Total Supply Chain Cost Research Articles

A quantitative model for disruption mitigation in a supply chain

In this paper, a three-stage supply chain network, with multiple manufacturing plants, distribution centers and retailers, is considered. For this supply chain system we develop three different approaches, (i) an ideal plan for an infinite planning horizon and an updated plan if there are any changes in the data, (ii) a predictive mitigation planning approach for managing predictive demand changes, which can be predicted in advance by using an appropriate tool, and (iii) a reactive mitigation plan, on a real-time basis, for managing sudden production disruptions, which cannot be predicted in advance. In predictive mitigation planning, we develop a fuzzy inference system (FIS) tool to predict the changes in future demand over the base forecast and the supply chain plan is revised accordingly well in advance. In reactive mitigation planning, we formulate a quantitative model for revising production and distribution plans, over a finite future planning period, while minimizing the total supply chain cost. We also consider a series of sudden disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions and which consequently require plans to be revised after the occurrence of each disruption on a real-time basis. An efficient heuristic, capable of dealing with sudden production disruptions on a real-time basis, is developed. We compare the heuristic results with those obtained from the LINGO optimization software for a good number of randomly generated test problems. Also, some numerical examples are presented to explain both the usefulness and advantages of the proposed approaches.

Integrating packaging and supply chain decisions: Selection of economic handling unit quantities

In this work, a new framework is developed for selecting the packaging parameters that optimize end-to-end supply chain costs assuming a fixed supply chain network for one period of deterministic demand for a single product shipping to multiple customers. The Handling Unit Quantities represent the type and size of the nested handling units such as containers, pallets, cases and sub-cartons. The approach seeks the optimal or Economic Handling Unit Quantities (EHUQ) that fully define the choice of container type and loading configuration, pallet and case quantities, warehouse pick profiles, as well as the prediction of end-to-end supply chain costs. The problem is formulated as a non-convex Mixed Integer Non Linear Programming (MINLP) problem, and transformed into a Quadratically Constrained Quadratic Program (QCQP). It considers a 3-echelon supply chain of factories, warehouses and customers. The model optimizes the total supply chain costs of manufacturing, packaging consumables, in- and out-bound transportation, warehouse receiving, picking and packing costs. The formulation requires integer variables to model the packaging choices, binary variables to capture transportation choices, non-linear inverse bi- and tri-linear terms, and discontinuous step functions which are required to model warehouse picking costs. A power law relationship is used for customer demand to generate test problems that replicate the complexity of real world problems. With a small amount of information, the cost, operational performance and environmental impact for supply chain options under evaluation may be predicted. Because of the size and complexity of the problem, a heuristic method was developed to provide solutions quickly.

Optimizing a two-echelon serial supply chain with different carbon policies

In recent years, organizations are becoming environment conscious due to stringent laws, competitive advantages and increasing awareness of customers. They are integrating environmental practices into their operations to curb carbon emissions. Regulatory bodies are also imposing carbon policies to check emission. In this paper, we developed three models considering three carbon policies (I. carbon tax, II. strict cap and III. cap-and-trade) and have determined the optimal order quantity and number of shipments for a two-echelon supply chain. The objective is to minimize the total supply chain cost which comprises the ordering, setup, production, inventory holding and transportation costs. In Model I tax on carbon emissions has been included in the cost function; in Model II we have considered a strict carbon cap on the total carbon emission; and in Model III trading price of carbon is included in the cost function. A numerical study is given to illustrate the solution procedure. Further, sensitivity analyses are performed to examine the impact of the various parameters on the total cost and total emission.

Optimal inventory modeling of supply chain system involving quality inspection errors and fuzzy defective rate

In this paper an integrated vendor–buyer supply chain model is designed for a defective inventory product. We provide a simple mathematical model for determining an optimal vendor–buyer inventory policy by accounting for fuzzy defective rate and quality inspection errors at the buyer’s end. In this study we fuzzify the defective rate to the triangular fuzzy number in the expected total cost of the entire supply chain. We then used the signed distance method to defuzzify the fuzzy total cost of the system and a methodology has been proposed to minimize this cost. We frame an iterative algorithm procedure to achieve the optimal solution of delivery lot-size, the total number of deliveries from the vendor to the buyer in one production batch, and it has been illustrated by way of a numerical example. Also sensitivity analysis are given to demonstrate the performance of the proposed methodology. Our results indicate that the optimal solutions of the fuzzy case slightly fluctuate from the solutions of the crisp case.

