An integrated optimal inventory lot-sizing and vehicle-routing model for a multisupplier single-assembler system with JIT delivery

In a manufacturing system, a just-in-time (JIT) procurement and supply system is important for reducing cost and responding to customer’s requirement quickly. Successful implementation of a JIT system needs supplier/manufacturer cooperation in small lot-size delivery and inbound logistics cost reduction. In this study, an integrated optimal model of inventory lot-sizing vehicle routing of multisupplier single-manufacturer with milk-run JIT delivery is established. A novel method for computing transportation cost is proposed. Because the integrated model is a NP-hard problem, a meta-heuristic algorithm of ant colony optimisation is developed for solving the model. Numerical examples are used to demonstrate and test the effectiveness of the model and the algorithm. The results show that integrated model with milk-run delivery can reduce the total cost and realise the JIT production and procurement philosophy which emphasises small lot-size production and delivery. The results highlight the importance of cooperation between suppliers and manufacturers in JIT production practices.     

Simulation and performance evaluation of partially and fully integrated sales and operations planning

This article presents rolling horizon simulation models and performance analysis of partially and fully integrated sales and operations planning (S&OP) against traditional decoupled planning in a multi-site make-to-order (MTO) based manufacturing supply chain. Three simulation models are developed illustrating, respectively, the fully integrated S&OP model, which integrates cross-functional planning of sales, production, distribution, and procurement centrally; the partially integrated S&OP model, in which the joint sales and production planning is performed centrally while distribution and procurement are planned separately at each site; and the decoupled planning model, in which sales planning is carried out centrally while production, distribution, and procurement are planned separately and locally. A solution procedure is provided for each model so that a more realistic planning process can be simulated. Performances of rolling horizon simulation models are evaluated against those of the fixed horizon deterministic models. The results demonstrate that while deterministic models are important for theoretical studies, they are insufficient for decision support and performance evaluations in a real business environment. A rolling horizon simulation model is required to provide more realistic solutions. The effects of demand uncertainties and forecast inaccuracies are incorporated in the evaluation. The study is carried out based on a real industrial case of a Canadian-based oriented strand board (OSB) manufacturing company.     

Fuzzy scheduling of a build-to-order supply chain

The overwhelming majority of the literature in the area of supply chain planning and scheduling considers the traditional make-to-stock (MTS) environment. However, manufacturers of assembled products such as cars, computers, furniture, etc. adopt the build-to-order supply chain (BOSC) to become agile in a mass customization process in order to meet diversified customer requirements. In this paper we propose an integrated production–distribution planning model for a multi-echelon, multi-plant and multi-product supply chain operating in a build-to-order (BTO) environment. The uncertainties associated with estimation of the various operational cost parameters are represented by fuzzy numbers. The BOSC scheduling model is thus constructed as a mixed-integer fuzzy programming (MIFP) problem with the goal of reducing the overall operating costs related to component fabrication, procurement, assembling, inspection, logistics and inventory, while improving customer satisfaction by allowing product customization and meeting delivery promise dates at each market outlet. An efficient compromise solution approach by transforming the problem into an auxiliary multi-objective linear programming model is also suggested.     

Optimal ordering and pricing policy for an inventory system with order cancellations

This paper investigates the problem of ordering and pricing over a finite time planning horizon for an inventory system with advance sales and spot sales. It is assumed that the planning horizon is divided into several sales cycles each of which is divided into an advance sales period and a spot sales period. During the advance sales period, all customers are required to make reservations for their orders and will receive them at the arrival time of the replenishment orders. In the case of the spot sales periods, all customers receive their orders at the time of the purchase. In actual practice, since customers with reservations may cancel their orders before receiving them, this paper considers this phenomenon and develops a continuous time inventory model to deal with the proposed problem. This paper maximizes the total profit over a finite time planning horizon by determining the optimal advance sales price, spot sales price, order size, and replenishment frequency. Analysis of results shows that a simple algorithm can be developed to arrive at an optimal decision.     

