A simple heuristic for echelon policies in serial supply chains

This paper proposes a simple heuristic that generates a solution for echelon (r,nQ,T) policies by sequentially solving a deterministic demand problem, a subproblem with fixed reorder intervals, and a subproblem with fixed batch sizes. For each of these problems, we further simplify the computation by solving a series of single-stage systems whose parameters are obtained directly from the original problem data. In a numerical study, we find that this heuristic outperforms an existing one in the literature.     

Managing raw material in supply chains

In this paper, we explore how firms can manage their raw material sourcing better by developing appropriate sourcing relationships with their raw material suppliers. We detail three empirical case studies of firms explaining their different raw material sourcing strategies: (a) firms can adopt a hands-off approach to raw material management, (b) firms can supply raw material directly to their suppliers, and this may be beneficial for some agents in the supply chain, and (c) firms can bring their component suppliers together, and the resulting cooperation between suppliers can be beneficial for supply chain. We then analytically model the three raw material scenarios encountered in our empirical work, examine the resulting profits along the supply chain, and extend the results to a competitive buyer scenario. Overall, our results show that active management of raw material sourcing can add value to supply chains.     

Integrated scheduling and batch ordering for construction project

Multi-Mode Resource Constrained Project Scheduling Problem and material batch ordering for construction project are integrated to help project manager consider various trade-offs among several costs, such as renewable resources’ cost, material price, ordering cost, back-ordering cost, inventory holding cost and reward/penalty for early/late project completion. Therefore, we prove a mixed integer programming model and impel to calculate inventory holding cost and back order cost in objective function. Moreover, a hybrid algorithm combined adapted harmony search and genetic algorithm is proposed correspondingly. In order to inherit elitist solution and maintain population’s diversity simultaneously, we add a selection operator when the harmony memory is initialized and modify the replacement operator based on distance. Besides, genetic algorithm is adopted based on a ‘012’ coding scheme. Finally, algorithm and model performance is presented and several project instances are provided with different network structures and realizations to discuss the factors on total cost.     

Order-Up-To policies in Information Exchange supply chains

Collaboration in Supply Chains (SC) is concerned with the alignment of the decision making process amongst SC partners. This is crucial in the planning and inventory management area where this alignment is enabled by the exchange of information. Several benefits deriving from such effective collaboration exist, such as: excess inventory elimination, lead times reduction, improved customer service, efficient product development, etc. Operations Management literature proclaims the virtues of collaboration and information sharing but academicians and practitioners have recently identified various gaps that still need further work. More specifically it has been shown that several deleterious phenomena as the bullwhip effect; inventory instability and intermittent orders are not completely eliminated in Information Exchange supply chains. The reason is mainly because companies adopt order policies that are prone to create instability along the SC. In this paper we show how the performance of an Information Exchange SC can be improved by shifting from a myopic periodic review Order-Up-To policy to a periodic review Order-Up-To with feedback gain. To do so, we model the SC structure through difference equations and study the system response in term of internal process efficiency and customer service level.     

Analysis and evaluation of an Assemble-to-Order system with batch ordering policy and compound Poisson demand

We consider a multi-product and multi-component Assemble-to-Order (ATO) system where the external demand follows compound Poisson processes and component inventories are controlled by continuous-time batch ordering policies. The replenishment lead-times of components are stochastic, sequential and exogenous. Each element of the bill of material (BOM) matrix can be any non-negative integer. Components are committed to demand on a first-come-first-serve basis. We derive exact expressions for key performance metrics under either the assumption that each demand must be satisfied in full (non-split orders), or the assumption that each unit of demand can be satisfied separately (split orders). We also develop an efficient sampling method to estimate these metrics, e.g., the expected delivery lead-times and the order-based fill-rates. Based on the analysis and a numerical study of an example motivated by a real world application, we characterize the impact of the component interaction on system performance, demonstrate the efficiency of the numerical method and quantify the impact of order splitting.     