Modelling of supply chain of rice in Bangladesh

ABSTRACTRice supply chain in Bangladesh is a focus for considerations of food security and climate change impacts. Although Bangladesh is self-sufficient in rice, supply chain management lacks efficiency and results in artificial crisis and price volatility. This paper presents a system dynamics model of supply chain of rice starting from the farmers to the consumers to address supply chain management scenarios and design the policy options for efficient and sustainable supply chain management of rice in Bangladesh to ensure the availability of rice to the consumers in an economic manner under uncertainty. Towards these ends, the impacts of productivity changes of the seasonal production of rice, lead time and demand average variability on the supply chain performances are assessed and different management policies are analysed. Initially drying the rice and round the year operation of the milling industries stabilises not only the milling inventory but also the whole system. Also less total supply chain cost from economic operation results in less fuel consumption and hence less contribution to global warming. The model has the potential to identify and study the critical components of the overall supply chain, allowing for the creation of an efficient and sustainable supply chain network.

Multi-echelon Supply Chain Flexibility Enhancement Through Detecting Bottlenecks

This study suggests a supply chain design deploying a novel idea from production planning. The idea of capacity bottlenecks is used to improve flexibility in a multi-echelon multi-product supply chain. We suggest an optimization model that focuses on optimal capacity allocations to bottleneck points in order to enhance overall flexibility. The proposed mixed-integer linear programming model minimizes the total cost of facility establishment as well as their utilization and transportation cost. The performance of suggested model is investigated by several test problems with uncertainty in demand, cost, capacity, and product specifications. The results indicate the superiority of the suggested model to the previous flexibility formulation method. According to the numerical results, the proposed model decreases the total supply chain cost by up to 16 % on average. Another advantageous feature of the proposed model is its capability of solving previously insolvable test problems by optimizing flexibility levels.

Supply chain cost model for prefabricated building material based on time-driven activity-based costing

The effect of ‘supply chain management’ can be leveraged when benefits of collaboration within and beyond the capacities of individual organizations are witnessed. One of the primary tasks in reducing total supply chain costs is to understand where the costs occur in a supply chain and how each activity impacts the total supply chain costs. Most supply chains in construction usually involve multiple entities, each one in a different process. A rebar supply chain is one example where many entities are involved in different processes. The supply chain coordinator needs a supply chain cost model, which shows how each activity impacts all supply chain costs to reduce the total costs. The research suggests a supply chain cost model using time-driven activity-based costing. The proposed cost model was applied to a building construction project, followed by sensitivity analysis identifying critical activities. This method can be adapted to analyze other fragmented material supply chains in the construction industry.

Quantifying the Impact of Feedstock Quality on the Design of Bioenergy Supply Chain Networks

Logging residues, which refer to the unused portions of trees cut during logging, are important sources of biomass for the emerging biofuel industry and are critical feedstocks for the first-type biofuel facilities (e.g., corn-ethanol facilities). Logging residues are under-utilized sources of biomass for energetic purposes. To support the scaling-up of the bioenergy industry, it is essential to design cost-effective biofuel supply chains that not only minimize costs, but also consider the biomass quality characteristics. The biomass quality is heavily dependent upon the moisture and the ash contents. Ignoring the biomass quality characteristics and its intrinsic costs may yield substantial economic losses that will only be discovered after operations at a biorefinery have begun. This paper proposes a novel bioenergy supply chain network design model that minimizes operational costs and includes the biomass quality-related costs. The proposed model is unique in the sense that it supports decisions where quality is not unrealistically assumed to be perfect. The effectiveness of the proposed methodology is proven by assessing a case study in the state of Tennessee, USA. The results demonstrate that the ash and moisture contents of logging residues affect the performance of the supply chain (in monetary terms). Higher-than-target moisture and ash contents incur in additional quality-related costs. The quality-related costs in the optimal solution (with final ash content of 1% and final moisture of 50%) account for 27% of overall supply chain cost. Based on the numeral experimentation, the total supply chain cost increased 7%, on average, for each additional percent in the final ash content.

