Coordinating a three-layer supply chain with uncertain demand and random yield

The paper considers a three-layer supply chain involving one raw-material supplier, one manufacturer and one retailer. The market demand is assumed to be stochastic and productions at the raw-material supplier and manufacturer are subject to random yield. The centralised model is studied as the benchmark case. The decentralised model is solved and Nash equilibrium solutions are obtained. It is shown that buyback contract fails to coordinate such a supply chain. However, a composite contract framed combining buyback, and sales rebate and penalty contracts is shown to coordinate the supply chain. Numerical examples are provided to illustrate the developed models.     

Stochastic optimization model for order acceptance with multiple demand classes and uncertain demand/supply

This article considers an order acceptance problem in a make-to-stock manufacturing system with multiple demand classes in a finite time horizon. Demands in different periods are random variables and are independent of one another, and replenishments of inventory deviate from the scheduled quantities. The objective of this work is to maximize the expected net profit over the planning horizon by deciding the fraction of the demand that is going to be fulfilled. This article presents a stochastic order acceptance optimization model and analyses the existence of the optimal promising policies. An example of a discrete problem is used to illustrate the policies by applying the dynamic programming method. In order to solve the continuous problems, a heuristic algorithm based on stochastic approximation (HASA) is developed. Finally, the computational results of a case example illustrate the effectiveness and efficiency of the HASA approach, and make the application of the proposed model readily acceptable.     

Coordinating a supply chain under uncertain demand and random yield in presence of supply disruption

A two-echelon supply chain involving one manufacturer and one retailer for a single product is considered in this paper. The end customers’ demand is assumed to be random. The production of the manufacturer is subject to random yield, and there is a possibility of supply disruption in which case no item from her can reach the retailer. The retailer has a backup supplier who is costlier but perfectly reliable, and is having a limit up to which he may deliver. In addition to placing an order to the manufacturer, the retailer is allowed to reserve a quantity from the backup supplier in the ordering period; he may buy up to the reserved quantity after realising actual market demand in the trading period. Aiming at studying the effects of the various uncertainties involved in the chain on the optimal decisions, we develop and analyse centralised and decentralised models. We also propose a contract mechanism to coordinate the chain and find threshold conditions for which the coordinated model would collapse. Numerical examples are provided to illustrate the developed model.     

Newsvendor decisions under supply uncertainty

We analyse the impact of supply uncertainty on newsvendor decisions. First, we derive a solution for a newsvendor facing stochastic supply yield, in addition to stochastic demand. While earlier research has considered independent uncertainties, we derive the optimal order quantity for interdependent demand and supply and provide a closed-form solution for a specific copula-based dependence structure. This allows us to give insights into how dependence impacts the newsvendor’s decision, profit and risk level. In addition to the theory, we present experimental results that show how difficult newsvendor decisions under supply uncertainty are for human subjects. In our experiment, the control group replicated a well-known newsvendor experiment, whereas the test group faced additional supply yield uncertainty. Comparison of these results shows that under low-profit condition, subjects are able to incorporate supply uncertainty quite well in their decisions. Under high-profit condition, the deviation from the optimum is much more significant. We discuss this asymmetry and also propose some ways to improve newsvendor decision-making.     

Service capability procurement decision in logistics service supply chain: a research under demand updating and quality guarantee

Though existing researches have already studied on service quality guarantee and demand updating in a supply chain respectively, there is little attention paid to integrated research on service quality guarantee problem with demand updating. This paper aims to investigate the impacts of demand uncertainty revelation and quality guarantee change cost (GCC) on the optimal decisions of logistics service integrator (LSI) and functional logistics service provider (FLSP) in a logistics service supply chain. At the beginning of the first period, the FLSP first guarantees an initial quality level and the LSI procures service capacity from the FLSP based on the demand prediction. Then the demand information is updated after the first-period demand being satisfied, and the LSI and the FLSP make their optimal decisions based on the renewed demand in the next period. Before the second period, uncertainty complete revelation/uncertainty incomplete revelation (UCR/UIR) and GCC/no guarantee change cost (NGCC) may take place, which will affect the decisions the LSI and the FLSP make. Consequently, four situations are considered: (1) UCR and GCC; (2) UIR and GCC; (3) UCR and NGCC; and (4) UIR and NGCC. In each situation, we derive the optimal decisions of the FLSP and the LSI, and a comparison between the first- and second-period decisions in each situation is conducted. Several managerial insights are concluded, and the most important one is that the LSI is supposed to reduce the procurement quantity and the FLSP is supposed to promise a higher quality defect rate in the case of UIR and NGCC. Furthermore, in case of UIR and GCC, we specify a critical condition in which the LSI and the FLSP insist on the initial decisions of the first period. At last, we conducted numerical analysis and gave a practical example of China Yuantong Express Company to support our conclusions.     