Measurement and optimization of robust stability of multiclass queueing networks: Applications in dynamic supply chains

Multiclass queueing networks are an essential tool for modeling and analyzing complex supply chains. Roughly speaking, stability of these networks implies that the total number of customers/jobs in the network remains bounded over time. In this context robustness characterizes the ability of a multiclass queueing network to remain stable, if the expected values of the interarrival and service times distributions are subject to uncertain shifts. A powerful starting point for the stability analysis of multiclass queueing networks is the associated fluid network. Based on the fluid network analysis we present a measure to quantify the robustness, which is indicated by a single number. This number will be called the stability radius. It represents the magnitude of the smallest shift of the expected value of the interarrival and/or service times distributions so that the associated fluid network looses the property of stability. The stability radius is a worst case measure and is a conceptual adaptation from the dynamical systems literature. Moreover, we provide a characterization of the shifts that destabilize the network. Based on these results, we formulate a mathematical program that minimizes the required network capacity, while ensuring a desired level of robustness towards shifts of the expected values of the interarrival times distributions. This approach provides a new view on long-term robust production capacity allocation in supply chains. The capabilities of our method are demonstrated using a real world supply chain.     

An agent-based model of supply chains with dynamic structures

This paper proposes a common agent-based model for the simulation of MTS and MTO supply chains with dynamic structures. Based on the model, scholars can model supply chains easily. Basic characters of supply chains are proposed in the model. Agents, who are used to simulate the members of supply chains, produce appropriate products by intelligent choices. The relationships among agents are connected by their products. Different agents’ attributes are presented by their knowledge and actions of agents are introduced in the paper. Experiments are produced to show the availability of the agent-based model. The model should be available as a toolkit for the studying of dynamic supply chains.     

Contracting with asymmetric demand information in supply chains

We solve a buyback contract design problem for a supplier who is working with a retailer who possesses private information about the demand distribution. We model the retailer’s private information as a space of either discrete or continuous demand states so that only the retailer knows its demand state and the demand for the product is stochastically increasing in the state. We focus on contracts that are viable in practice, where the buyback price being strictly less than the wholesale price, which is itself strictly less than the retail price. We derive the optimal (for the supplier) buyback contract that allows for arbitrary allocation of profits to the retailer (subject to the retailer’s reservation profit requirements) and show that in the limit this contract leads to the first-best solution with the supplier keeping the entire channel’s profit (after the retailer’s reservation profit).     

Cost analysis in global supply chains

This paper aims to explore effect of supply chain members’ costs change on participants of the network. On one perspective, it explores when there is a cost change to a firm, how other firms are affected and who bear(s) the most effect. On the other perspective, it investigates how an individual firm’s performance is affected by the other members in its network and whose cost change would impose a most significant effect on its profit.     

Existence of solutions to Cauchy problems for a mixed continuum-discrete model for supply chains and networks

We consider a continuum-discrete model for supply chains based on partial differential equations. The state space is formed by a graph: The load dynamics obeys to a continuous evolution on each arc, while at nodes the good density is conserved, while the processing rate is adjusted. To uniquely determine the dynamics at nodes, the through flux is maximized, with the minimal possible processing rate change. Existence of solutions to Cauchy problems is proven. The latter is achieved deriving estimates on the total variation of the density flux, density and processing rate along a wave-front tracking approximate solution. Then the extension to supply networks is showed.     

Optimal inventory policy with supply uncertainty and demand cancellation

We consider a periodic review model where the firm manages its inventory under supply uncertainty and demand cancellation. We show that because of supply uncertainty, the optimal inventory policy has the structure of re-order point type. That is, we order if the initial inventory falls below this re-order point, otherwise we do not order. This is in contrast to the work of Yuan and Cheung (2003) who prove the optimality of an order up to policy in the absence of supply uncertainty. We also investigate the impact of supply uncertainty and demand cancellation on the performance of the supply chain. Using our model, we are able to quantify the importance of reducing the variance of either the distribution of yield or the distribution of demand cancellation. The single, multiple periods and the infinite horizon models are studied.     