A sustainable second-generation biodiesel supply chain network design problem under risk

This paper presents a multi-objective possibilistic programming model to design a second-generation biodiesel supply chain network under risk. The proposed model minimizes the total costs of biodiesel supply chain from feedstock supply centers to customer centers besides minimizing the environmental impact (EI) of all involved processes under a well-to-wheel perspective. Non-edible feedstocks are considered for biodiesel production. Variable cultivation cost of non-edible feedstock is assumed to be non-linear and dependent upon the amount of cultivated area. New formulation of possibilistic programming method is developed which is able to minimize the total mean and risk values of problems with possibilistic-based uncertainty. To solve the proposed multi-objective model, a hybrid solution approach based on flexible lexicographic and augmented ɛ-constraint methods is proposed which is capable to find appropriate efficient solutions from the Pareto-optimal set. The performance of the proposed possibilistic programming method as well as the developed solution approach are evaluated and validated through conducting a real case study in Iran. The outcome of this study demonstrates that high investment cost is required for improving the environmental impact and risk of sustainable biodiesel supply chain network design under risk. Decision maker preferences are required for suitable trade-off among total costs, risk values and environmental impact.

Concurrent Design of Assembly Plans and Supply Chain Configurations Using AND/OR Graphs and Dynamic Programing

Making concurrent decisions on assembly plans and supply chain configurations (SCCs) is a desirable strategy in today's competitive global market. Such concurrent decisions help reduce decision iterations for shortened lead time and lower the total cost for manufacturers and their supply chains. However, there lacks deep understanding of the interaction between assembly plans and SCCs, as well as methodologies for integrating them. In this paper, we consider an assembly planning problem that simultaneously decomposes an assembly product to modules and assigns the modules to suppliers. The joint decision is formulated as an optimization problem of finding the assembly decomposition and the corresponding supply chain network with the lowest total supply chain cost. We develop a coordinated decision-making method by integrating assembly AND/OR graphs with supply chain cost information. A dynamic programing (DP) based algorithm is developed to efficiently solve the combined optimization problem. Case studies demonstrate that the proposed method can find optimal solutions for the integrated decision-making problem efficiently and provide a tool to plan assemblies from the perspective of the total supply chain cost in the early stages of product development.

A dynamic programming approach to integrated assembly planning and supplier assignment with lead time constraints

As manufacturers face fierce competition in the global market, responsiveness has become an important competitiveness factor in addition to quality and cost. One essential responsiveness strategy is to reduce product development and lead times by integrating assembly planning with supplier assignment. This paper addresses the problem of integrated assembly and supply chain design under lead-time constraints by formulating and solving an optimisation problem with minimal total supply chain costs. This new time-constrained joint optimisation problem belongs to an NP-hard resource-constrained scheduling problem. To model this problem effectively, we develop a novel Hyper AND/OR graph and apply it for integrating assembly and supply chain decisions. We also develop a dynamic programming model and associated algorithm in order to solve the integrated optimisation problem with pseudo-polynomial time complexity in practice. Numerical case studies validate that the methods developed can solve the integrated decision-making problem optimally and efficiently. This paper overcomes the limitations of previous studies on concurrent assembly decomposition and supplier selection, which optimises cost without time constraints. The models and results of this research can be applied to a variety of areas including assembly design, maintenance module planning and supply chain restructuring.

A Multiperiod Supply Chain Network Design Considering Carbon Emissions

This paper introduces a mixed integer linear programming formulation for modeling and solving a multiperiod one-stage supply chain distribution network design problem. The model is aimed to minimize two objectives, the total supply chain cost and the greenhouse gas emissions generated mainly by transportation and warehousing operations. The demand forecast is known for the planning horizon and shortage of demand is allowed at a penalty cost. This scenario must satisfy a minimum service level. Two carbon emission regulatory policies are investigated, the tax or carbon credit and the carbon emission cap. Computational experiments are performed to analyze the trade-offs between the total cost of the supply chain, the carbon emission quantity, and both carbon emission regulatory policies. Results demonstrate that for a certain range the carbon credit price incentivizes the reduction of carbon emissions to the environment. On the other hand, modifying the carbon emission cap inside a certain range could lead to significant reductions of carbon emission while not significantly compromising the total cost of the supply chain.