Supply chain network optimization considering assembly line balancing and demand uncertainty

In supply chain optimisation problems, determining the location, number and capacity of facilities is concerned as strategic decisions, while mid-term and short-term decisions such as assembly policy, inventory levels and scheduling are considered as the tactical and operational decision levels. This paper addresses the optimisation of strategic and tactical decisions in the supply chain network design (SCND) under demand uncertainty. In this respect, a two-stage stochastic programming model is developed in which strategic location decisions are made in the first-stage, while the second-stage contains SCND problem and the assembly line balancing as a tactical decision. In the solution scheme, the combination of sample average approximation and Latin hypercube sampling methods is utilised to solve the developed two-stage mixed-integer stochastic programming model. Finally, computational experiments on randomly generated problem instances are presented to demonstrate the performance and power of developed model in handling uncertainty. Computational experiments showed that stochastic model yields better results compared with deterministic model in terms of objective function value, i.e. the sum of the first-stage costs and the expected second-stage costs. This issue proved that uncertainty would be a significant and fundamental element of developed model and improve the quality of solutions.     

Robust optimization applied to uncertain production loading problems with import quota limits under the global supply chain management environment

Global supply chain management presents some special challenges and issues for manufacturing companies in planning production: these challenges are different from those discussed in domestic production plans. Globally loading production among different plants usually involves substantial uncertainty and great risk because of uncertain market demand, fluctuating quota costs incurred in the global manufacturing process, and shortening lead times. This study proposes a dual-response production loading strategy for two types of plants—company-owned and contracted—to hedge against the short lead time and uncertainty, and to be as responsive and flexible as possible to cope with the uncertainty and risk involved. Three types of robust optimization models are presented: the robust optimization model with solution robustness, the robust optimization model with model robustness, and the robust optimization model with the trade-off between solution robustness and model robustness. A series of experiments are designed to test the effectiveness of the proposed robust optimization models. Compared with the results of the two-stage stochastic recourse programming model, the robust optimization models provide a more responsive and flexible system with less risk, which is particularly important in the current context of global competitiveness.     

Planning of capacity,production and inventory decisions in a generic reverse supply chain under uncertain demand and returns

There is a growing interest for the design and operation of reverse supply chain systems due to the cost and the legislation issues. In this paper, we address the disassembly, refurbishing and production operations in a reverse supply chain setting for modular products such as computers and mobile phones considering the uncertainties in this system, which are the return amounts of the used products and demand for final products. We develop a large-scale mixed integer programming model in order to capture all characteristics of this system, and use two-stage stochastic optimisation and robust optimisation approaches to analyse the system behaviour. In the first stage, we focus on the strategic decisions about the capacities at disassembly and refurbishing sites considering different scenarios regarding the uncertainties in the system. In the second stage, we analyse the operational decisions such as production, inventory and disposal rates. We observe through our extensive numerical analysis that the randomness of demand and return values effect the performance of the system substantially and the uncertainty of the return amounts of used products is much more important than the uncertainty of demand in this system.     

Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach

Motivated by a real case of an automobile company, this study proposes a multi-objective, multi-site production planning model integrating procurement and distribution plans in a multi-echelon supply chain network with multiple suppliers, multiple manufacturing plants and multiple distribution centres. The model incorporates four important conflicting objectives simultaneously: minimisation of the total cost of logistics, maximisation of the total value of purchasing, minimisation of defective items and minimisation of late deliveries subject to some realistic constraints. Due to the imprecise/fuzzy nature of the objectives’ aspiration levels and some critical data, an interactive fuzzy goal programming formulation is first developed. Then, a novel fuzzy approach is proposed to convert the FGP model into an auxiliary crisp formulation to find an efficient compromise solution. The proposed model and solution method are validated through some numerical tests. Computational results indicate the practicality and tractability of the proposed model and also the superiority of the proposed auxiliary crisp formulation in contrast to the current alternative fuzzy approaches.     