A two-period model for selection of resilient multi-tier supply portfolio

A two-period decision-making model is developed for selection of resilient supply portfolio in a multi-tier supply chain under disruption risks. The planning horizon is divided into two aggregate periods: before and after the disruption. The resilience of the supply chain is achieved by selection ahead of time primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously, recovery and transshipment portfolios are determined for each disruption scenario and decisions on usage the pre-positioned inventory are made to minimise expected cost or maximise expected service level. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. The findings also indicate that the developed portfolio approach with an embedded network flow structure leads to computationally efficient stochastic mixed integer program with a strong LP relaxation.     

Flexible work-in-process production system in supply chain management under quality improvement

For a complex product production, any flexible manufacturing system with a work-in-process inventory is recommended for a supply chain management (SCM) system. Building a flexible manufacturing system increases the total cost of the supply chain; for this reason, a discrete investment is important. For flexible production systems, production rate within a finite specific interval of production rate as work-in-process inventory is calculated. The aim of the supply chain is to reduce the total cost when demand during the lead time is a random variable with a normal distribution. A crashing cost is utilised to reduce the duration of lead time within the supply chain system. A model is proposed to obtain the optimal flexible production rate with the reduced total cost of the supply chain. A classical optimisation technique is employed to obtain the closed-form and quasi-closed-form solutions of the decision variables. An improved algorithm is designed to obtain the global minimum cost of SCM under the framework of a flexible production system. An illustrative numerical example and sensitivity analysis are given to test the model. A numerical study proves that this model obtains the minimum cost with the optimal decision variables.     

Just-in-time purchasing: an integrated inventory model involving deterministic variable lead time and quality improvement investment

Nowadays supply chain management is a popular practice in manufacturing systems, and just-in-time (JIT) production plays a crucial role in supply chain environments. Companies are using JIT production to gain and maintain a competitive advantage. The characteristics of JIT systems are consistent high quality, small lot sizes, frequent delivery, short lead time, and close supplier ties. This paper presents an integrated inventory model to minimize the sum of the ordering/setup cost, holding cost, quality improvement investment and crashing cost by simultaneously optimizing the order quantity, lead time, process quality and number of deliveries while the probability distribution of the lead time demand is normal. This integrated inventory model is useful particularly for JIT inventory systems where the vendor and the purchaser form a strategic alliance for profit sharing.     

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.     

Risk assessment for the supply chain of fast fashion apparel industry: a system dynamics framework

With the rapid progress of science and technology and continuously growing customer expectations, share of merchandise exhibiting characteristics of perishability is on the rise and a wide range of industries are affected by this phenomenon. This paper focuses on the fast fashion apparel industry due to its particular characteristics such as short life cycle products, volatile demand, low predictability, high level of impulse purchase, high level of price competition and global sourcing. A system dynamics model is proposed for analysing the behaviour and relationships of the fast fashion apparel industry with three supply chain levels. The Conditional Value at Risk measure is applied to quantify the risks associated with the supply chain of these products and also to determine the expected value of the losses and their corresponding probabilities. Multiple business situations for effective strategic planning and decision-making are generated. In particular, the impact of lead time and delivery delays on the supply chain performance (inventory, cost, backlog and risk) is analysed as the key to success for this industry is to satisfy customers’ needs in the shortest time.     

Procurement models under purchase price uncertainty for Chinese oil refineries

This paper examines the crude oil procurement practices of Chinese oil refineries to suggest improved procurement policies that can reduce the cost associated with fluctuating oil prices in the international spot market. In the industrial environment, the purchase price of crude oil is based on the spot price at the time of delivery rather than that at the time of ordering. Therefore, the purpose of this paper is to develop a procurement model that factors in the spot prices at both the time of ordering and the time of delivery so that the total procurement cost can be minimised over the planning horizon. First, the shortcomings of the current procurement policy of a typical Chinese refinery are presented. A model to address these shortcomings is then developed and embellished by incorporating market information dynamics through Bayesian learning. The effectiveness of the proposed models is compared with the current practice using the historical spot price data of crude oil from two representative spot markets; this comparison verifies that the model with Bayesian sampling performs well empirically and can result in considerable cost savings.     

