Search results

1 – 10 of over 13000
Article
Publication date: 27 December 2022

Satya Prakash and Indrajit Mukherjee

This study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one…

Abstract

Purpose

This study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).

Design/methodology/approach

A five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.

Findings

The simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.

Research limitations/implications

A two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.

Originality/value

The proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.

Details

Journal of Modelling in Management, vol. 18 no. 6
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 2 August 2013

Pritee Ray and Mamata Jenamani

The paper proposes multi‐sourcing models for optimal order allocation in newsvendor setting under supply disruption with stochastic demand where suppliers are capacity constrained.

2077

Abstract

Purpose

The paper proposes multi‐sourcing models for optimal order allocation in newsvendor setting under supply disruption with stochastic demand where suppliers are capacity constrained.

Design/methodology/approach

Mathematical models are constructed to describe the stochastic single period two echelon supply chain. We first investigate the uncapacitated suppliers’ problem. Then capacity constraint is included in the model to study the effect on sourcing decision. A numerical example and its solution are included to illustrate the solution procedure. We find the solution using traditional optimization approach, genetic algorithm and simulation optimization approach.

Findings

The models capture the impact of disruption risk on optimal sourcing decision. When demand is highly uncertain the order should be place with the lowest cost suppliers in case of uncapacitated problem; whereas, it is to be appropriately split among a set of low‐cost suppliers in case of capacitated problem. Simulation optimization found to be the best solution approach for such problem.

Research implications/limitations

The model is applicable for a single period short‐life cycle product.

Originality/value

The models can be utilized for any number of suppliers. The numerical study illustrates the impact of probability of disruption, its consequences and cost of purchase on sourcing decisions.

Details

Journal of Advances in Management Research, vol. 10 no. 2
Type: Research Article
ISSN: 0972-7981

Keywords

Article
Publication date: 9 April 2018

Harpreet Kaur and Surya Prakash Singh

Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon…

Abstract

Purpose

Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon emissions, the manufacturing firms are enforced to regulate and reduce the emissions caused throughout the supply chain. It is observed that procurement and logistics activities in manufacturing firms contribute heavily toward carbon emissions. Moreover, highly dynamic and uncertain business environment with uncertainty in parameters such as demand, supplier and carrier capacity adds to the complexity in procurement planning. The paper aims to discuss these issues.

Design/methodology/approach

This paper is a novel attempt to model environmentally sustainable stochastic procurement (ESSP) problem as a mixed-integer non-linear program. The ESSP optimizes the procurement plan of the firm including lot-sizing, supplier and carrier selection by addressing uncertainty and environmental sustainability. The model applies chance-constrained-based approach to address the uncertain parameters.

Findings

The proposed ESSP model is solved optimally for 30 data sets to validate the proposed ESSP and is further demonstrated using three illustrations solved optimally in LINGO 10.

Originality/value

The ESSP model simultaneously minimizes total procurement cost and carbon emissions over the entire planning horizon considering uncertain demand, supplier and carrier capacity.

Details

Management of Environmental Quality: An International Journal, vol. 29 no. 3
Type: Research Article
ISSN: 1477-7835

Keywords

Article
Publication date: 22 June 2018

Remica Aggarwal

Green supply chain management and new product innovation and diffusion have become quite popular and act as a rich source of providing competitive advantage for companies to trade…

Abstract

Purpose

Green supply chain management and new product innovation and diffusion have become quite popular and act as a rich source of providing competitive advantage for companies to trade without further deteriorating environmental quality. However, research on low-carbon footprint supply chain configuration for a new product represents a comparably new trend and needs to be explored further. Using relatively simple models, the purpose of this paper is to demonstrate how carbon emissions concerns, such as carbon emission caps and carbon tax scheme, could be integrated into an operational decision, such as product procurement, production, storage and transportation concerning new fast-moving consumer goods (FMCG) product introduction.

Design/methodology/approach

The situation titled “low-carbon footprint supply chain configuration problems” is mathematically formulated as a multi-objective optimization problem under the dynamic and stochastic phenomenon concerning receiver’s demand requirements and production plant capacity constraints. Further, the effects of demand and capacities’ uncertainties are modeled using the chance constraint approach proposed by Charnes and Cooper (1959, 1963).

Findings

Various cases have been validated using the case example of a new FMCG product manufacturer. To validate the proposed models, data are generated randomly and solved using optimization software LINGO 10.0.

Originality/value

The attempt is novel in the context of considering the dynamic and stochastic phenomenon with respect to demand center’s requirements and manufacturing plant’s capacity constraints with regard to the low-carbon footprints supply chain configuration of a new FMCG product.

