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The purpose of this paper is to develop a framework for the manufacturer of a make-to-order company to simultaneously negotiate with multiple customers through mediator to achieve order acceptance decisions (OADs).

The paper developed mathematical models for the manufacturer, as well as customers to revise their offers during negotiations. Moreover, the paper also proposed a method for the mediator to carry out his assigned duties to assist in negotiation. In the decision process, mediator acts as a bridge between the manufacturer and customers to reach an agreement. A numerical example is enumerated to illustrate the working mechanism and superiority of proposed framework as compared to the framework where simultaneous negotiations are carried out without the presence of mediator.

Iterative method of negotiation conducted without mediator leads to delay in reaching agreement as the aspiration level of manufacturer offer and counter-offer of customer will never cross each other. In addition, the party who submits the offer first may suffer as the opponent can take the advantage of his/her offer during negotiation, thereby, derailing the issue of fairness. Introducing mediator between the manufacturer and the customer for their negotiations could overcome these two issues. Numerical analysis clearly illustrates that, in average, the rounds of negotiation to reach an agreement can be reduced by 22 percent using proposed negotiation framework. In addition, the fairness in negotiations can be improved by 33 percent with the incorporation of mediator.

Through continuing research efforts in this domain, certain models and strategies have been developed for negotiation. Iterative method of negotiations without mediator will help neither the manufacturer nor the customer in terms of fairness and negotiations round to reach an agreement. To the best of the author’s knowledge, so far, this is the first instance of research work in the domain of OAD and negotiation framework that attempts to incorporate mediator for simultaneous negotiation between manufacturer and customers on multiple issues simultaneously.

The purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity (SCC).

Through extensive literature review, the authors discussed various drivers of SCC. These drivers were classified into five dimensions based on expert opinion. Moreover, a novel hybrid mathematical model was developed by integrating analytical hierarchy process (AHP) and grey relational analysis (GRA) methods to measure the level of SCC. A case study was conducted to demonstrate the applicability of the developed model and analyze the SCC level of the company in the study.

The authors identified 22 drivers of SCC, which were further clustered into five complexity dimensions. The application of the developed model to the company in the case study showed that the SCC level of the company was 0.44, signifying that there was a considerable scope of improvement in terms of minimizing complexity. The company that serves as the focus of this case study mainly needs improvement in tackling issues concerning government regulation, internal communication and information sharing and company culture.

In this paper, the authors propose a model by integrating AHP and GRA methods that can measure the SCC level based on various complexity drivers. The combination of such methods, considering their ability to convert the inheritance and interdependence of drivers into a single mathematical model, is preferred over other techniques. To the best of the authors' knowledge, this is the first attempt at developing a hybrid multicriteria decision-based model to quantify SCC.

This study aims to propose a method known as the fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) for complex project selection in organizations. To fulfill study objectives, the factors responsible for making a project complex are collected through literature review, which is then analyzed by fuzzy TOPSIS, based on three decision-makers’ opinions.

The selection of complex projects is a multi-criteria decision-making (MCDM) process for global organizations. Traditional procedures for selecting complex projects are not adequate due to the limitations of linguistic assessment. To crossover such limitation, this study proposes the fuzzy MCDM method to select complex projects in organizations.

A large-scale engine manufacturing company, engaged in the energy business, is studied to validate the suitability of the fuzzy TOPSIS method and rank eight projects of the case company based on project complexity. Out of these eight projects, the closeness coefficient of the most complex project is found to be 0.817 and that of the least complex project is found to be 0.274. Finally, study outcomes are concluded in the conclusion section, along with study limitations and future works.

The outcomes from this research may not be generalized sufficiently due to the subjectivity of the interviewers. The study outcomes support project managers to optimize their project selection processes, especially to select complex projects. The presented methodology can be used extensively used by the project planners/managers to find the driving factors related to project complexity.

The presented study deliberately explained how complex projects in an organization could be select efficiently. This selection methodology supports top management to maintain their proposed projects with optimum resource allocations and maximum productivity.

Modular product development is a turning table concept in terms of benefits and impact in manufacturing companies. It offers added benefits to the companies with respect to reduce lead-time, improve assemble ability and agility in supply chain management, promote product family and enhance customer satisfaction. This paper aims to identify the patterns of modular product development strategy by using measurement techniques.

Both quantitative and qualitative approaches are considered. In the qualitative section, relevant data on product design are collected from six case companies. On the other hand, in the quantitative section, collected data are analyzed to measure the product modularity level with the case companies.

This research identifies potential metrics which can be used successfully to measure product modularity level or index in manufacturing industries. Selection of such metrics also depends on individual company’s objectives to measure modularity index.

This research contributes to the development of modular product design that supports product family design with lean and agile (leagile) supply chain process. It also provided a parsimonious framework to mapping modules within a product, which is ultimately used to measure modularity index.

The purpose of this paper is to develop a mathematical model of a make-to-order manufacturing company simultaneously negotiating multiple contingent orders that possess conflicting issues in order to achieve order acceptance decisions (OADs).

The paper developed a mathematical model by incorporating probabilistic theory and some theories of negotiation in the OAD problem. The model helps to harness the relationship between the manufacturer and customers of contingent orders on conflicting issues. A numerical example is enumerated to illustrate the working mechanism and sensitivity of the model developed.

In the negotiation-based OAD system, if more than one customer is willing to negotiate on the offer of manufacturer, rather than engaging in one-to-one negotiation, the manufacturer has to negotiate with all the customers simultaneously to maximize the expected contribution and acceptance probability from all the orders. Also, the numerical example illustrates that, sometimes, rejecting an order/orders from the order set gives better results in terms of the expected contribution than continuing negotiations on them.

Through continuing research efforts in this domain, certain models and strategies have been developed for negotiation on a one-to-one basis (i.e. negotiation by the manufacture with only one customer at a time). One-to-one negotiation will neither help companies to streamline their production systems nor will it maximize the expected contribution. To the best of the author’s knowledge, so far, this is the first instance of research work in the domain of a joint OAD and negotiation framework that attempts to develop a simultaneous negotiation method for arriving at OADs.

As we move up in the supply chain (SC) from retailer to supplier, amplification in the fluctuation of order increased. To minimize this amplification, understanding of key decision variables which affects the SC is essential. So, in the present work the authors developed a novel approach to examine the structural dependencies among variables responsible for perfect order fulfillment (POF).

Interpretive structural modeling approach has been used to model the structural relationship among the key SC variables. Further, to study the driver-dependence dynamics among variables MICMAC analysis has been used. In the second phase, the influence of driver variables on the POF is investigated by using fuzzy logic.

From the results, it is observed that the variables’ delivery time, number of echelons, data accuracy and information sharing have high driving power which may help the organizations to meet challenges offered by POF. The results showed that for POF is said to be at optimum level when the number of echelons should be low and data accuracy should be high, and information sharing among all partners should also be very high.

Research on SC is classified into three categories, i.e. operational, design and strategic. In the present study authors discussed strategic variables responsible for POF which is the main limitation of the study. The work can be extended by including operational and design variables.

POF in SC network is affected by various variables. The in-depth understanding of contextual association among the variables helps the managers to improve the efficiency of the SC and reduce the bullwhip effect across the downstream SC network.

The study presents a hybrid approach to analyze the key POF dimensions, i.e. forecasting, number of echelons, information sharing, cycle time and delivery time, critical to POF in downstream SC network by developing various case settings.