Kanchan Das

Integrating effective flexibility measures into a strategic supply chain planning model

This paper develops models for capacity, product mix, distribution and input supply flexibility and integrates them in a strategic level, mixed integer supply chain (SC) planning model as a way of addressing demand and supply uncertainty, as well as improving market responsiveness. Capacity flexibility is modeled via the SCs production capacity planning to address budgeted demand and ensure the fulfillment of prospective demand increases when considering various market scenarios. This model selects an optimal number of products from fast moving and extended product range options--based on the product mix flexibility. The model confirms a quick response to a changing marketplace by considering elements like transportation and supply lead time along with the probabilities of stock out options when addressing input supply and distribution flexibility. This paper proposes a solution procedure to solve the model for real world problems, and investigates the sensitivity of the model outputs with respect to changes in flexibility measures.

Optimization of operation and changeover time for production planning and scheduling in a flexible manufacturing system

This paper deals with the production planning problem of a flexible manufacturing system. It specifically addresses issues of machine loading, tool allocation, and part type grouping with the intent of developing an operation sequencing technique capable of optimizing operation time, non-productive tool change times, and orientation change times when processing a groups design features. A hierarchical approach has been adopted to determine the part groups - depending on the operation, tool change and orientation change times at the upper level. At the next level, we sequence the operations of the part groups. Integer programming models are formulated to group the parts and to address the operation-sequencing problem. The model is illustrated with an example related to an auto engine cylinder head machining plant.

A new method for bottleneck detection

This paper presents a new method to identify and rank the bottlenecks in a manufacturing system. The proposed method is based on performance related data that are easy to capture, offers low computational burden and less prone to be in error due to its simplicity. The proposed method analyzes inter-departure time from different machines to identify and rank the bottlenecks. In a follow-up paper the authors plan to present a method to allocate loss of production to different machines based on analysis of inter-departure time. This paper also proposes a set of rules that may be used to improve data integrity. The proposed method may be used to analyze both steady state as well as non-steady state data and can be extended easily to analysis of a job shop.

A quality integrated strategic level global supply chain model

This paper proposes a strategic global supply chain (GSC) model that integrates critical-to-quality (CTQ) issues along with other system-based quality requirements that allow a GSC to assure product quality and safety to the primary user, preventing expensive product recall. The proposed model ensures product quality and other critical-to-business (CTB) requirements for GSCs that operate in various, multi-layered, multi-location-based suppliers and manufacturing plants by first integrating a quality-based supply management (QSM) system to GSC operations. The QSM evaluates and affiliates prospective supplier plants to supply inputs based on their performance in CTQ and other crucial quality system-based attributes. The model then integrates a CTQ-based approval process for GSC-operated manufacturing facilities that also considers CTB and standard quality management system factors. Finally, the model provides a procedure for introducing a quality monitoring process for the continuous improvement of supplier plants, as well as GSC-operated plants in its overall strategic decision making process. A numerical example illustrates the models application among suppliers, manufacturing systems and distribution systems in a global business space.

Integrating reverse logistics into the strategic planning of a supply chain

This paper proposes a mixed-integer programming (MIP) model for the strategic production and distribution planning of a supply chain (SC) integrating reverse logistics system. Such reverse logistics planning addresses the collection, recovery and marketing of recovered products, in addition to returned components and packing/wrapping materials. The model includes an approach that uses retail outlets as a two-way channel for marketing new products, collecting used/returned products and remarketing recovered products as a way of promoting an effective product recovery system in SC operation and optimising costs. The recovery of products/components is planned through a pool of recovery service providers (RSPs), so that maximum recovery can be ensured through combining the expertise of RSPs within optimum costs. The model follows a two-step process that addresses strategic decisions about product recovery in the first step, and the integration of the recovery process into overall SC decisions in the final step. A numerical example illustrates the applicability of the model. A sensitivity analysis has been conducted to show the effects that changes in the recovered product quantity have on the overall SC performance.