Details

Management of Environmental Quality: An International Journal, vol. 29 no. 6
Type: Research Article
ISSN: 1477-7835

Keywords

Article
Publication date: 25 November 2013

Nancy Chun Feng

The purpose of this paper is to examine the potential effect of busy season resource constraints on the selection of a new auditor, conditioned upon the status of the prior…

Abstract

Purpose

The purpose of this paper is to examine the potential effect of busy season resource constraints on the selection of a new auditor, conditioned upon the status of the prior auditor.

Design/methodology/approach

The paper employs multivariate logistic regressions for a sample of firms that changed auditors between 1979 and 2005 to explore the empirical correlations between having a December fiscal year-end (FYE) and non-lateral switches.

Findings

The paper finds that non-BigN clients with December FYEs are less likely to switch to BigN auditors than those with non-December FYEs prior to the enactment of the Sarbanes-Oxley Act (SOX). This trend subsides after SOX. For firms with BigN predecessor auditors, fiscal year-end appears to have insignificant influence on auditor switching.

Research limitations/implications

The findings suggest that upwardly mobile clients face greater audit supply constraints compared to clients already being audited by a BigN firm during the traditional busy season. However, the curbing influence on switching upwards erodes after SOX.

Practical implications

This study is to show the impact of supplier capacity constraints on audit production and structural changes within the auditing profession.

Originality/value

The findings can further the understanding of the determinants of auditor-client realignment, given that the paper identifies and explores the effects of having a December FYE on subsequent auditor appointments, conditioned upon the status of the prior auditor.

Details

Journal of Applied Accounting Research, vol. 14 no. 3
Type: Research Article
ISSN: 0967-5426

Keywords

Article
Publication date: 14 March 2008

Shin‐Chan Ting and Danny I. Cho

The paper seeks to provide academic researchers and practitioners with a better understanding about purchasing strategies through an integrated approach to supplier selection and…

9103

Abstract

Purpose

The paper seeks to provide academic researchers and practitioners with a better understanding about purchasing strategies through an integrated approach to supplier selection and purchasing decisions.

Design/methodology/approach

This paper views supplier selection as a multi‐criteria problem. Through the analytical hierarchy process (AHP), in consideration of both quantitative and qualitative criteria, a set of candidate suppliers is identified. A multi‐objective linear programming (MOLP) model, with multiple objectives and a set of system constraints, is then formulated and solved to allocate the optimum order quantities to the candidate suppliers.

Findings

The paper provides tradeoffs among different objectives, which are more consistent with the complexity and nature of the real‐world decision‐making environment. It also offers better information and solutions supporting effective purchasing decisions.

Research limitations/implications

The main concept of the proposed approach can be applicable to any organization with a purchasing function. However, its implementation will be very specific to a particular organization of interest, as each individual organization must define its own subjective criteria and constraints. The area of decision support system development, which automates (or computerizes) the input process of the proposed models and integrates with other databases in a company, will provide great opportunities for future research.

Practical implications

The paper provides practitioners with flexibility and effectiveness in their supplier selection and purchasing decision process and with a better understanding about their future purchasing strategies. The results from the application of the proposed models to the supplier selection problem at a high‐technology firm in Taiwan show that the models are effective and applicable.

Originality/value

This paper takes an integrated approach to problem analysis (i.e. multi‐objectives with both quantitative and qualitative information), uses a sound scientific methodology in model development (i.e. integrating AHP with MOLP), and provides practical use of the models. It offers additional knowledge and value to both academics and practitioners.

Details

Supply Chain Management: An International Journal, vol. 13 no. 2
Type: Research Article
ISSN: 1359-8546

Keywords

Article
Publication date: 14 January 2021

Fereshte Shabani-Naeeni and R. Ghasemy Yaghin

In the data-driven era, the quality of the data exchanged between suppliers and buyer can enhance the buyer’s ability to appropriately cope with the risks and uncertainties…

Abstract

Purpose

In the data-driven era, the quality of the data exchanged between suppliers and buyer can enhance the buyer’s ability to appropriately cope with the risks and uncertainties associated with raw material purchasing. This paper aims to address the issue of supplier selection and purchasing planning considering the quality of data by benefiting from suppliers’ synergistic effects.

Design/methodology/approach

An approach is proposed to measure data visibility’s total value using a multi-stage algorithm. A multi-objective mathematical optimization model is then developed to determine the optimal integrated purchasing plan in a multi-product setting under risk. The model contemplates three essential objective functions, i.e. maximizing total data quality and quantity level, minimizing purchasing risks and minimizing total costs.