Modeling and analysis of build-to-order supply chains

The build-to-order supply chain (BTO-SC) or make-to-order (MTO) system has received a great deal of attention in recent years because of the success of high-tech companies such as Dell, BMW, Compaq, and Gateway. Some auto companies have also implemented BTO-SC. Quite a few research articles have been written on BTO-SC and MTO. However, those that explicitly address the problems of BTO-SCM with modeling are rather limited in number. Considering the growing importance of more informed and timely decision making in these areas, there is a need to encourage further research on the modeling and analysis of global outsourcing, optimization between product variants and the cost of production, the point of differentiation along the production/assembly process, the selection of suppliers, logistics costs, and customer relationship management. Traditional operations research models have been used to solve supply chain management problems. Considering the importance of BTO or MTO, an attempt has been made to review the selected literature on the modeling and analysis of BTO-SC with the objectives of providing assistance to and motivating both researchers and practitioners to design, develop, and manage BTO-SC effectively; and suggesting some future research directions on BTO supply chain management (BTO-SCM). The literature available on BTO-SCM has been classified based on the nature of the decision-making areas and then sub-classified to focus on solving problems with modeling and analysis. We have focused mostly on the modeling aspect of the BTO-SC, but have not extended our efforts to empirical research. We have developed a unified framework for modeling and analyzing BTO-SCM and suggest some future research directions.     

A queuing approach for inventory planning with batch ordering in multi-echelon supply chains

This paper presents stylized models for conducting performance analysis of the manufacturing supply chain network (SCN) in a stochastic setting for batch ordering. We use queueing models to capture the behavior of SCN. The analysis is clubbed with an inventory optimization model, which can be used for designing inventory policies . In the first case, we model one manufacturer with one warehouse, which supplies to various retailers. We determine the optimal inventory level at the warehouse that minimizes total expected cost of carrying inventory, back order cost associated with serving orders in the backlog queue, and ordering cost. In the second model we impose service level constraint in terms of fill rate (probability an order is filled from stock at warehouse), assuming that customers do not balk from the system. We present several numerical examples to illustrate the model and to illustrate its various features. In the third case, we extend the model to a three-echelon inventory model which explicitly considers the logistics process.     

Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries for multiple customers in supply chains

In this paper, an integrated due date assignment and production and batch delivery scheduling problem for make-to-order production system and multiple customers is addressed. Consider a supply chain scheduling problem in which n orders (jobs) have to be scheduled on a single machine and delivered to K customers or to other machines for further processing in batches. A common due date is assigned to all the jobs of each customer and the number of jobs in delivery batches is constrained by the batch size. The objective is to minimize the sum of the total weighted number of tardy jobs, the total due date assignment costs and the total batch delivery costs. The problem is NP-hard. We formulate the problem as an Integer Programming (IP) model. Also, in this paper, a Heuristic Algorithm (HA) and a Branch and Bound (B&B) method for solving this problem are presented. Computational tests are used to demonstrate the efficiency of the developed methods.     

An exact solution procedure for multi-item two-echelon spare parts inventory control problem with batch ordering in the central warehouse

We consider a multi-item two-echelon inventory system in which the central warehouse operates under a (Q,R) policy, and the local warehouses implement basestock policy. An exact solution procedure is proposed to find the inventory control policy parameters that minimize the system-wide inventory holding and fixed ordering cost subject to an aggregate mean response time constraint at each facility.     

