Integrated Order Scheduling and Packing

We consider an integrated production–distribution scheduling model in a make‐to‐order supply chain consisting of one supplier and one customer. The supplier receives a set of orders from the customer at the beginning of a planning horizon. The supplier needs to process all the orders at a single production line, pack the completed orders to form delivery batches, and deliver the batches to the customer. Each order has a weight, and the total weight of the orders packed in a batch must not exceed the capacity of the delivery batch. Each delivery batch incurs a fixed distribution cost. The problem is to find jointly a schedule for order processing and a way of packing completed orders to form delivery batches such that the total distribution cost (or equivalently, the number of delivery batches) is minimized subject to the constraint that a given customer service level is guaranteed. We consider two customer service constraints—meeting the given deadlines of the orders; or requiring the average delivery lead time of the orders to be within a given threshold. Several problems of the model with each of those constraints are considered. We clarify the complexity of each problem and develop fast heuristics for the NP‐hard problems and analyze their worst‐case performance bounds. Our computational results indicate that all the heuristics are capable of generating near optimal solutions quickly for the respective problems.     

Decision model of customer order decoupling point on multiple customer demands in logistics service supply chain

The key factor in realising competitiveness of a logistics service supply chain (LSSC) is its ability to meet customised requirements with the cost of mass service. This paper introduces the concepts of mass customisation and customer order decoupling point (CODP) into the field of logistics service to solve the problem of CODP when a logistics service integrator (LSI) in an LSSC operates under mass customisation. Two models of CODP decision, respectively, on single and multiple customer demands, are developed. Both process constraints and lead time constraints from the customised logistics service orders are considered. Unlike CODP positioning models within the manufacturing supply chain, the objective of this paper is to exclusively consider the cost of order transferring and order waiting at CODP to minimise total cost of the LSI. The applications of the two models are also discussed by two real cases, and three major conclusions are drawn. First, introducing mass customisation into an LSSC and minimising total cost of order processing of the LSI by selecting an optimised CODP is feasible. Second, total cost will decrease monotonously along with the movement of CODP toward the last service procedure under the precondition of satisfying lead time constraints. Third, parameters about the customers’ logistics service orders will bring about great influence on the selection of CODP. For instance, the lead time of orders will influence the number of feasible CODPs and the position of optimal CODP. Also, decreasing cost of order transferring and order waiting will not cause the change of optimal CODP, while increasing cost of order transferring and order waiting will lead to the switch of optimal CODP.     

On the fair optimization of cost and customer service level in a supply chain under disruption risks

This paper presents a new decision-making problem of a fair optimization with respect to the two equally important conflicting objective functions: cost and customer service level, in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers, and schedule the orders over the planning horizon, to equitably optimize expected cost and expected customer service level. The supplies of parts are subject to independent random local and regional disruptions. The fair decision-making aims at achieving the normalized expected cost and customer service level values as much close to each other as possible. The obtained combinatorial stochastic optimization problem is formulated as a stochastic mixed integer program with the ordered weighted averaging aggregation of the two conflicting objective functions. Numerical examples and computational results, in particular comparison with the weighted-sum aggregation of the two objective functions are presented and some managerial insights are reported. The findings indicate that for the minimum cost objective the cheapest supplier is usually selected, and for the maximum service level objective a subset of most reliable and most expensive suppliers is usually chosen, whereas the equitably efficient supply portfolio usually combines the most reliable and the cheapest suppliers. While the minimum cost objective function leads to the largest expected unfulfilled demand and the expected production schedule for the maximum service level follows the customer demand with the smallest expected unfulfilled demand, the equitably efficient solution ensures a reasonable value of expected unfulfilled demand.     

