Integrated inventory valuation in multi-echelon production/distribution systems

The pressure to reduce inventory has increased as competition expands, product variety grows, and capital costs increase. This investigation addresses the problem of inventory quantification and distribution within multi-echelon supply chains under market uncertainty and management flexibility. This approach is based on an optimisation model emphasising demand uncertainty and the relevant dimensions of network design as number of echelons, lead time, service level, and cost of processing activities. Overstock quantification enables the understanding of inventory level sensitivity to market uncertainty. A comparison among production sites and storage facilities revealed that higher downstream overstock levels decrease upstream echelons of uncertainty exposition. The contribution of this study relies on management's ability to establish inventory targets for each stocking point according to risk exposure and to promote the optimisation of working capital. Overall, this investigation increases knowledge related to the treatment of demand uncertainty in flexible and integrated supply chains     

A multiperiod stochastic production planning and sourcing problem with service level constraints

We study a stochastic multiperiod production planning and sourcing problem of a manufacturer with a number of plants and/or subcontractors. Each source, i.e. each plant and subcontractor, has a different production cost, capacity, and lead time. The manufacturer has to meet the demand for different products according to the service level requirements set by its customers. The demand for each product in each period is random. We present a methodology that a manufacturer can utilize to make its production and sourcing decisions, i.e., to decide how much to produce, when to produce, where to produce, how much inventory to carry, etc. This methodology is based on a mathematical programming approach. The randomness in demand and related probabilistic service level constraints are integrated in a deterministic mathematical program by adding a number of additional linear constraints. Using a rolling horizon approach that solves the deterministic equivalent problem based on the available data at each time period yields an approximate solution to the original dynamic problem. We show that this approach yields the same result as the base stock policy for a single plant with stationary demand. For a system with dual sources, we show that the results obtained from solving the deterministic equivalent model on a rolling horizon gives similar results to a threshold subcontracting policy. Correspondence to: Fikri KaraesmenThe authors are grateful to Yves Dallery for his ideas, comments and suggestions on the earlier versions of this paper.     

A dynamic multi-plant lot-sizing and distribution problem

In this paper, we investigate a multi-plant, production planning and distribution problem for the simultaneous optimisation of production, inventory control, demand allocation and distribution decisions. The objective of this rich problem is to satisfy the dynamic demand of customers while minimising the total cost of production, inventory and distribution. By solving the problem, we determine when the production needs to occur, how much has to be produced in each of the plants, how much has to be stored in each of the warehouses and how much needs to be delivered to each customer in each period. On a large real data-set inspired by a case obtained from an industrial partner, we show that the proposed integration is highly effective. Moreover, we study several trade-offs in a detailed sensitivity analysis. Our analyses indicate that the proposed scenarios give the company competitive advantage in terms of reduced total logistics cost, and also highlight more possibilities that become available taking advantage of an integrated approach towards logistics planning. These abundant opportunities are to be synergised and exploited in an interconnected open global logistics system.     

Optimal batch ordering policies for assembly systems with guaranteed service

For a supply chain modelled as a multi-echelon inventory system, effective management of its inventory at each stock is critical to reduce inventory costs while assuring a given service level to customers. In our previous work, we used the guaranteed-service approach (GSA) to design optimal echelon batch ordering policies for continuous-review serial systems with Poisson customer demand and fixed order costs. The approach assumes that the final customer demand is bounded and each stock has a guaranteed service time in the sense that the demand of its downstream stock can always be satisfied in the service time. This paper extends this work by considering more general assembly systems. We first derive an analytical expression for the total cost of the system in the long run. The problem of finding optimal echelon batch ordering policies for the system can then be decomposed into two independent sub-problems: order size decision sub-problem and reorder point decision sub-problem. We develop efficient dynamic programming algorithms for the two sub-problems. Numerical experiments on randomly generated instances show the effectiveness of the proposed approach.     

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.     