Flexible long-term capacity planning in closed-loop supply chains with remanufacturing

We deal with long-term demand-driven capacity planning policies in the reverse channel of closed-loop supply chains (CLSCs) with remanufacturing, under high capacity acquisition cost coupled with uncertainty in actual demand, sales patterns, quality and timing of end-of-use product returns. The objective is to facilitate the decision-making when the management faces the dilemma of implementing either a strategy of early large-scale investments to benefit from economies of scale and capacity readiness, or a flexible strategy of low volume but more frequent capacity expansions. We consider a CLSC with two sequential product-types. We study the system’s response in terms of transient flows, actual/desired capacity level, capacity expansions/contractions and total supply chain profit, employing a simulation-based system dynamics optimization approach. Extensive numerical investigation covers a broad range of real-world remanufacturable products under alternative scenarios in relation to the market preference over product-types. The key findings propose flexible policies as improved alternatives to large-scale capacity expansions/contractions in terms of adaptability to the actual pattern of end-of-use product returns and involved risk in the investments’ turnover. Flexible policies are also proposed as practices to avoid overcapacity phenomena in collection and remanufacturing capacity and as robust policies to product demand. Their implementation is revealed to be even more important for the case of remanufacturing, when a high capacity acquisition unit-cost ratio (remanufacturing/collection) is coupled with strong economies of scale. Finally, results under different information sharing structures show changes in remanufacturing policies, thus justifying the importance of coordination between the decision-maker and the distributor.     

An integrated guaranteed- and stochastic-service approach to inventory optimization in supply chains

Multi-echelon inventory optimization literature distinguishes stochastic- (SS) and guaranteed-service (GS) approaches as mutually exclusive frameworks. While the GS approach considers flexibility measures at the stages to deal with stockouts, the SS approach only relies on safety stock. Within a supply chain, flexibility levels might differ between stages rendering them appropriate candidates for one approach or the other. The existing approaches, however, require the selection of a single framework for the entire supply chain instead of a stage-wise choice. We develop an integrated hybrid-service (HS) approach which endogenously determines the overall cost-optimal approach for each stage and computes the required inventory levels. We present a dynamic programming optimization algorithm for serial supply chains that partitions the entire system into subchains of different types. From a numerical study we find that, besides implicitly choosing the better of the two pure frameworks, whose cost differences can be considerable, the HS approach enables additional pipeline and on-hand stock cost savings. We further identify drivers for the preferability of the HS approach.     

Two-tier equilibria for supply chains with a single manufacturer and multiple asymmetric retailers

We derive sufficient conditions for the existence and uniqueness of the Stackelberg–Nash–Cournot equilibria for a supply chain problem with a single manufacturer and multiple asymmetric retailers and characterize the first and second order derivatives of the total equilibrium quantities. The Stackelberg manufacturer is assumed to supply a homogeneous product to all retailers with the retail price determined by a general nonlinear inverse demand function. We provide several extensions of our previous results [G.J. Kyparisis, C. Koulamas, A note on equilibria for two-tier supply chains with a single manufacturer and multiple retailers, Operations Research Letters 39 (2011) 471–474] obtained for a similar supply chain with symmetric retailers.     

An analytical method for cost analysis in multi-stage supply chains: A stochastic network model approach

This paper studies the cost distribution characteristics in multi-stage supply chain networks. Based on the graphical evaluation and review technique, we propose a novel stochastic network mathematical model for cost distribution analysis in multi-stage supply chain networks. Further, to investigate the effects of cost components, including the procurement costs, inventory costs, shortage costs, production costs and transportation costs of supply chain members, on the total supply chain operation cost, we propose the concept of cost sensitivity and provide corresponding algorithms based on the proposed stochastic network model. Then the model is extended to analyze the cost performance of supply chain robustness under different order compensation ability scenarios and the corresponding algorithms are developed. Simulation experiment shows the effectiveness and flexibility of the proposed model, and also promotes a better understanding of the model approach and its managerial implications in cost management of supply chains.     

A stochastic model for risk management in global supply chain networks

With the increasing emphasis on supply chain vulnerabilities, effective mathematical tools for analyzing and understanding appropriate supply chain risk management are now attracting much attention. This paper presents a stochastic model of the multi-stage global supply chain network problem, incorporating a set of related risks, namely, supply, demand, exchange, and disruption. We provide a new solution methodology using the Moreau–Yosida regularization, and design an algorithm for treating the multi-stage global supply chain network problem with profit maximization and risk minimization objectives.