Analyzing the Effect of Express Orders on Supply Chain Costs and Delivery Times

Delivery time differentiation is a supply chain concept that has been implemented in various industries, but not yet in the automotive industry. One reason is that the effects of delivery time differentiation on the supply chain are not well understood. The BMW Group, for instance, has considered offering an express order option, where express orders bypass standard orders in the supply chain processes to achieve short delivery times. Express orders distort planning processes, increase operations cost, and increase the delivery times of standard orders, however the effects have not been quantified yet. This study analyzes the impact of express orders on the supply chain, when express orders are built‐to‐order. To understand the supply chain consequences of express orders better, we analyzed the relevant supply chain processes at BMW Group. We determine the effect that built‐to‐order express orders have on delivery times and on component demand. To analyze the effect of introducing express orders on expected delivery times and expected cost, we use queuing theory and derive expressions for the transient behavior of a discrete time batch queue. Our analyses indicate that many supply chain processes are only marginally affected. However, the orders to the suppliers become considerably more uncertain, which must be compensated by additional safety stock. Our results indicate that express orders can be an attractive option for BMW and other automotive companies. If the fraction of express orders stays at a reasonable level, express orders can be delivered within about two weeks.     

Integrated supply,production and distribution scheduling under disruption risks

This paper presents a bi-objective stochastic mixed integer programming approach for a joint selection of suppliers and scheduling of production and distribution in a multi-echelon supply chain subject to local and regional disruption risks. Two conflicting problem objectives are minimization of cost and maximization of service level. The three shipping methods are considered for distribution of products: batch shipping with a single shipment of different customer orders, batch shipping with multiple shipments of different customer orders and individual shipping of each customer order immediately after its completion. The stochastic combinatorial optimization problem is formulated as a time-indexed mixed integer program with the weighted-sum aggregation of the two objective functions. The supply portfolio is determined by binary selection and fractional allocation variables while time-indexed assignment variables determine the production and distribution schedules. The problem formulation incorporates supply–production, production–distribution and supply–distribution coordinating constraints to efficiently coordinate supply, production and distribution schedules. Numerical examples modelled after an electronics supply chain and computational results are presented and some managerial insights are reported. The findings indicate that for all shipping methods, the service-oriented supply portfolio is more diversified than the cost-oriented portfolio and the more cost-oriented decision-making, the more delayed the expected supply, production and distribution schedules.     

A MODEL FOR BATCH ADVANCED AVAILABLE‐TO‐PROMISE

The available‐to‐promise (atp) function is becoming increasingly important in supply chain management since it directly links production resources with customer orders. In this paper, a mixed integer programming (mip) ATP model is presented. This model can provide an order‐promising and ‐fulfillment solution for a batch of orders that arrive within a predefined batching interval. A variety of constraints, such as raw material availability, production capacity, material compatibility, and customer preferences, are considered. Simulation experiments using the model investigate the sensitivity of supply chain performance to changes in certain parameters, such as batching interval size and customer order flexibility.     

Using Lean Six Sigma to improve mobile order fulfilment process in a telecom service sector

This study proposes and follows a specific and systematic framework for implementing Lean Six Sigma (LSS) methodology in a telecom company in order to improve customer satisfaction by minimizing the company’s response time to customer requirements. The goal of this study was achieved by utilizing several LSS tools under five phases of the DMAIC methodology. Unlike previous studies in the telecom sector that used only qualitative method, in this study, both qualitative and quantitative methods were utilized to draw meaningful conclusions. As a result of the implementation of the LSS methodology, the average order fulfilment lead time for sales orders (SO) and value-added service (VAS) orders was reduced from 10.3 to 5.9 days and from 1.5 to 0.5 days, respectively. The reduction in lead time resulted in an increase in the sigma level for SO and VAS orders from 0.44 to 1.26 and from 0.73 to 2.66, respectively. These improvements were expected to lead to a financial benefit in savings of over $600,000 per year in operational costs, enhancements to customer experience and an increase in revenue generating opportunities. Moreover, this article enriches the existing literature on the application of LSS concept in the service industry, and helps the company to speed up the response to customer requirements.     