Innovative vendor-managed inventory strategy exploiting interconnected logistics services in the Physical Internet

Traditional supply chain networks are often designed in the interests of a company. Once the network has been defined, the storage and distribution of goods are usually fixed and restricted within the network. This is assumed to be an inherent limit of current inventory control research. Instead of specialised hierarchical storage networks, this paper proposes an innovative vendor-managed inventory strategy exploiting the Physical Internet (PI), which is an open, universal, interconnected logistics system. In such a system, facilities and means of transport are shared and can be allocated according to demands of users. As a result, the PI allows users to stock anywhere in the network and also provides open multisourcing options for orders with on-demand warehousing services within the PI. Inventory decisions can be made dynamically by each player to minimise networkwide inventory levels. A non-linear, simulation-based optimisation model was developed for the vendors’ inventory decision-making when confronted with stochastic demands. A metaheuristic using simulated annealing was applied to solve the problem, and then, the optimised inventory decisions were validated using simulation. The results suggest that the proposed PI inventory model can reduce the total logistics cost while maintaining a comparable or better level of end customers’ services.     

Mitigating supply chain disruptions through interconnected logistics services in the Physical Internet

This paper investigates the resilience of inventory models using interconnected logistics services in the Physical Internet (PI). With traditional supply chain network design, companies define and optimise their own logistics networks, resulting in current logistics systems being a set of independent heterogeneous logistics networks. The concept of PI aims to integrate independent logistics networks into a global, open, interconnected system. Prior research has shown that new inventory models enabled by and applied to PI could help reduce inventory levels thanks to its high flexibility. Continuing along these lines, this paper examines how inventory models applying PI deal with disruptions at hubs and plants. To attain this, a single product inventory problem with uncertain demands and stochastic supply disruptions is studied. A simulation-based optimisation model is proposed to determine inventory control decisions. The results suggest that the PI inventory model, with greater agility and flexibility, outperforms the current classic inventory models in terms of resilience. Moreover, the difference in performance increases when the product value, penalty costs and disruption frequency increases. This paper indicates a novel approach to build a resilient supply network.     

多级库存控制的连锁经营企业配送网络设计

研究了随机需求条件下连锁经营企业配送网络设计及其库存决策的联合优化问题.详细分析了基于POT(power of two)多级库存控制策略的连锁企业多级工作库存及订货成本,给出了门店及配送中心在满足给定服务水平条件下的安全库存成本.在综合考虑运输成本和配送中心选址成本的基础上,建立了以系统总成本最小为目标的配送系统总成本优化模型,并采用遗传算法求解该优化模型,在得到最优配送网络设计方案的同时,确定了配送中心订货周期及门店配送周期.通过算例验证了模型及算法的有效性,并分析了需求、运输距离和选址成本等因素的变化对系统总成本的影响,为连锁经营企业的物流配送网络设计及库存控制提供决策支持.     

Evaluating long-term service performance under short-term forecast updates

Managing product availability in a cost-effective way has always been a major challenge faced by inventory managers. We study the problem of a firm selling a perishable product with short-term demand patterns and a long-term service target using the newsvendor framework. The newsvendor determines his long-term order at the first stage, and revises the order according to a short-term forecast update at the second stage. He also evaluates a long-term service target for his overall performance on product availability across all possible forecast updates. We characterise his optimal inventory policy that minimises the expected inventory cost while meeting the long-term service target. Both in-stock rate and fill rate targets are examined.     

Quality decision of the logistics service supply chain with service quality guarantee

In the logistics service supply chain (LSSC), quality supervision and coordination is an important approach that can ensure the effective operation of LSSC and obtain more customers. This paper aims to study the quality decisions of the functional logistics service provider (FLSP) and the logistics service integrator (LSI) when a service quality defect guarantee is promised by the FLSP. Assuming that customer demand is a function of the quality defect guarantee of the FLSP, the optimal quality decisions of the LSI and FLSP are presented under three typical game modes: Nash game, Stackelberg game, and centralised decision. Numerical analysis is conducted to show how the quality parameters of the customer affect the optimal quality decision-making behaviour of the LSI and FLSP. The research results indicate that, under all three game modes, the optimal quality defect guarantee of the FLSP increases as the customer punishment increases, and decreases as the elasticity of the customer demand for the quality defect guarantee increases. In addition, under all three game modes, the optimal quality supervision effort of the LSI is an increasing function of both customer punishment and elasticity of the customer demand for the quality defect guarantee.     

