Total Supply Chain Cost Research Articles

Uncertain supply chain network design considering carbon footprint and social factors using two-stage approach

Sustainable development has become one of the leading global issues over the period of time. Currently, implementation of sustainability in supply chain has been continuously in center of attention due to introducing stringent legislations regarding environmental pollution by various governments and increasing stakeholders’ concerns toward social injustice. Unfortunately, literature is still scarce on studies considering all three dimensions (economical, environmental and social) of sustainability for the supply chain. An effective supply chain network design (SCND) is very important to implement sustainability in supply chain. This study proposes an uncertain SCND model that minimizes the total supply chain-oriented cost and determines the opening of plants, warehouses and flow of materials across the supply chain network by considering various carbon emissions and social factors. In this study, a new AHP and fuzzy TOPSIS-based methodology is proposed to transform qualitative social factors into quantitative social index, which is subsequently used in chance-constrained SCND model with an aim at reducing negative social impact. Further, the carbon emission of supply chain is estimated by considering a composite emission that consists of raw material, production, transportation and handling emissions. In the model, a carbon emission cap is imposed on total supply chain to reduce the carbon footprint of supply chain. To solve the proposed model, a code is developed in AMPL software using a nonlinear solver SNOPT. The applicability of the proposed model is illustrated with a numerical example. The sensitivity analysis examines the effects of reducing carbon footprint cap, negative social impacts and varying probability on the total cost of the supply chain. It is observed that a stricter carbon cap over supply chain network leads to opening of more plants across the supply chain. In addition, carbon footprint of supply chain is found to be decreased in certain extent with the reduction in negative social impacts from suppliers. The carbon footprint of the supply chain is found to be reduced with increasing certainty of material supply from the suppliers. The total supply chain cost is observed to be augmented with increasing probability.

Development of coordination system model on single-supplier multi-buyer for multi-item supply chain with probabilistic demand

Nowadays, the level of competition between supply chains is getting tighter and a good coordination system between supply chains members is very crucial in solving the issue. This paper focused on a model development of coordination system between single supplier and buyers in a supply chain as a solution. Proposed optimization model was designed to determine the optimal number of deliveries from a supplier to buyers in order to minimize the total cost over a planning horizon. Components of the total supply chain cost consist of transportation costs, handling costs of supplier and buyers and also stock out costs. In the proposed optimization model, the supplier can supply various types of items to retailers whose item demand patterns are probabilistic. Sensitivity analysis of the proposed model was conducted to test the effect of changes in transport costs, handling costs and production capacities of the supplier. The results of the sensitivity analysis showed a significant influence on the changes in the transportation cost, handling costs and production capacity to the decisions of the optimal numbers of product delivery for each item to the buyers.

Development of coordination system model on single-supplier multi-buyer for multi-item supply chain with probabilistic demand

Nowadays, the level of competition between supply chains is getting tighter and a good coordination system between supply chains members is very crucial in solving the issue. This paper focused on a model development of coordination system between single supplier and buyers in a supply chain as a solution. Proposed optimization model was designed to determine the optimal number of deliveries from a supplier to buyers in order to minimize the total cost over a planning horizon. Components of the total supply chain cost consist of transportation costs, handling costs of supplier and buyers and also stock out costs. In the proposed optimization model, the supplier can supply various types of items to retailers whose item demand patterns are probabilistic. Sensitivity analysis of the proposed model was conducted to test the effect of changes in transport costs, handling costs and production capacities of the supplier. The results of the sensitivity analysis showed a significant influence on the changes in the transportation cost, handling costs and production capacity to the decisions of the optimal numbers of product delivery for each item to the buyers.