Sharing shipment quantity information in the supply chain

This paper evaluates the benefit of a strategy of sharing shipment information, where one stage in a supply chain shares shipment quantity information with its immediate downstream customers—a practice also known as advanced shipping notice. Under a periodic review inventory policy, one supply-chain member places an order on its supplier every period. However, due to supplier's imperfect service, the supplier cannot always exactly satisfy what the customer orders on time. In particular, shipment quantities arriving at the customer, after a given lead-time, may be less (possibly more) than what the customer expects—we define this phenomenon as shipment quantity uncertainty. Where shipment quantity information is not shared with customers, the only way to respond is through safety stock. However, if the supplier shares such information, i.e. customers are informed every period of the shipment quantity dispatched, the customer may have enough time to adapt and resolve this uncertainty by adjusting its future order decisions. Our results indicate that in most circumstances this strategy, enabled by information technologies, helps supply-chain members resolve shipment quantity uncertainty well. This study provides an approach to quantify the value of shared shipment information and to help supply-chain members evaluate the cost-benefit trade-off during information system construction. Numerical examples are provided to indicate the impact of demand/shipment parameters on strategy implementation. While previous studies mainly focus on the information receiver's perspective, we evaluate a more general three-tier linear supply chain model via simulation, studying how this strategy affects the whole supply chain: the information sender, the information receiver and the subsequent downstream tier.     

Editorial Another milestone

A Master Production Scheduling Decision Support System within a multi-product medical supplies market has the dual task of providing good customer service levels while maintaining minimum reasonable levels of finished goods stock in the face of considerable internal manufacturing lead time and customer demand uncertainty. This paper examines the critical design parameters within an adaptive model highlighting how the total system orders in the internal pipeline are utilized in the decision-making process for assessing how much to load the internal manufacturing pipeline. Two different methods for tracking manufacturing lead times within the adaptive loop are also considered. Classical control concepts are applied within the Decision Support System (DSS) to avoid any long-term drift in finished stocks. Finally scenario analysis is performed via simulation for a set of design parameters and a range of stimuli typical of company operating situations. An effective decision support system design in terms of architecture and parameter settings is recommended based upon the ability of the model to maintain high customer service levels. The DSS readily interfaces between marketing and production functions to enhance company competitive advantage across a wide range of products.     

Impact of customer loyalty and service operations on customer behaviour and firm performance: empirical evidence from UK retail sector

Abstract

Retail networks are striving to achieve competitive advantage by increasing value through loyalty and efficiency with a focus on service operations. As sales promotions have become an integral part of the retail supply chain planning, customer behavioural aspects based on loyalty and service operations have been challenged greatly. Subsequently, management capabilities, such as planning and timely replenishment, have become complicated tasks for many retail store managers. This study develops a model integrating retail network value and efficiencies with customer behaviour and performance. We validate the model using survey data from prominent U.K. retail store customers. Our data analysis shows that both loyalty and service operation attributes have positive significant impact on customer behaviour, while the service operation mediates the relationship between loyalty and customer behaviour. This result gives a new outlook to build managerial capability based on customer loyalty and service operations. Our results specifically show that the service operation attributes will indirectly influence the customers’ buying behaviour even in the presence of loyalty attribute such as promotion schemes. This result sends a strong signal to retail supply chain managers to offer customised promotions considering local community rather than having uniform sales promotion nationwide.     

A decision support system for optimising the order fulfilment process

Many authors have highlighted gaps at the interfaces between supply chains (SCs) and demand chains. Generally, the latter tends primarily to be ‘agile’ by maximising effectiveness and responsiveness while the former tends to be ‘lean’ by maximising efficiency. When, in the SC, disruptions (that lead to stock-out situations) occur after customer orders have been accepted, managers are faced with the problem of maximising customer satisfaction while taking into consideration the conflicting objectives of the supply and demand sides of the order fulfilment process. This article proposes a cross-functional multi-criteria decision-making (advanced available-to-promise) tool that provides different strategic options from which a solution can be chosen. It also proposes a performance measurement system to support the decision-making and improvement process. The results of some experimental tests show that the model enables to make strategic decisions on the degree of flexibility required to achieve the desired level of customer service.     