Optimal collaborative transportation service trading in B2B e-commerce logistics

This paper investigates a less-than-truckload carrier collaboration decision-making problem in the e-commerce logistics network. E-commerce less-than-truckload carrier collaboration problem considers multiple logistics service providers (LSPs) forming a collaborative alliance in an e-commerce logistics network. They share their transportation requests and vehicle capabilities to maximise the total profit of the entire alliance, improve their vehicle utilisation and cope with fluctuations in demand. An e-commerce logistics trading system with collaborative decisions is designed. A collaborative transportation planning model is introduced to maximise the total profit without reducing the individual profit of the carriers with information sharing. A stochastic plant-pollinator algorithm is proposed for the problem and extensive computational experiments are conducted. The results show that the proposed plant-pollinated algorithm performs better than the genetic algorithm. Furthermore, the results illustrate that the higher degree of cooperation, the more benefits for carriers. Last but not least, since the increasing gasoline price leads to the decreasing margins for the small- and medium-sized LSPs. The results also show that it is critical for them to join in the alliance to survive in the competition.     

Can the desired service level be achieved when the demand and lost sales are unobserved?

In this paper we consider an inventory management problem in the retail industry with unobserved lost sales. The retailer does not know the demand for a particular product but she has access to Point-Of-Sale (POS) data. The retailer uses a fixed review period, order-up-to level system to control the inventory. The objective of the retailer is to achieve a pre-specified service level. We define the service level as the fraction of demand satisfied from inventory. However, due to the unobserved lost sales nature of the problem, the retailer cannot exactly measure the current service level. We propose a POS data-based mechanism for periodic updating of the order-up-to level. We show that the periodic updating approach yields the desired service level without using any inventory information. Once the periodic updating scheme converges, it also gives the actual demand distribution.     

考虑仓储与运输一体化的物流系统运输方式选择

为解决运输与库存成本存在“二律背反”难以实现总物流成本最小的问题，建立了集仓储与运输一体的物流经济运输批量模型，对模型进行分析求解。研究发现，仓储和运输一体化系统最优成本是关于物流需求率的分段函数；当装载量无限大，铁路的规模运输效应优于公路运输；当装载量一定时，选择何种运输方式应该综合考虑物流需求率与装载量、单位运输成本和库存成本之间的关系。本文的结论对承运方选择运输方式提供了理论参考。     

Warehouse location and two-echelon inventory management with concave operating cost

In this paper, we study a location–inventory network design problem which jointly optimises the warehouse location, the warehouse–retailer assignments, the warehouse–retailer echelon inventory replenishment and the safety stock-level decisions over an infinite planning horizon. The consideration of the facility operating cost, the safety stock cost and the two-echelon inventory cost results in an MIP model with several nonlinear terms. Due to the complex trade-offs among the various costs and multiple nonlinear terms in the model, traditional solution approaches no longer work for this problem. We outline a polymatroid cutting-plane approach based on the submodular property of the cost terms to address this problem. Computational results demonstrate that the cutting-plane method based on polymatroid inequalities can efficiently solve randomly generated instances with moderate sizes.     

A single-product network design model with lead time and safety stock considerations

Most existing network design and facility location models have focused on the trade-off between the fixed costs of locating facilities and variable transportation costs between facilities and customers. However, operational performance measures such as service levels and lead times are what motivates customers to bring business to a company and should be considered in the design of a distribution network. While some previous work has considered lead times and safety stocks separately, they are closely related in practice, since safety stocks are often set relative to the distribution of demand over the lead time. In this paper we consider a two-stage supply chain with a production facility that replenishes a single product at retailers. The objective is to locate Distribution Centers (DCs) in the network such that the sum of the location and inventory (pipeline and safety stock) costs is minimized. The replenishment lead time at the DCs depends on the volume of flow through the DC. We require the DCs to carry enough safety stock to maintain the prescribed service levels at the retailers they serve. The explicit modeling of the relationship between the flows in the network, lead times and safety stocks allows us to capture the trade-off between them. We develop a Lagrangian heuristic to obtain near-optimal solutions with reasonable computational requirements for large problem instances.     