Implementation of Hybrid Genetic Algorithm for CLSC Network Design Problem—A Case Study on Fashion Leather Goods Industry

The implementation of closed loop supply chain system is becoming essential for fashion leather products industry to ensure an economically sustainable business model and eco-friendly industrial practice as demanded by the environmental regulations, consumer awareness and the prevailing social consciousness. In this context, this research work addresses a closed loop supply chain network problem of fashion leather goods industry, with an objective of minimizing the total cost of the entire supply chain and also reducing the total waste from the end of life product returns. The research work commenced with a literature review on the reverse and closed loop supply chain network design problems of fashion and leather goods industry dealt in the past. Then, the identified CLSCND problem is solved using a mathematical model based on Mixed Integer Non-Linear Programme (MINLP) and then a suitable Hybrid Genetic Algorithm (HGA) developed for the CLSCND is implemented for obtaining optimum solution. Both the MINLP model and HGA are customized as per the CLSCND problem chosen and implemented for the industrial case of an Indian Fashion Leather Goods Industry. Finally, the solutions obtained for MINLP model in LINGO 15 and for HGA in VB.NET platform are compared and presented. The optimum solution obtained from the suitable HGA is illustrated as an optimum shipment pattern for the closed loop supply chain network design problem of the fashion leather goods industry case.

Comparison Of Periodic-Review Inventory Control Policies In A Serial Supply Chain

Tedarik zinciri yonetimi, dogru urun veya hizmetlerin mumkun olan en iyi kalitede, dogru zamanda, dogru yerde ve uygun fiyatli olarak musterilere sunulmasini saglamakta ve bu sayede musteri tatmininin arttirilmasina yardimci olmaktadir. Tedarik zinciri icerisinde farkli kademelerde stok bulundurulmaktadir. Tedarik zinciri surecinde etkili ve etkin bir stok yonetimi, musterilere sunulan hizmetleri iyilestirilmesini saglamaktadir. Tedarik zinciri maliyetlerinin icerisinde onemli bir paya sahip olan stok maliyetlerinin azaltilmasi urun veya hizmetlerin daha uygun fiyatlarla musterilere sunulmasina yardimci olmaktadir. Bu calismada seri tedarik zincirinde, (R,S) ve (R, S, Qmin) stok kontrol politikalarinin karsilastirilmasi amaclanmistir. Bu stok kontrol yontemleri uygulandiginda, toplam tedarik zinciri maliyetlerinin minimize edilmesini saglayan “S” degerinin optimal degerini bulabilmek icin simulasyon temelli genetik algoritma (GA) kullanilmis ve iki stok kontrol politikasinin uygulanmasinin sonuclari karsilastirilmistir.

The Development of Logistics Services in the United States

This research studies third party logistics (3PL) providers in the United States to investigate how the industry has strategically developed its service offerings in response to the customers’ growing needs in managing global supply chains. Logistics management has significant impacts on various aspects of supply chains such as response time, total supply chain cost, sourcing risk, customer service, security, etc. The results show that 3PL services vary based on industry served, region served, and asset ownership structure. Over the years logistics services providers have served more industry sectors, became asset light, and provided broader services. Two of the five service categories, technology services and special services, have been evolved and expanded rapidly. The development has strengthened the capabilities of the logistics service providers and sustained the growth of the industry.DOI:10.12660/joscmv8n2p23-35URL: http://dx.doi.org/10.12660/joscmv8n2p23-35

Production-inventory model for two-echelon closed-loop supply chain with finite manufacturing and remanufacturing rates

ABSTRACTThis research develops a two-echelon closed-loop supply chain production-inventory model consisting of a manufacturer, a remanufacturer and a retailer. The retailer faces a constant demand from the customers, which is satisfied by new and remanufactured product received from the manufacturer and the remanufacturer, respectively. The manufacturer and remanufacturer produce the product in batches with finite production rate. The manufacturer procures raw material in batches from outside supplier, and the remanufacturer receives the used product from the customers at a constant return rate. Many studies in the area of forward supply chain have established that the centralised production-inventory control models provide better results than the decentralised models. Hence, with synchronised activities of the supply chain players, this research proposes to find an optimal production-inventory policy of the players by minimising the joint total cost of the two-echelon closed-loop supply chain. Numerical illustration is presented to demonstrate the solution procedure and a sensitivity analysis is carried out to examine the impact of key parameters of the problem. Also, the total cost of closed-loop supply chain model is compared with the total cost of the corresponding forward supply chain model.