A stochastic dynamic programming based model for uncertain production planning of re-manufacturing system

Re-manufacturing is recycling by manufacturing as-good-as-new products from used products, often involving disassembly, cleaning, testing, part replacement/repair, and re-assembly operations. Production planning and inventory control is one of the most important research issues for re-manufacturing system, which are faced with a greater degree of uncertainty and complexity. This leads to a critical need for planning and control systems designed to deal with the added uncertainty and complexity. We formulate a stochastic dynamic programming based model to study the production planning, i.e. dynamic lot sizing problem, of re-manufacturing systems. In the model the demand and return amounts are stochastic over the finite planning horizon. The objective is to determine the quantities that have to be re-manufactured at each period in order to minimise the total cost, including re-manufacturing cost, holding cost for returns and re-manufactured products and backlog cost. The optimal production plan of the re-manufacturing system over a finite planning horizon can be obtained with the policy iteration method. In the end, a numerical example is performed to illustrate how the model is applied and to prove its feasibility.     

Operations planning in a supply chain system with fixed-interval deliveries of finished goods to multiple customers

Raw material ordering policy and the manufacturing batch size for fixed-interval deliveries of finished goods to multiple customers play a significant role in economically managing the supply chain logistics. This paper develops an ordering policy for raw materials and determines an economic batch size for a product at a manufacturing center which supplies finished products to multiple customers, with a fixed-quantity at a fixed time-interval to each of the customers. In this model, an optimal multi-ordering policy for procurement of raw materials for a single manufacturing system is developed to minimize the total cost incurred due to raw materials and finished goods inventories. The carried over inventory of finished goods from the previous cycle is used as initial finished goods inventory, resulting in shifting the production schedule ahead for the next cycle. A closed-form solution to the problem is obtained for the minimal total cost. The algorithm is demonstrated for multiple customer systems.     

Development of an optimal core steel slitting and inventory policy

The objective of this investigation is to develop an optimal slitting and inventory policy for a deterministic demand of steel over a finite planning horizon. The cost function includes the inventory carrying cost and the cost of scrap steel generated besides the usual fixed costs. The minimum of this cost function is obtained by using a combination of dynamic programming and integer linear programming which provides a practical and sound procedure. Actual data is used to determine the optimal solution for a planning horizon of ten weeks.     

周期可变下考虑多车型的库存路径优化研究

目的 研究基于供应商管理库存模式下，由一个供应商和多个零售商组成的二级供应链配送系统的库存路径优化问题，确定计划期内各配送周期的长度、数量，对供应链中库存策略与配送方案问题进行协调优化，使系统总成本最低。方法 考虑的成本包括库存持有成本、缺货成本和配送成本。为降低供应链系统的总成本，提出全新的周期可变策略。结果 采用遗传算法求解得出最佳方案。将不同策略的算例结果进行对比分析，结果表明周期可变策略与周期固定策略相比，系统总成本最低节约比例为1.7%，最高节约比例为42.3%。结论 通过对计划期内各配送周期的长度及数量进行划分调整可以有效地节约系统总成本，同时，采用多车型的配送方案明显优于采用同车型的配送方案。     

Optimal pricing policy for a deteriorating product by dynamic tracking control

We study the optimal selling price of a deteriorating product under a deterministic situation in a finite time horizon where the time horizon is either known or unknown. Inventory holding cost is expressed as a quadratic function of the current inventory level. Given a known time horizon, we develop a model by considering the deterioration dynamics of the product, and show its equivalence to a generalised optimal control problem of a linear quadratic form, i.e. an optimal dynamic tracking problem with constraints on the control variable. An optimal pricing policy is derived based on the maximum value principle. The control policy takes a state feedback form; it exhibits a closed-loop relationship between the optimal selling price (control variable) and the optimal inventory level (state variable). Given an unknown time horizon, an optimal pricing policy is derived through a similar approach when the initial inventory level meets certain conditions. Numerical situations are conducted to illustrate the effectiveness of the derived price control policies. Some interesting managerial insights are discussed.     