Modeling the impact of competing utilization paths on biomass-to-liquid (BtL) supply chains

Second generation biofuels offer the opportunity to mitigate emissions from the growing transportation sector while respecting the scarcity of arable land in agriculture. Biomass-to-liquid (BtL) concepts based on large-scale gasification are capable of using low-quality residual feedstock, such as wheat straw or forest residues, for the production of transportation fuels. However, large amounts of biomass feedstock are required to achieve the economic capacity of a synthesis plant. Depending on the steepness of the terrain and the role of feedstock owners, biomass potentials can only be utilized to a large extent at increasing costs per ton. Such diseconomies of scale are particularly problematic in the presence of already established value chains consuming the easily accessible and low-cost feedstock. Asa result, second-generation biofuel supply chains face steep supply curves with sharply increasing unit costs. This article investigates the impact of established utilization paths on a large-scale biofuel production value chain. To do so, a mixed-integer linear model is presented which first determines the allocation of biomass resources to CHP plants and domestic consumers. Based on the resulting costs and supply curves, the model then determines the optimum configuration of the synfuel supply chain including locations and capacities of conversion plants, feedstock procurement and transportation. The model is applied to a case study covering six regions in south-central Chile. The total supply chain cost for the production of synthetic gasoline is estimated to amount to 0.8–0.9€ per liter. Feedstock costs of the synfuel supply chain are 20–50% higher in comparison to the price paid by CHP plants and households. The results indicate that both torrefaction and fast pyrolysis can be applied beneficially to utilize remote biomass resources which are less in demand by established consumers.

Multi-stage manufacturing/re-manufacturing facility location and allocation model under uncertain demand and return

Manufacturing/re-manufacturing facility location and allocation systems have attracted many researchers and manufacturers because of globalisation, increasing competition, technological advancement and reduced product life cycle. In this paper, a multi-stage, multi-product capacitated closed-loop supply chain (CLSC) network is proposed with an objective to optimise the total supply chain cost. The network also includes reuse, reprocessing and disposal of parts. Customer demands and returns are fluctuating, thereby causing an uncertain environment. To handle the uncertain parameters, a scenario-based mixed integer linear programming was developed and implemented in a cartridge manufacturing industry. Scenarios were framed by changing the demand, return rate, reprocessing rate, reassembling rate and disposal rate. Totally, 125 scenarios were analysed and effect on the percentage change in total cost was studied. The model is solved using CPLEX, an optimisation tool. Computational properties and complexities in solving the problems are studied. Some insights are provided for the model based on different scenarios.

A single-vendor single-manufacturer integrated inventory model with stochastic demand and variable production rate

This paper investigates an alternative way to react to demand uncertainty in an integrated inventory model, namely the variation of the production rate that enables the manufacturer to reduce lead times and the corresponding demand uncertainty. To investigate the impact of variable production rates on the supply chain, this paper considers a single-vendor single-manufacturer integrated inventory model where the vendor ships finished products in multiples of full truckloads to the manufacturer. The objective of the model is to coordinate both production and distribution of the product in such a way that the total costs of the supply chain are minimized. A solution procedure is suggested, and the behaviour of the model is analysed in numerical examples. Obviously, the total supply chain cost is reduced when the manufacturer's production rate is included as a decision variable in the model. These savings can generally benefit both the vendor and the manufacturer. However, in situations where coordinated decision making is initially not beneficial to the vendor, the supply chain members can benefit from a revenue sharing contract that supports the sharing of the total savings. The model proposed in the paper at hand supports both the determination of an optimal production rate as well as the distribution of coordination benefits among the supply chain members.

Short‐term crude oil scheduling with preventive maintenance operations: a fuzzy stochastic programming approach

AbstractThis paper presents a fuzzy stochastic mathematical model for a short‐term crude oil scheduling problem with preventive maintenance for charging tanks. The objective function of the proposed model minimizes the supply chain total cost. Lost sale is also reflected in the objective function acting as a criterion for the supply chain responsiveness. This study is among the first attempts to consider preventive maintenance operations, unavailability of the charging tanks, and minimizing lost sale for crude oil scheduling problem. The model encounters two types of uncertainty, namely fuzzy and stochastic uncertainty caused by demand, cost, and time parameters. Fuzzy programming and chance constraint method are employed to formulate the nondeterministic model. The model's applicability is illustrated with a case study. Results indicate that in cases where demand is sizable compared to storage volumes and maximum flow rate is sufficiently large, a preventive maintenance system can be scheduled regularly. Furthermore, the performed sensitivity analyses reveal that the problem is more sensitive to demand uncertainty than to cost and time uncertainty.