A game theoretic analysis in capacity-constrained supplier-selection and cooperation by considering the total supply chain inventory costs

The first models of supplier selection were based on maximizing buyer׳s interests. Recently, with the growing attention towards supply chain competition and vertical coordination, some researchers propose models that select suppliers based on total supply chain cost. These models usually consider a centralized system to tackle the problem of proper incentives for members in cooperation. Our purpose in this paper is to analyze the selected suppliers and prices that are agreed on in decentralized supply chains. Also we aim to study the conditions in which the selected suppliers are in the benefit of total supply chain. We use the inventory related costs to calculate payoff function of buyer and suppliers.At the beginning of this paper, a centralized model is developed as a benchmark. Then, we investigate the problem in a decentralized supply chain with the suppliers having capacity constraints. In this regard, we use cooperative and non-cooperative game theory to analyze the selected suppliers and total supply chain costs in two scenarios. In the first scenario, suppliers act independently while in the second scenario the suppliers cooperate with each other making coalitions. It is shown that the cooperative model could result in a stable solution with same total supply chain cost as the centralized model and also, when suppliers have equal opportunity costs for each single production capacity, selected suppliers are determined independent from the opportunity cost. But when the suppliers act independently or have different opportunity costs, the selected suppliers are influenced by the opportunity cost that they have. A numerical example clarifies the findings.

Supply chain optimization of residual forestry biomass for bioenergy production: The case study of Portugal

Within a large set of renewable energies being explored to tackle energy sourcing problems, bioenergy can represent an attractive solution if effectively managed. The supply chain design supported by mathematical programming can be used as a decision support tool to the successful bioenergy production systems establishment. This strategic decision problem is addressed in this paper where we intent to study the design of the residual forestry biomass to bioelectricity production in the Portuguese context. In order to contribute to attain better solutions a mixed integer linear programming (MILP) model is developed and applied in order to optimize the design and planning of the bioenergy supply chain. While minimizing the total supply chain cost the production energy facilities capacity and location are defined. The model also includes the optimal selection of biomass amounts and sources, the transportation modes selection, and links that must be established for biomass transportation and products delivers to markets. Results illustrate the positive contribution of the mathematical programming approach to achieve viable economic solutions. Sensitivity analysis on the most uncertain parameters was performed: biomass availability, transportation costs, fixed operating costs and investment costs.

Supply chain coordination in vendor-managed inventory systems with stockout-cost sharing under limited storage capacity

We examine vendor-managed inventory (VMI) systems with stockout-cost sharing between a supplier and a customer using an EOQ model with shortages allowed under limited storage capacity, in which a stockout penalty is charged to the supplier when stockouts occur at the customer. In the VMI systems the customer and the supplier minimize their own costs in designing a VMI contract and making replenishment decisions, respectively. We compare the VMI systems with an integrated supplier–customer system where the supply chain total cost is minimized. We show that VMI with stockout-cost sharing and the integrated supplier–customer system result in the same replenishment decisions and system performance if and only if the supplier's reservation cost is equal to the minimum supply chain total cost of the integrated system. On the other hand, we also show how VMI along with fixed transfer payments as well as stockout-cost sharing can lead to the supply chain coordination regardless of the supplier's reservation cost. We also provide several interesting computational results.

Evaluation of product-platform decisions based on total supply chain costs

Over the past decades, several companies have introduced product platforms in the design of their products in order to produce a large product variety in a cost-efficient way. However, for some companies, the introduction of platforms ended up being more costly than expected, leading them to reconsider their platform decisions. In this paper, we develop a model to support companies in determining (1) how many platforms to develop, (2) which platforms to develop and (3) which products to derive from which platforms. The model takes into account the impact of these product-platform decisions on a company’s relevant supply chain activities and costs. The model shows how the optimal product-platform decisions depend on the trade-off between the costs of platforms versus the costs of customising these platforms to final product variants. We propose a simulated annealing algorithm to solve large problem instances within reasonable time. The practical validity of our model is shown through its application in a global technology company specialised in the development and production of medical screens.