Decoupling thinking in service operations: a case in healthcare delivery system design

The notion of decoupling thinking has been well established in the manufacturing operations and supply chain management literature. This paper explores how this decoupling thinking can be applied in service operations and in particular in health care. It first reviews the relevant literature on decoupling fundamentals, the front- and back-office distinction, and new emerging decoupling thinking in service operations. Subsequently, a flow-based framework including content and process is developed for decoupling thinking in service operations. The framework provides an integrated perspective on customer contact, flow driver and flow differentiation (level of customisation). The framework hence, through flow differentiation, introduces the concept of standardisation versus customisation in a service context. This is followed by a health care case example to illustrate how the framework can be applied. The managerial implications are primarily in terms of a modularised approach to system design and management. The framework offers potential for benchmarking with other service systems as well as with manufacturing systems based on the shared foundation in decoupling thinking. Finally, suggestions are provided for further research opportunities derived from this research.     

随机订单干扰下考虑合并决策的供应链网络调度研究

为满足客户个性化需求的快速响应，企业需具备柔性的外部供应链网络结构，以协同方式共同完成产品生产。本文考虑具有交互特征的多个不同类型协同供应链网络，构建生产成本、库存成本、等待成本以及订单延期交货成本最小化的目标函数，并设计合并决策判断变量构建同类订单在相同协同企业处的开始时间约束。此外，模型中考虑确定订单以及随机订单两种类型订单，并设计随机订单在区间时间段中离散时间点的到达概率。为获取协同供应链网络生产调度优化策略，基于随机订单到达与否的场景构建四个子决策模型，并进一步设计判断提前安排随机订单协同生产和不提前安排随机订单协同生产不同调度策略下成本差异的主决策模型。仿真结果表明合并决策在带来生产成本效益的同时也引起了部分订单的延期交货，且不同类型的协同供应链网络对随机订单的抗干扰能力存在一定程度的差异。     

Using Frontier Portfolios to Improve Make‐to‐Order Operations

Make‐to‐order (MTO) manufacturers face a common problem of maintaining a desired service level for delivery at a reasonable cost while dealing with irregular customer orders. This research considers a MTO manufacturer who produces a product consisting of several custom parts to be ordered from multiple suppliers. We develop procedures to allocate orders to each supplier for each custom part and calculate the associated replenishment cost as well as the probability of meeting the delivery date, based on the suppliers' jobs on hand, availability, process speed, and defective rate. For a given delivery due date, a frontier of service level and a replenishment cost frontier are created to provide a range of options to meet customer requirements. This method can be further extended to the case when the delivery due date is not fixed and the manufacturer must “crash” its delivery time to compete for customers.     

An association-based clustering approach to order batching considering customer demand patterns

Research on warehousing systems has gained interest since the 1980s, reflecting the fact that supply chain management has pursued a demand-driven organization with high product variety, small order sizes, and reliable short response times throughout the supply chain. This market trend has affected warehouse management and operations tremendously. Order batching in a warehouse attempts to achieve high-volume order processing operations by consolidating small orders into batches. Order batching is an essential operation of order processing in which several orders are grouped into batches. This paper describes the development of an order batching approach based on data mining and integer programming. It is valuable to discover the important associations between orders such that the occurrence of some orders in a batch will cause the occurrence of other orders in the same batch. An order-clustering model based on 0–1 integer programming can be formulated to maximize the associations between orders within each batch. From the results of several test problems, the proposed approach shows its ability to find quality solutions of order batching problems.     