Findings

With emerging competitive areas, in the presence of industry 4.0, internet of things and big data, high data quality can improve the process of supply chain decision-making. This paper supports the managers for the procurement planning of modern organizations under risk and thus provides an in-depth understanding for the enterprises having the readiness for industry 4.0 transformation.

Originality/value

Various data quality attributes are comprehensively subjected to deeper analysis. An applicable procedure is proposed to determine the total value of data quality and quantity required for supplier selection. Besides, a novel multi-objective optimization model is developed to determine the purchasing plan under risk.

Article
Publication date: 19 July 2019

Mina Mikhail, Mohammed El-Beheiry and Nahid Afia

The purpose of this paper is to develop a decision tool that enables supply chain (SC) architects to design resilient SC networks (SCNs). Two resilience design determinants are…

Abstract

Purpose

The purpose of this paper is to develop a decision tool that enables supply chain (SC) architects to design resilient SC networks (SCNs). Two resilience design determinants are considered: SC density and node criticality. The effect of considering these determinants on network structures is highlighted based on the ability to resist disruptions and how SC performance is affected.

Design/methodology/approach

A mixed-integer non-linear programming model is proposed as a proactive strategy to develop resilient structures; design determinants are formulated and considered as constraints. An upper limit is set for each determinant, and resistance capacity and performance of the developed structures are evaluated. These upper limits are then changed until SC performance stabilizes in case of no disruption.

Findings

Resilient SCN structures are achieved at relatively low design determinants levels on the expense of profit and without experiencing shortage in case of no disruption. This reduction in profit can be minimized on setting counter values for the two determinants; relatively higher SC density with lower node criticality or vice versa. At very low SC density levels, the design model will reduce the number of open facilities largely leading to only one facility open at each echelon; therefore, shortage occurs and vulnerability to disruption increases. On the other hand, at high determinants levels, SC vulnerability also increases as a result of having more geographically clustered structures with higher inbound and outbound flows for each facility.

Originality/value

In this paper, a novel proactive decision tool is adopted to design resilient SCNs. Previous literature used metrics for SC density and node criticality to assess resilience; in this research, determinants are incorporated directly as constraints in the design model. Results give insight to SC architects on how to set determinant values to reach resilient structures with minimum performance loss in case of no disruption.

Details

Journal of Modelling in Management, vol. 14 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 20 July 2015

Amol Singh

This paper aims to address the problem of optimal allocation of demand of items among candidate suppliers to maximize the purchase value of items. The purchase value of the items…

1642

Abstract

Purpose

This paper aims to address the problem of optimal allocation of demand of items among candidate suppliers to maximize the purchase value of items. The purchase value of the items directly relates to cost and quality of raw materials purchased from the supplier. In an increasingly competitive environment, firms are paying more attention to selecting the right suppliers for procurement of raw materials and component parts for their products. The present research work focuses on this issue of supply chain management.

Design/methodology/approach

This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

Findings

The supply chain network is witnessing a changing business environment due to government policies aimed at promoting new small manufacturing enterprises (small and medium-sized enterprises) for intermediate parts and components. Hence, the managers have an option to select a new group of suppliers and allocate the optimal multi-period demand among the new group of suppliers to maximize their purchase value. In this context, the proposed hybrid model would be beneficial for the managers to operate their supply chain effectively and efficiently. The present research work will be helpful for the managers who are interested to reconfigure their supply chain under the failure of any supply chain partner or in a changing business environment. The model provides flexibility to the managers for evaluation of the different available alternatives to take a decision of optimal demand allocation among the suppliers. The proposed hybrid (fuzzy, TOPSIS and MILP) model provides more objective information for supplier evaluation and demand allocation among suppliers in a supply chain. The managers can use the proposed model to the analysis of other management decision-making problems.

Originality/value

This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. This hybrid algorithm prioritizes the suppliers and then allocates the multi-period demand among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

Details

Journal of Modelling in Management, vol. 10 no. 2
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 28 January 2020

Mohammad Saeid Atabaki, Seyed Hamid Reza Pasandideh and Mohammad Mohammadi

Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the…

Abstract

Purpose

Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs.

Design/methodology/approach

The problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO.

Findings

Taking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible.

Originality/value

Developing a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.

Details

Journal of Modelling in Management, vol. 15 no. 4
Type: Research Article
ISSN: 1746-5664

Keywords

1 – 10 of over 13000