain management and analyse how diverse modelling techniques such as agent technology and Petri net can be applied to support dynamic supply chain configuration. The purpose of this article is to consolidate the existing research efforts concerning the issues related to information systems in dynamic supply chains, and to identify promising emerging issues for further study in this area.

In the global market conditions, the integrated Flexible Systems (FS) with Total Quality Management (TQM) and Supply Chain Management (SCM) is a growing field of interest for many a researchers and practitioners from the last decades. In the present paper, the ongoing integration process of aforesaid philosophies in flexible system, total quality management and supply chain has been highlighted through literature review. Many specific empirical studies have been carried out, and categories such as types of Flexibility and Total Quality Management have been studied to a great depth in the past. This paper focuses more on relatively unexplored categories, as they offer potential for further exploration and research. Classification developed to show supply chains and Flexible System in TQM context from a wider perspective. Based on problem context as classification and scope for future practice and research, an evolutionary timeline has been prepared taking into account all the relevant and seminal papers published in the area of Flexible system, TQM and SCM. The definition and scope of FS in the literature from Total Quality Management to Supply Chain context classification has been undertaken. The focus on TQM, Flexible Systems and Supply Chains aspects has been done in order to facilitate further study and research.

The Internet, with its unprecedented growth, is a promising platform to exchange information along the business channels. The purpose of this study was to determine the factors that are critical to organizations in their adoption decision of Information Systems (IS). Security assurance across flexible supply chain network is a critical factor for international business managers and in the evolution of international trade generally

A Flexible Manufacturing System (FMS) is designed to combine high productivity and production flexibility. But the controlling of an FMS requires high skills. Furthermore, the decisions at this stage have to be made very carefully in order to ensure that the manufacturing system will successfully satisfy the demands of an ever-changing market. Discrete-event simulation has been widely used to control such complex system. On the other hand, simulation is more and more used for decision making and evaluate control strategies. In this paper, we propose a logic simulation model which corresponds to the modeling of the computer control system. This paper also describes the various control strategies in an FMS. In the last some of the useful simulation techniques along with some simulation packages have been addressed at the end of the paper and these techniques/software will be useful for the modeling and decision making of FMS.

The uncertainty of demand, lead times and other shop floor data in manufacturing systems, presence of manufacturing wastes and lack of systematic approach for solving customer specific problems are the main concerns of competitive manufacturing. Uncertainty caused by variation in demand and lead times together with lack of accurate data on bill of materials and inventory status leads to inefficient materials planning, master production schedule and capacity planning. Manufacturing efficiency is adversely affected by the presence of manufacturing wastes. Lastly process variability adversely affects competitive manufacturing. Enterprise Resource Planning (ERP), Lean Manufacturing and Six Sigma are the three different strategies to address the above mentioned concerns for competitive manufacturing. ERP focuses on data and schedule accuracy and ensures repeatable and predictable processes. Lean manufacturing focuses on identification and elimination/ minimizing of all kinds of manufacturing wastes. Six sigma is a process improvement initiative used to eliminate defects from processes and focuses on reducing process variability. These three strategies are mostly being used by organizations as stand alone initiatives. The paper provides a brief description of ERP, lean manufacturing and six sigma strategies. Benefits of using these strategies in synergy have been discussed. The past and present perspectives of lean manufacturing together with a holistic approach combining ERP, lean and six sigma strategies has been presented. Research gaps and areas of future research have been identified.

The need for holistic modeling efforts for returns that capture the extended closed loop supply chain (CLSC) system at strategic as well as operational level has been clearly recognized by the industry and academia. Strategic decision-makers need comprehensive models that can guide them in efficient decision-making to increase the profitability of the entire forward and return chain. Therefore, determination of a near optimal design configuration, which includes the environmental, economical and technological capability factors, is important in strategic decision-making effort that affect the profitability of the closed loop supply chain. In this paper, we adopted an improved system dynamics methodology to tackle strategic issues that affect various performance measures, like market, time/cost, environment etc., for closed loop supply chains. After studying real life implementation issues in CLSC design, we presented guidelines for the PBM (Participative Business Modeling) methodology and presented its extension for the strategic dynamic system modeling of return chains. Finally, we demonstrated the measurement of operational performance by extending SD (system dynamic) application to closed loop supply chain management.