Prioritizing high-risk sub-groups in a multi-manufacturer vaccine distribution program

PurposeAnnual influenza epidemics cause great losses in both human and financial terms. The purpose of this paper is to propose a model for optimizing a large-scale influenza vaccination program (VP). The goal is to minimize the total cost of the vaccination supply chain while guaranteeing a sufficiently high level of population protection. From a practical point of view, the analysis returns the number of shipments and the quantity of vaccines in each periodic shipment that should be delivered from the manufacturers to the distribution center (DC), from the DC to the clinics, and from the clinics to each sub-group of customers during the vaccination season.Design/methodology/approachA mixed-integer programming optimization model is developed to describe the problem for a supply chain consisting of vaccine manufacturers, the healthcare organization (HCO) (comprising the DC and clinics), and the population being vaccinated (customers). The model suggests a VP that implemented by a nation-wide HCO.FindingsThe benefits of the proposed approach are shown to be particularly salient in cases of limited resources, as the model distributes demand backlogs in an efficient manner, prioritizing high-risk sub-groups of the population over lower-risk sub-groups. In particular, the authors show a reduction in direct medical burden of consumers, such as the need for doctors, hospitalization resources, and reduction of indirect, non-medical burden, such as loss of workdays.Practical implicationsDrawing from the extended enterprise paradigm, and, in particular, taking consumer benefits into account, the authors suggest an operational-strategic model that creates impressive added value in a highly constrained supply chain. The model constitutes a powerful decision tool for the deployment of large-scale seasonal products, and its implementation can yield multiple benefits for various consumer segments.Originality/valueThe model proposed herein constitutes a decision support tool comprising operational-tactical and tactical-strategic perspectives, which logistics managers can utilize to create an enterprise-oriented plan that takes into account medical and non-medical costs.

A three level joint location-inventory problem with correlated demand, shortages and periodic review system: Robust meta-heuristics

This paper considers a location-inventory problem in the three-level supply chain where demand across the retailers is assumed to be correlated and inventory shortages are allowed. For better monitoring the stock status, a periodic review of inventory level is taken into account. In order to overcome the joint location-inventory problem, this paper proposes an optimization model based on a mixed integer non-linear programming (MINLP) whose objective function is the minimization of the total supply chain costs. To solve the designed MINLP model, two meta-heuristic algorithms are presented, including genetic algorithm (GA) and simulated annealing (SA) with an appropriate decoding scheme. Since the performance of meta-heuristic algorithms depends on setting the parameters; therefore, the Taguchi method is used to set parameters of the developed solving algorithms. Finally, the proposed algorithms have been used to several numerical test problems that indicate the higher performance of the GA compared with the SA in terms of objective function.

A single-supplier, multi-buyer, multi-product VMI production-inventory system under partial backordering

The vendor managed inventory (VMI) is an efficient coordination policy in supply chain management, in which supplier is responsible to manage inventory at the buyer and decides on replenishment policies. This paper presents a VMI model for a supply chain problem with a single supplier, multi-buyer and multi-product, via a production-inventory system in which shortage is allowed and partially backordered. In order to take into account the concern about environmental issues, the total green house gas emission is also considered as a green constraint. The aim is to find the appropriate values of production quantity and the maximum shortage level for all products of all buyers in such a way that the total cost of supply chain is minimized under VMI. The problem is formulated as a non-linear programming model and then a decomposition based analytical approach is proposed to solve it optimally. At the end, a numerical example is discussed to illustrate the proposed approach.

Impact of Retailers with Knowledge of Supplier's Inventory on Supply Chain Performance

We model a supply chain consisting of a supplier and multiple retailers facing deterministic demand. We denote some retailers as strategic in the sense that given the supplier inventory information, they will implement the optimal stocking policy by incorporating such information. On the other hand, some retailers are denoted as naïve in the sense that they ignore supply information and resort to a simplistic ordering policy. Naïve retailers learn the optimal policy over time and adjust their orders accordingly. We study the dynamics of this game and investigate the impact of such strategic and naïve retailers on the cost, ordering pattern and stocking policies of all parties. We analyze the supply chain under two scenarios: the centralized supply chain where the objective is to minimize the total supply chain cost, and the decentralized supply chain where each self‐interested player minimizes its own cost in a Stackelberg game setting. We fully characterize the optimal policies under both centralized and decentralized scenarios and show that, surprisingly, the supply chain might be better off by virtue of naïve retailers. The result is driven by the fact that strategic and naïve players’ decisions shift the positioning of inventory in the supply chain with its final impact being determined by the relative costs of different retailer‐types. Our results also offer managerial insights into how access to supply information can improve supply chain performance.

Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization

A real-world inventory control system, due to its nonlinear, stochastic, time-dependent nature, and the presence of complex interactions between supply chain members, can become quite challenging to optimize and requires a complex model. At this point, the Simulation Optimization (SO) model gains a better understanding of the complex and messy phenomenon of the inventory control of supply chain members. By creating SO models for Distribution Center (DC)s and Suppliers, we wish to present flexible and comprehensible research on the important decision of whether to minimize the differences between total overordering cost and total underordering cost (Model 1) or to minimize the total supply chain cost (Model 2). We also try to point out several important issues: the optimal value of the initial inventory, the reorder point, and the order-up-to level in continuous (s, S) policy for each DC and each Supplier; whether SO models can successfully integrate the supplier selection and continuous (s, S) policy for the supply chain environment; how to apply statistical analysis skills to compare these SO models with a greater level of detail. According to the cost analysis, the total supply chain cost of Model 1 is improved approximately 22% with Model 2. Also, Model 2 is the best one according to quantity-based analysis, order-based analysis, probability-based analysis, and lead-time-based analysis. Model 2 can be successfully applied for the actual situation of the supply chain inventory system and companies can obtain a remarkable amount of saving while increasing their competitive edge.

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.

A new robust model for location - inventory in three echelon supply chain with uncertain demand in small and medium business enterprises

Today, with changing patterns of production and services competition, independent companies are faced with competition between supply chains. Most of the small and medium businesses (SMEs) are still used to the traditional supply planning, production and distribution. It means, each of these SMEs plan independently for their activities, in most cases, this will increase the cost of the whole supply chain. In this study, we present a robust model of location, inventory in three-echelon supply chain under uncertainty. Model has been considered in single-period, multi-product, multi-mode transportation with three level producers - distributors-retailers in certain and robust mode. The innovation of this article are considering multi-mode transportation, rousting the model and sending the goods directly from the factory to the retailer. The objective of the proposed model is minimising the total cost of the three-echelon supply chain. The certain model is solved by GAMS and robust model is solved by GAMS and augmented e-constraint method.

A new robust model for location-inventory in three echelon supply chain with uncertain demand in small and medium business enterprises

Today, with changing patterns of production and services competition, independent companies are faced with competition between supply chains. Most of the small and medium businesses (SMEs) are still used to the traditional supply planning, production and distribution. It means, each of these SMEs plan independently for their activities, in most cases, this will increase the cost of the whole supply chain. In this study, we present a robust model of location, inventory in three-echelon supply chain under uncertainty. Model has been considered in single-period, multi-product, multi-mode transportation with three level producers - distributors-retailers in certain and robust mode. The innovation of this article are considering multi-mode transportation, rousting the model and sending the goods directly from the factory to the retailer. The objective of the proposed model is minimising the total cost of the three-echelon supply chain. The certain model is solved by GAMS and robust model is solved by GAMS and augmented e-constraint method.

Cost Sharing in the Prevention of Supply Chain Disruption

We examine the influence of cost-sharing mechanism on the disruption prevention investment in a supply chain with unreliable suppliers. When a supply chain faces considerable loss following a disruption, supply chain members are motivated toward investing in manners that reduce their disruption probability. In improving supply chain reliability, the cost-sharing mechanism must be set appropriately to realize the efficiency of the disruption prevention investment. In a supply chain where the focal manufacturing company has its own subsidiary supplier and an outsourcing supplier, we analyze different forms of cost-sharing mechanisms when both suppliers confront disruption risks. Through the cost-sharing mechanisms presented in this study, supply chain members can improve their reliability via disruption prevention investments without considerably increasing the total supply chain cost. We present two concepts, the cost-sharing structure and the cost-sharing ratio, in this study. As the two key components of cost-sharing mechanism, these two elements constitute a practicable cost allocation mechanism to facilitate disruption prevention.