Pricing and effort decisions for a supply chain with uncertain information

This paper analyses the pricing and effort decisions of a supply chain with single manufacturer and single retailer. The manufacturer produces a kind of product and then wholesales the product to the retailer, who in turn retails it to customers over a single selling season. The retailer can influence demand through her sales effort. This research depicts the consumer demand, the manufacturing cost and the sales effort cost as uncertain variables. Considering the demand expansion effectiveness of sales effort, one centralised and three decentralised game models are built on the basis of the expected value criterion, and the equilibrium solutions are obtained. We investigate the effects of the parameters’ uncertainty degrees on the pricing and effort decisions. The results indicate that the manufacturer benefits from improvement in demand and cost uncertainties when he has at least bargaining power in the supply chain. The results also imply that the uncertainty degree of sales effort elasticity has an outstanding influence on the pricing and effort decisions, whereas the uncertainty degree of price elasticity has a modest impact on these decisions. We also study the effects of the parameters’ uncertainty degrees on the supply chain from the consumers’ perspective. The results suggest that with a power retailer, the retail price should always be on the high end. Consequently, consumers do not necessarily benefit from a power retailer. When the manufacturer and the retailer have equal bargaining power, consumers do not necessarily benefit from the supply chain, either.     

A simulation-optimisation approach for supply chain network design under supply and demand uncertainties

We investigate a three-echelon stochastic supply chain network design problem. The problem requires selecting suppliers, determining warehouses locations and sizing, as well as the material flows. The objective is to minimise the total expected cost. An important feature of the investigated problem is that both the supply and the demand are uncertain. We solve this problem using a simulation-optimisation approach that is based on a novel hedging strategy that aims at capturing the randomness of the uncertain parameters. To determine the optimal hedging parameters, the search process is guided by particle swarm optimisation procedure. We present the results of extensive computational experiments that were conducted on a large set of instances and that provide evidence that the proposed hedging strategy constitutes an effective viable solution approach.     

Managing demand uncertainty through fuzzy inference in supply chain planning

In this paper, we investigate methods for managing the irregular and uncertain demands involved in supply chain planning. We first build a supply chain planning model based on fuzzy linear programming, which defines demand as a fuzzy parameter. Next, we propose a fuzzy inference approach for converting fuzzy demand into crisp demand. In the proposed fuzzy inference-based approach, judgments of upcoming demand from both internal and external experts are used as input variables to reflect the expected demand irregularity. By adopting fuzzy inference, we can compensate for the limitations of the existing demand treatment approaches, which usually demonstrate poor forecasting performance in cases of irregular demand and thus reduce the accuracy of supply chain planning. To verify the feasibility of the proposed approach, we present an illustrative example of a Korean electronics company.     

Aggregate procurement,production, and shipment planning decision problem for a three-echelon supply chain using swarm-based heuristics

Supplier selection is deemed as a crucial strategic decision-making activity in building a competitive edge. Firms prefer to operate with a few trusted suppliers, selected from a bigger pool of vendors. The chosen suppliers are the ones whose commitments are best oriented in realising the business goals of the company. At the same time enterprise targets cannot be achieved in the absence of cost-effective inventory management policies. This has created the inevitable need for aggregate production and distribution planning. Even more competitive strategy would be integrating procurement planning with production-distribution scheduling. We address the problem of integrated procurement, production and shipment planning for a supply chain, spanning over three echelons. Supplier order scheduling is combined with a production-shipment planning process to realise a minimum cost operations policy. Two recently developed swarm heuristics are employed to search for the near optimal solution of the mathematical model, which is developed to capture the aggregate planning problem.     

A decision support framework for global supply chain modelling: an assessment of the impact of demand,supply and lead-time uncertainties on performance

We developed a decision support framework for a global manufacturer of specialty chemicals to study the relative impact of demand, supply and lead-time uncertainties on cost and customer service performance. Our approach combines optimisation and simulation methodologies as follows: mathematical models provide optimal plans via a novel approach to the supply chain planning mechanism of the Company. Simulation models execute the supply chain plans so as to allow the examination of the outcomes under the various sources of uncertainty. The iterative use of optimisation and simulation methodologies allows the user the benefit of obtaining optimal solutions while revealing the impact of uncertainties on system performance. Our results indicate that demand uncertainty has the greatest negative impact on performance for the supply chain that we modelled in this study, emphasising the importance of effective forecasting. The relative importance of supply and lead-time uncertainties varies according to the performance measures. While our results are valid for the specific supply chain and the operating environment we modelled, our study emphasises the importance of the ability to model supply chains realistically to obtain valid and useful results.     