Optimising replenishment policy for an integrated production inventory deteriorating model considering green component-value design and remanufacturing

This paper develops an integrated deteriorating production inventory model with green component design, remanufacturing and JIT deliveries. We provide a rigorous analysis to derive the number of deliveries, the optimal cycle time of deliveries, and the delivery sise for the integrated buyer-supplier inventory deteriorating model. Distinct from the former concept of average inventory level, our paper proposes a significantly different approach to deal with the first production batch and uses a revised method to approximate the relationship between the supplier's production and storage time. A manufacturing case example of Taiwan computer power-supply component producers is presented to illustrate the theory. It is shown that the parameters of component-value design and unit holding cost are the critical factors affecting the deteriorating inventory planning.     

A strategic model for exact supply chain network design and its application to a global manufacturer

This paper presents a comprehensive model that captures significant strategic decisions involved in designing or redesigning high-performance supply chains from the perspective of the manufacturer. The problem considers deterministic demand by multiple clients, for multiple products, over the periods of a long-term horizon. The design decisions involve selection of suppliers, establishment or resizing of production facilities and distribution centres, possible subcontracting of related activities, and selection of transportation modes and routes. The problem is formulated by a Mixed Integer Linear Programming model. Its objective is to minimise the overall costs associated with procurement, production, inventory, warehousing, and transportation over the design horizon. Appropriate constraints model the complex relationships among the links of the supply chain. The proposed model has been applied to a large case study of a global manufacturing firm, providing valuable insights into the transformation of the firm’s current supply chain network, as well as into the potential of the proposed approach.     

A dynamic model for serial supply chain with periodic delivery policy

A three-echelon, dynamic lot-sizing model is presented for computing the parameters of a serial supply chain, which consists of integrated material replenishment, production at a capacity constraint, and outbound dispatching with a periodic delivery policy. The model is applicable in the general context of a three-echelon model with capacity constraint at the middle echelon of the serial supply chain which was not considered in most of the literature. This paper addresses a number of structural properties of the model and proposes a polynomial-time algorithm for deriving the optimal solution of minimising the total system costs over the planning horizon. Examples are provided to describe the algorithm.     

运输能力柔性约束的供应链Pareto优化策略

针对单位产品运输成本对批量敏感并由生产商负责产品运输的情况,建立了供需双方在分散决策和集中决策情形下的最佳批量模型.分析结果表明运输能力柔性越强,生产商的最佳生产批量越小,与批发商要求的短周期、小批量订货越接近,从而在不增加成本的情况下,生产商能够对批发商的需求快速响应.且当运输能力非完全柔性时,生产商可以通过降低产品转让价格改让批发商负责产品运输,以使批发商在分散决策情形下的最佳订货批量更接近于生产商的最佳生产批量和供应链在集中决策情形下的订货批量,从而在供需方双赢的前提下实现整个供应链的利益优化.     

面向供应链的制造业库存决策支持系统的分析与设计

从制造型企业自身的特点出发,结合供应链环境下制造商库存管理的对象和方法,在分析库存决策机制的基础上,设计了供应链环境下面向制造业的库存决策支持系统,并讨论了本系统数据仓库的设计,模型库的组织与存储,以及所采用的数据挖掘方法等关键技术.本文所设计的库存决策支持系统适用于大型制造型企业,有助于提高供应链环境下企业的库存管理水平.