A. R. Singh

Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection

A company must purchase a lot of diverse components and raw material from different upstream suppliers to manufacture or assemble its products. Under this situation the supplier selection has become a critical issue for the purchasing department.The selection of suppliers depends on number of criteria and the challenge is to optimize selection process based on critical criteria and select the best supplier(s). During supplier selection process initial screening of potential suppliers from a large set is vital and the determination of prospective supplier is largely dependent on the criteria chosen of such pre-qualification. In the literature, many judgments based methods are proposed and derived criteria selection from the opinion of either the customers or the experts. All these techniques use the knowledge and experience of the decision makers. These methods inherit certain degree of uncertainty due to complex supply chain structure. The extraction of hidden knowledge is one of the most important tools to address such uncertainty and data mining is one such concept to account for such uncertainty and it has been found applicable in many scenarios. The proposed research aims to introduce a data mining approach, to discover the hidden relationships among the supplier’s pre-qualification data with the overall supplier rating that have been derived after observation of previously executed work for a period of time. It provides an overview that how supplier’s initial strength influence its final work performance.

Robust strategies for mitigating operational and disruption risks: a fuzzy AHP approach

Some supply chains continue to function smoothly and take short time to recover after a major disruption because they possessed ‘robust’ strategies for mitigating operational and disruption risks. In this paper, some of the strategies are proposed that make the supply chain more efficient and resilient. Linguistic and subjective evaluations take place in questionnaire form to find out the best strategy for mitigating risks in Indian manufacturing industries. Each linguistic variable has its own numerical value in predefined scale. In classical analytical hierarchy process (AHP), these numerical values are exact numbers whereas in fuzzy AHP method they are intervals between two numbers with most likely value. Therefore, fuzzy AHP approach is used because linguistic value can change person to person. In these circumstances, taking the fuzziness in to account will provide results that are not prone to risks. Our finding highlights the ranking of the different strategies to mitigate supply chain risks on the basis of weight and survey data.

Kano integrated robust design approach for aesthetical product design: a case study of a car profile

Visual shape parameters and aesthetic aspects of a product are one of the crucial factors for the success of a product in the market. The type and the value of the shape parameters plays an important role in visual appearance of a product and designers tends to be critical while deciding these parameters. The aesthetic aspect of a product has been matter of concern for researchers with its electromechanical design. The Kano model has been found to be a useful tool to establish the relationship between performance criteria and customer satisfaction. To achieve the desired customer satisfaction weight of each product criteria is determined by using Kano model. This study presents an integrative design approach combining the Kano model, Taguchi method and grey relation analysis to obtain the optimal combination of shape parameters and aesthetic aspects. Prioritized criteria of aesthetic attributes have been abstracted through proposed methodology. A case study has been presented to evolve a profile of a car.

Exploring omnichannel and network design in omni environment

Abstract Modern day supply chain has gone through many changes in order to satisfy the ever increasing needs of customers. One of such change is origin of omnichannel in supply chain which believes in not only satisfying customer’s needs but also delighting them. Implementation of omnichannel in practice comes with lots of challenges. These challenges are summarized as major research areas in omnichannel and being explored in this paper through literature. Further, distribution system in omnichannel demands flexibility in distribution which results in restructuring and reconfiguring the practices of traditional supply chain. For this purpose, a mathematical model has been developed under omni environment which provides the facility of flexible distribution for customers. This proposed model is bi-objective in nature which considers sustainability along with minimization of total supply chain cost. To check the feasibility of the proposed model, numerical illustrations have been carried out for two different scenarios. In first scenario, the proposed model is solved for single product and single time period while in second scenario similar conditions are considered for conventional Supply Chain Network (SCN) and is being compared with proposed model. The proposed MILP model is coded in GAMS and solved using CPLEX solver. The results obtained indicate that proposed model possess superiority over conventional SCN in many aspects. The findings from the proposed model ensure that customers are free to avail product and services from their desired channel as per their choice.

Neural network based predictive control for nonlinear chemical process

The paper presents a neural network based predictive control (NPC) strategy to control nonlinear chemical process or system. Multilayer perceptron neural network (MLP) is chosen to represent a Nonlinear autoregressive with exogenous signal (NARX) model of a nonlinear process. Based on the identified neural model, a generalized predictive control (GPC) algorithm is implemented to control the composition in a continuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. Also an Instantaneous linearization based predictive control (IPC) strategy is discussed, in which an approximated linear model is extracted from nonlinear neural network by instantaneous linearization around operating points. The tracking performance of the NPC and IPC is tested using different amplitude step function as a reference signal on CSTR application and it is shown using simulation results, that the NPC strategy is more effective and robust than the IPC strategy.

Integrated methodology for supplier selection in supply chain management

Selection of potential supplier is a strategic decision taken by purchasing department which helps in ensuring a long term commitment between supplier and industry. Selection of supplier is a multi criteria decision making (MCDM) problem involving quantitative, qualitative, tangible and non tangible diverse criterias. In this work a strategic frame work has been proposed which allows decision makers to consider more criterias for supplier selection. In this paper an integrated method of modified Kano model and Weighted Aggregated Sum Product Assessment (WASPAS) method has been proposed. Kano model has been applied for criteria classification and importance weight to criterias has been assigned by modified Kano model and WASPAS method has been applied for assigning ranks to suppliers A numerical example has been taken up to demonstrate the proposed methodology.