Balancing manual mixed-model assembly lines using overtime work in a demand variation environment

This study deals with the balancing problem of a manual mixed-model assembly line, where the production volume or the product mix changes from shift to shift during the planning horizon. The unstable demand can be characterised by several representative scenarios, and the line uses overtime work to meet the demand variation. The balancing problem concerns how to assign assembly tasks to stations and determine the amount of overtime in each possible demand scenario. The objective is to satisfy the demand in each possible scenario with the minimum labour costs paid for both normal shifts and overtime work. A lower bound on the labour costs is proposed, and a heuristic algorithm is developed to quickly find a feasible solution. A branch, bound and remember (BB&;R) algorithm is then proposed to find better solutions. These solution methods are tested on 765 instances. The BB&;R algorithm obtains optimal solutions for 510 instances and gives high-quality solutions for the remaining 255 instances within 60?s. The experimental results show that the use of overtime work and adjustable cycle times significantly reduces the labour costs, especially when the demand or task processing time variations are large.     

Investigating the demand propagation in EOQ supply networks using a probabilistic model

In this paper, we introduce a new demand probabilistic approach, named M.DPA.eoq (Montanari Demand Probabilistic Approach in economic order quantity [EOQ] scenario), for predicting the demand seen by an upper tier echelon (e.g. a distribution centre) of a supply network, serving several lower tier echelons operating according to an EOQ reorder policy. The M.DPA.eoq is based on an analytic approach, by which we derive the distribution of the demand seen by the upper tier echelon of the supply network. The approach has been designed to be very simple, so as to gain in pedagogical value. The simplicity and ease of application of this approach are confirmed by the possibility of exploiting general purpose software, such as Microsoft Excel^TM, to implement and validate it. Moreover, the M.DPA.eoq has potential to be directly exploited by practitioners, such as supply network managers, to estimate the distribution of the demand the upper tier echelon will face under a defined network structure. Students and researchers could also benefit from such a model, given its ease of understanding and usage. With the purpose of showing its potential usefulness in real cases, we discuss two practical implications of the M.DPA.eoq, referring to the use of its results for: (1) computing the bullwhip effect of the network; and (2) analysing the impact of each retail store on the variance of the demand seen by the upper tier echelon.     

A stochastic inventory model for an immediate liquidation and price-promotion decision under price-dependent demand

We consider a periodic review stochastic inventory system where the current on-hand inventory exceeds the maximum supply needs in the future. Consequently, one must make an immediate inventory liquidation decision on the liquidation quantity and promotional price with the goal of maximising the overall profit where the demand during the liquidation period (DDLP) is a random variable whose distribution depends on the promotional price. We develop a price-dependent DDLP model and an inventory model for optimising the liquidation quantity and unit promotional price. The model is applicable for general distributions of the DDLP and regular demand (i.e. demand during the future periods following the promotion period). We also investigate four special cases where the DDLP and regular demand are assumed to be either exponential or uniform random variables. The two models that assume the exponential distribution for regular demand can be examined analytically and simplified using the mathematical properties we derive. The additional two models that assume the uniform distribution for regular demand do not have closed-form expressions but can be solved numerically. Some numerical examples are presented for further elaboration of the models and to demonstrate their practical use.     

A system dynamics model for the supply chain procurement transport problem: comparing spreadsheets,fuzzy programming and simulation approaches

This article proposes a simulation approach based on system dynamics for operational procurement and transport planning in a two-level, multi-product and multi-period supply chain. This work uses the Vensim^® simulation tool to highlight the potential of system dynamics for supply chain simulation. A real continuous simulation application is presented in an automobile supply chain. The effectiveness of the proposed model is validated through the comparison of the results provided by spreadsheet-based simulation, fuzzy multi-objective programming and system dynamics-based simulation models. The fundamental point of this paper is that the simulation model is the most effective approach in quantifying the trade-off between number of truck shipments and average inventory level. In this case, the number of truck shipments is to be minimised, resulting in a higher inventory level. If the average inventory level were minimised, then there would be more truck shipments. Here, it is shown the benefit of this type of simulation model in reducing inventory by about 10%.     

Incorporating risk aversion and fairness considerations into procurement and distribution decisions in a supply chain

This paper considers a three-tier supply chain in which a manufacturer uses raw materials sourced from multiple suppliers to produce an item and sells it through multiple distributors. We develop an integrated optimisation model to study supply chain procurement and distribution decisions incorporating the manufacturer’s aversion to risk and the distributors’ concern for fairness in a climate of uncertain supply and demand. Resilient strategies, such as alternative sourcing and transshipment, are also considered when optimising the supply chain cost and service level. To solve the problem, a Monte Carlo simulation-based multi-objective stochastic programming model is built. It uses the CVaR (Conditional Value-at-Risk) and unfairness aversion utility function to reflect the decision maker’s risk aversion and the customer’s concern for fairness, respectively. A Normalised Normal Constraint based algorithm is adopted to obtain the Pareto Frontier. In addition, the numerical analysis provides some valuable insights for supply chain managers.