基于服务能力均衡的网购供应链资源整合决策

为客户提供满意的个性化服务是网购企业提升客户体验价值和竞争力的关键,满意的个性化服务需要对网购企业后台运行的供应链资源进行有效整合。在网购供应链资源整合特征分析基础上,从网购供需服务能力动态协调与均衡的角度出发,基于网购个性化服务模式分析并挖掘出资源整合的主导因素,建立了整合决策的优化数学模型,搭建了改进的蚁群寻优算法来实现整合决策优化的求解。最后通过算例验证了方法的有效性与可行性。     

考虑跨渠道退货的双渠道供应链最优策略

基于零售商线上线下销售与消费者退货并存的双渠道供应链,构建考虑退货的双渠道供应链定价模型以及考虑跨渠道退货策略的双渠道供应链定价决策模型。通过理论分析和数值实验对比,研究集中决策与分散决策下的零售商最优定价策略以及总利润的变化情况,同时分析在不同退货策略下顾客偏好以及退货率对零售商定价和收益的影响。结果表明：零售商渠道的最优价格设置与顾客对渠道的偏好成正比,与退货率成正比。当顾客对某一渠道偏好程度高时,分散决策下的利润要高于集中决策,并且随着该渠道退货率的提高,这种利润差距会进一步扩大,因此零售商应实施分散决策的渠道策略,鼓励线上线下两个渠道进行竞争,从而获得更大的收益。从仿真结果可以看出,对于一般的服装产品,提供跨渠道退货服务的零售商总利润更高,在一定条件下跨渠道服务水平的投入会增加零售商的收入,但这种投入应该控制在一个合理的水平上,并保持服务与顾客回报之间的正相关关系。     

A Comparative Analysis of Master Production Scheduling Techniques for Assemble-To-Order Products

This paper studies the master production scheduling (MPS) activity of manufacturing firms that produce assemble-to-order (ATO) products. It describes four techniques for master scheduling ATO products: end-product bills, modular bills, super bills, and percentage bills. These procedures are compared in terms of the percentage of customer orders delivered late, the mean tardiness of customer order deliveries, and the total cost of inventory using simulation analysis. The results indicate that the performance of an MPS technique is affected by the level of uncertainty of the end products' demands and the degree of component commonality in the product structure. In particular, modular bills produce the highest customer service level and super bills produce the lowest total inventory cost under most operating conditions. The conclusions also suggest that the choice of a particular MPS technique is often a compromise between the benefits of improved MPS performance and the costs of implementing and executing the MPS system.     

An investigation of order release and flow time allowance policies for assembly job shops

The effects of a number of essentially different work order release and flow time allowance policies on the assembly order flow times and assembly order due date performance are investigated. Work orders within an assembly order have different routing length. The assembly order flow time is the time that elapses between the release of the first work order and the completion of the last work order of the assembly order. The timing of the release of work orders, and the distribution of the flow time allowance over the work orders in an assembly order were varied, and used systematic computer simulation to investigate the effects on performance. The results show that the best performance is obtained with simultaneous work order release, an average operation flow time allowance equal to the average operation waiting time and equalized flow time allowances per work order in an assembly order.     

Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing

This paper presents a stochastic mixed integer programming approach to integrated supplier selection and customer order scheduling in the presence of supply chain disruption risks, for a single or dual sourcing strategy. The suppliers are assumed to be located in two different geographical regions: in the producer's region (domestic suppliers) and outside the producer's region (foreign suppliers). The supplies are subject to independent random local disruptions that are uniquely associated with a particular supplier and to random semi-global (regional) disruptions that may result in disruption of all suppliers in the same geographical region simultaneously. The domestic suppliers are relatively reliable but more expensive, while the foreign suppliers offer competitive prices, however material flows from these suppliers are more exposed to unexpected disruptions. Given a set of customer orders for products, the decision maker needs to decide which single supplier or which two different suppliers, one from each region, to select for purchasing parts required to complete the customer orders and how to schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The problem objective is either to minimize total cost or to maximize customer service level. The obtained combinatorial stochastic optimization problem will be formulated as a mixed integer program with conditional value-at-risk as a risk measure. The risk-neutral and risk-averse solutions that optimize, respectively average and worst-case performance of a supply chain are compared for a single and dual sourcing strategy and for the two different objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.