Exploring omnichannel and network design in omni environment

AbstractModern 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 differen...

Ranking Operational Causes of Bullwhip Effect in Supply Chain Using AHP: Perception of Managers in FMCG Sector

Bullwhip effect (BWE), which refers to the phenomenon wherein the variance in demand orders increases as one moves up the supply chain (SC), has been a source of concern for most of the SC managers as it has serious implications in the SC. Problems can vary from inefficient ordering and inventory policies (IP) to higher total cost of SC with extreme cases of stock-out and disruptions in the SC. The literature on this topic suggests various factors which cause BWE and these have further been divided into two broad groups, namely, “operational causes” and “behavioural causes.” This study focuses on the operational factors identified through the survey of literature from 1990 to 2013 and suggests 18 factors which contribute to the BWE. Further, from an analysis of literature, based on a number of articles and their citations, and responses from field experts through a questionnaire, a refined list of seven factors has been taken up for the study. These factors have been ranked using the “analytical hierarchy process” (AHP). The ranking is based on pairwise comparisons based on the perception of the SC managers in the manufacturing sector in north India. The article is divided into five sections. The first section deals with the introduction and identification of the BWE. The second section identifies the various factors which cause this effect under the two broad categories, namely, the operational and behavioural factors. The third section discusses AHP and the methodology used in the article to rank the operational factors. The fourth section presents the results in a tabular and graphical form. Order batching (OB) is found to be the biggest contributor of BWE in manufacturing sector in the aforementioned region followed by demand signal processing (DSP), lead time (LT), IP, price fluctuation (PF), lack of trust (LOT), and number of echelons (NOE). The fifth and final section undertakes a discussion and presents guidelines for future research.

Optimizing a vendor managed inventory (VMI) supply chain for perishable products by considering discount: Two calibrated meta-heuristic algorithms

Vendor Managed Inventory (VMI) is one of the inventory management strategies that reduce costs, increase responsiveness and improve collaboration between the members of supply chain. Although the VMI can reduce response time and deterioration in perishable supply chain (PSC), but there is a few reports on using VMI for PSC. In this paper VMI strategy is used for managing the inventory of perishable product at two-level supply chain with single vendor and multiple retailers. After passing a specific time of product lifetime that called the critical time, the product would be perished by a probability distribution function. It is probable that the inventory of product is not sold after the critical time, therefore the management system will use discount to stimulate demand. Then a proposed model is formulated as a nonlinear programming model. The objective function of the proposed model is minimizing the total cost of supply chain including the cost of fixed ordering, holding, discount, and deterioration whereas replenishment cycles and order size for retailers and also production time needed to supply inventory of each retailer can be determined through the proposed model. Since the model is a NP-hard problem, a Genetic algorithm (GA) and a Particle Swarm Optimization (PSO) algorithm are developed for solving it appropriately and the results are presented that PSO algorithm has a better performance for solving of the proposed model in this paper.Taguchi method is an applied to calibrate the parameters of the algorithms into provide reliable solution. Finally, the conclusion and further research are presented.

An inventory position-based policy for better supply chain performance

This paper proposes an inventory position-based policy, and its performance is assessed in terms of supply chain fill rate, bullwhip effect and total cost of supply chain under different scenarios. The scenarios are developed considering the different levels of supply chain structures, demand patterns, and information sharing strategies. Under each scenario, the performance of the proposed policy is compared with the other inventory position-based policies such as Order-Up-To, Order-Up-To smoothing, (s, S) and (s, Q) using spreadsheet-based simulation methodology. The multi-attribute performance comparison, grey relational analysis, shows that the proposed policy is the best compared to other policies tested because of the better performance in supply chain fill rate and order smoothing. The simulation study provides sufficient insights to managers in selecting a suitable inventory policy based on the operating conditions of a supply chain and multiple performance measures.