Supply chain optimisation with assembly line balancing

Supply chain management operates at three levels, strategic, tactical and operational. While the strategic approach generally pertains to the optimisation of network resources such as designing networks, location and determination of the number of facilities, etc., tactical decisions deal with the mid-term, including production levels at all plants, assembly policy, inventory levels and lot sizes, and operational decisions are related to how to make the tactical decisions happen in the short term, such as production planning and scheduling. This paper mainly discusses and explores how to realise the optimisation of strategic and tactical decisions together in the supply chain. Thus, a supply chain network (SCN) design problem is considered as a strategic decision and the assembly line balancing problem is handled as a tactical decision. The aim of this study is to optimise and design the SCN, including manufacturers, assemblers and customers, that minimises the transportation costs for determined periods while balancing the assembly lines in assemblers, which minimises the total fixed costs of stations, simultaneously. A nonlinear mixed-integer model is developed to minimise the total costs and the number of assembly stations while minimising the total fixed costs. For illustrative purposes, a numerical example is given, the results and the scenarios that are obtained under various conditions are discussed, and a sensitivity analysis is performed based on performance measures of the system, such as total cost, number of stations, cycle times and distribution amounts.     

Locating service facilities to reduce lost demand

We analyze the problem of locating a set of service facilities on a network when the demand for service is stochastic and congestion may arise at the facilities. We consider two potential sources of lost demand: (i) demand lost due to insufficient coverage; and (ii) demand lost due to congestion. Demand loss due to insufficient coverage arises when a facility is located too far away from customer locations. The amount of demand lost is modeled as an increasing function of the travel distance. The second source of lost demand arises when the queue at a facility becomes too long. It is modeled as the proportion of balking customers in a Markovian queue with a fixed buffer length. The objective is to find the minimum number of facilities, and their locations, so that the amount of demand lost from either source does not exceed certain pre-set levels. After formulating the model, we derive and investigate several different integer programming formulations, focusing in particular on alternative representations of closest assignment constraints. We also investigate a wide variety of heuristic approaches, ranging from simple greedy-type heuristics, to heuristics based on time-limited branch and bound, tabu search, and random adaptive search heuristics. The results of an extensive set of computational experiments are presented and discussed.     

库存随机折损的季节性商品服务策略和库存控制

研究一类随机需求环境下库存随机折损的季节性产品服务策略和库存控制问题.提出按照客户购买行为特征对需求进行分类,并在不同类型的客户之间进行差别化服务的策略.通过构建动态规划模型,得出动态的最优服务和库存控制策略.同无差别服务的报童模型相比,该策略能显著提升零售商的利润,减少商品损坏的损失.利润提高率与零售商对客户的区分力...     

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.     

Managing two-stage serial inventory systems under demand and supply uncertainty and customer service level requirements

We consider a two-echelon serial inventory system with demand and supply uncertainty, non-zero lead times for component procurement and end-product assembly, and a minimum customer service level requirement. We present two supply models which incorporate both quantity and timing uncertainty; these models correspond to current and proposed supply environments. Assuming that installation base-stock ordering policies are followed and that the demand distribution is quasi-concave, we show that the chance-constrained problem of determining optimal base-stock levels which minimize the total inventory investment (cost-weighted stock levels) subject to a service constraint is a convex programming problem. We characterize the relation between the optimal base-stock levels of the component and the end-product. We also illustrate how an optimal internal (component) service level can be computed, which permits decomposition of the two-stage serial system into two coordinated single-echelon systems. Computational experiments illustrate insights on the effects of supply uncertainty and other problem parameters on stock-positioning in a two-echelon serial system. In particular, we evaluate the benefits of switching from one supply environment to another.