Consideration of dynamic changes in machine reliability and part demand: a cellular manufacturing systems design model

In a dynamic manufacturing and marketing environment, machine reliability is often subject to issues of usage and age, with areas like part demand experiencing frequent changes as well. It is vital, therefore, for a manufacturing cell design to consider and plan for such changes so that said design will continue to meet expectations in future applications. This study proposes a multi-objective, integer-programming (IP) model for designing a cellular manufacturing system (CMS) that will remain optimal for the entire multi-period planning horizon by considering dynamic changes in machine reliability and part demand over the periods. This model will allow for alternative part processing routes and select suitable machines along those routes – maximising machine system reliability and minimising system costs. This model also accounts for the purchase of new machine capacity when needed in an effort to design an optimal cell that remains suitable for the entire planning horizon. This study illustrates an -constraint solution procedure that will facilitate the user when selecting suitable solutions based on the importance they impart to the objectives.

Modelling supply chain network: a quality‐oriented approach

Purpose – Successful supply chain (SC) design outlines a process for selecting compatible and competent partners and details their interrelationship at different nodes located in different echelons of the order fulfilment process. This paper aims to propose a quality‐oriented approach to such design that encourages all partners to grow in the business process.Design/methodology/approach – The design approach features a central node, formed by competent business process operators or lead firms to coordinate SC functions between customers and suppliers. This co‐ordination includes functions like monitoring quality, quantity and cost figures on the supply‐partner side, while overseeing the order fulfilment process on the receiving‐partner‐retailer side. The result is ensured dispatching, distribution and delivery of the right product at the right time. A mathematical model is proposed to aid the co‐ordination and decision‐making process and numerical examples illustrate the applicability of the proposed mode...

Risk readiness and resiliency planning for a supply chain

This paper proposes supply chain (SC) risk readiness and resiliency measures and formulates a model for planning and controlling select internal business factors to create desired risk resiliency in order to avert potential risks and mitigate their after-effect. SCs may be exposed to events that affect their business operations, and primarily impact the production processes (i.e. production-related risks), or events (such as natural calamities or terrorism) that affect the way the business interacts with the market, and primarily impact the transportation and distribution processes (i.e. market-related risks). Although a business cannot control such disasters as natural calamities or terrorism, it is possible to identify and control the factors that are responsible for production-related risks and that influence several market-related risks or disasters. The proposed model and the measures will guide SCs through the process of identification, planning and controlling the internal factors that make the chain resilient to these various risks. The resiliency measures and the mixed integer programming model will also enable SCs to conduct what-if analyses of cost and performance trade-off options. A numerical example illustrates the planning in typical scenarios.

A Quality and Partnering-Based Model for Improving Supply Chain Performance

This study proposes a Supplier Quality Affiliation (SQA) approach that is integrated into a mixed integer programming Strategic Supply Chain Management (SSCM) model for overall improvement of the supply chain business process. A pool of acceptable quality and high quality suppliers are affiliated using multi-dimensional quality attributes for the supplier operation parameters in the SQA model. Based on the pre-defined partnering attributes, the SQA model next identifies a select group of high quality suppliers that can be converted into partners. The outcome of the SQA model is then integrated into the SSCM model for ensuring input quality while providing several options for overall business gains of the supply chain members, which include suppliers, manufacturers, and retailers. Applicability of the SQA model is investigated using a real world case study and the SSCM model is illustrated with a numerical example using random data.

Integrating resilience in a supply chain planning model

Purpose The purpose of this paper is to create a resilient supply chain (SC) plan to contain disruptions and risks in the overall operations of a business. Design/methodology/approach The study integrates resilience considerations in a business planning model that formulates resilience performance (RP) of SC functions in terms of flexibility, reliability, and similar system factors. It evaluates the RP of SC plans and determines their vulnerability considering required and planned resources. The model estimates the possible effects of disasters on vulnerable functions using a scenario-based analysis and plans containment options. It also includes decision options for deploying resources to achieve the expected levels of resilience by preventing potential vulnerabilities. The model takes optimum decision in a what-if approach by comparing performance of the existing business plan, with options for containing the vulnerabilities inherent in not considering potential risks when planning to fulfill market demand, and the performance of a resilient plan that includes decision options to prevent vulnerabilities where possible and mitigate them otherwise. Findings It is possible, for example, to evaluate RP of SC plans, identify vulnerable functions, and decide optimum option to create resilient business system. Research limitations/implications The present study takes a generic approach and creates bases to explore its application in any industry-based case. Originality/value The research introduces formulations for RPs and vulnerability indices that can be included in a planning model to create a resilient SC.