This research is motivated by the co-operative production process of networked manufacturing systems (NMS). Manufacturing resource sharing and flexible production scheduling are two features of NMS. For an individual manufacturing system in an NMS, 'flexible production scheduling' means that it can produce multiple product-types and the switching of products is quick enough to respond to the demand fluctuation. 'Manufacturing resource sharing' means the utilisation of extra production capacity from other manufacturing systems in the NMS. Of course, that will bring extra cost. This paper focuses on the optimal production control problem of such a situation: one manufacturing system, multiple product-types, and uncertain demands. Here, it is assumed that there are two demand-levels for each product-type: the lower one and the higher one. The total normal production capacity is larger than the total lower demands and smaller than the total higher demands. If the total demands cannot be satisfied and the work-in-process (WIP) of all product-types decrease to a certain level, e.g. zero WIP, the extra production capacity may be utilised. For such a system, a new two-level hedging point policy is proposed, in which two hedging points (a higher one and a lower one) are given for each product-type. Different from the prioritised hedging point (PHP) policy which is usually applied to one-machine and multiple part-type systems, our control policy considers all part-types at the same prioritised level and keeps the work-in-process states of all product-types on a straight line in the state space. Thus, the total costs for WIP inventory and the occupation of extra capacity can be obtained in a closed form, which is a function with respect to the hedging points. Then the method for optimising the hedging points is proposed and the special structure of the optimal hedging point is obtained. Numerical experiments verify the optimality and the special structure of the hedging point obtained by our method.

Effective Supply Chain Management (SCM) is a pivotal issue in the dynamically changing business environment. Dynamic SCM requires integrated decision-making amongst autonomous chain partners with effective decision information synchronization amongst them. By exploiting flexibility in supply chain structures, better performance can be achieved. Similarly, by judiciously employing decision flexibility and the associated dynamic control amongst autonomous supply chain nodes, many improvements are possible. The paper presents a study on the role of different flexibility options (i.e. no flexibility, partial flexibility and full flexibility) in a dynamic supply chain model based on some key parameters and performance measures. Supply chain flexibility has significant potential and needs a greater research attention. The paper attempts to advance the knowledge of dynamic control on effective flexibility exploitation in the context of dynamic supply chains. A simulation model of a dynamic supply chain is used for this purpose. The key results are highlighted along with industry implications. Here each supply chain node involves decision-making. Based on the order and sample information available from the immediate buyers or customers, the supplier selection decisions are dynamically made. A seemingly good decision at a stage based on local information often ends up as detrimental not only to the total chain cost, but also to the total costs of the node itself. These observations are important for the designers and managers of the flexible supply chain systems to arrive at appropriate types and a judicious level of flexibility to attain significant improvements in total cost reduction. The modelling of dynamic supply chains with a focus on flexibility can offer enormous potential to the industry. This paper addresses this interesting and challenging domain.

In a supply chain, the suppliers' flexibility is considered as a tool to cope with the environmental uncertainties. Flexible suppliers are capable of supplying/processing other jobs in addition to the one for which they are the original supplier. In a cluster of flexible suppliers, it is expected that flexibility of suppliers be utilized more expressively through better control of the supply chain. The online real-time information system is considered as one of the tools for effective control of the supply chain. However, in a cluster of flexible suppliers, the physical and operating characteristics of alternative suppliers (available by virtue of suppliers' flexibility) may vary from each other for doing the same operation, which may result in a different lead-time to process the same job. This paper presents a simulation study on suppliers' flexibility level (SFL) in relation to information system automation level of the supply chain and physical characteristics of the flexible suppliers. This paper contributes an approach for decision-makers to identify the information system automation level and variations in physical characteristics of the alternative suppliers with respect to suppliers' flexibility level that can help in improving the overall delivery lead-time. A decision-maker can reach a trade-off between benefits obtained from supplier's flexibility and the investment required to have a suitable information system automation level and physical characteristics of suppliers. Results of simulation study show that if automation level of information system is not matching to the suppliers' flexibility level then supplier's flexibility may become counterproductive. Further, a relative change in physical characteristics of the alternative flexible suppliers as compared to the original supplier is more than a certain limit then suppliers' flexibility may again be counterproductive. However, a cost intensive real-time information system may not be needed when suppliers' flexibility levels are lower. Study identifies the productive and counterproductive performance regions for suppliers' delivery lead-time.

Globally, automotive manufacturers are faced with the challenges of expanding product variety and high customer service provision. They increasingly face a flexibility driven, time-based competition. This demands effective knowledge management to develop more effective processes. In other words, there is a need for knowledge-driven innovations that can offer business process improvements in the new business environment. One innovative response to these challenges is postponement, also known as “delayed differentiation”. Such a strategy calls for redesigning products and re-engineering processes so that the stages of the production process in which a common process is used are prolonged. This product/process re-engineering should allow a flexible option to defer the point of differentiation. The manufacturing industry motivation to focus in this direction can be encouraged through relevant demonstrative models capturing their existing processes. In this paper, we develop such a model that captures the costs and benefits associated with postponement strategy that is implemented through business process re-engineering. We apply this model to analyze a real case in an automobile manufacturing plant. Our findings are that there are enormous opportunities for cost-effective postponement strategies in automotive manufacturing sector through a knowledge management (KM)-based process re-engineering. We also note that as the number of differentiated models and variability in demand increase, the cost of process re-engineering can be recovered through value of postponement (VOP), in as short a time frame as 1 year. It is also suggested that for practitioners and researchers working in the KM context, the sharing of the tacit knowledge amongst different professionals managing the various processes must be continually emphasized. This is increasingly important to deal with flexibility challenges.