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.     

Benefits of considering inventory in service parts logistics network design problems with time-based service constraints

We study the integrated logistics network design and inventory stocking problem as characterized by the interdependency of the design and stocking decisions in service parts logistics. These two sets of decisions are usually considered sequentially in practice, and the associated problems are tackled separately in the research literature. The overall problem is typically further complicated due to time-based service constraints that provide lower limits on the percentage of demand satisfied within specified time windows. We introduce an optimization model that explicitly captures the interdependency between network design (location of facilities, and allocation of demands to facilities) and inventory stocking decisions (stock levels and their corresponding stochastic fill rates), and present computational results from our extensive experiments that investigate the effects of several factors including demand levels, time-based service levels and costs. We show that the integrated approach can provide significant cost savings over the decoupled approach (solving the network design first and inventory stocking next), shifting the whole efficient frontier curve between cost and service level to superior regions. We also show that the decoupled and integrated approaches may generate totally different solutions, even in the number of located facilities and in their locations, magnifying the importance of considering inventory as part of the network design models.     

存货质押委托监管模式下基于双边道德风险的收益分配模型研究

存货质押融资作为一项多主体的融资业务,道德风险是其不可忽视的一种重要风险。在存货质押委托监管模式的初始授信环节,银行与物流企业的努力程度为隐匿信息,因此银行与物流企业双方都有可能存在道德风险问题,收益分配作为一种激励机制,可以对双方道德风险起到一定约束作用。本文在存货质押融资委托监管模式下,基于委托代理理论,研究了考虑初始授信环节中银行与物流企业存在双边道德风险时的收益分配问题,建立了银行与物流企业的收益分配模型,推导出银行与物流企业间的最优收益分配比例,以防范银行与物流企业的双边道德风险。最后通过数值算例进一步对收益分配比例随物流企业实力变动的趋势进行了分析。     

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.     

区域物流网络中多配送中心的库存需求预测模型

研究了某区域物流网络中由m个配送中心、n个零售商组成的配送网络的需求预测问题。分析了区域物流网络的需求特点,运用Dijkstra算法得出最短路径矩阵,在此基础上构建了库存需求预测模型,通过该模型求得每个配送中心的需求总量,并以实例验证了该模型的正确性和可行性。     

An integrated model for the production-inventory-distribution problem in the Physical Internet

In contrast to traditional supply chain networks, Physical Internet (PI) is an interconnected open global logistics network based on open PI hubs and standard PI-containers that has the potential to achieve ground-breaking improvements in integrated production-inventory-distribution management. In this paper, to quantify the advantages of PI from a cost performance perspective, we propose a mixed-integer linear programming (MILP) formulation for addressing the problem that combines an integrated production-inventory-distribution decision with PI, which has been addressed separately in the existing literature. The results of computational experiments show that while achieving a comparable or better service level, PI can achieve significant cost savings compared to a traditional supply chain network with a dynamic configuration and a hybrid configuration. Moreover, we investigate the impact of several problem parameter changes on the total costs under each network setting, and managerial insights are derived.     

An inventory model with emergency orders under explicit energy cost considerations

The inventory model in this paper is targeted to production systems with constant production rates but underlying possibilities for undesirable circumstances to threaten the production schedule. The inventory policy proposed explicitly considers energy cost when determining optimal size for order quantity, safety stock and inventory cycle length such that the total expected cost per unit time is minimised. The results are compared to a traditional inventory policy that does not consider the direct impact of energy cost. An analysis of the model reveals three production environment characteristics in which inventory policies are most significantly affected by changes in energy cost: heavy product weight, high regular product demand or high emergency product demand. If any one of the three key factors increases, then changes of the inventory decisions or related logistics costs become more significant. The cost effectiveness of implementing the proposed inventory policy also becomes more significant as any one of the three key factors increase with respect to energy cost.     

Interconnected logistic networks and protocols: simulation-based efficiency assessment

Logistic networks intensely use means of transportation and storage facilities to deliver goods. However, these logistic networks are still poorly interconnected and this fragmentation is responsible for a lack of consolidation and thus efficiency. To cope with the seeming contradiction of just-in-time deliveries and challenging emissions targets, a major improvement in supply networks is sought here. This new organisation is based on the universal interconnection of logistics services, namely a Physical Internet where goods travel in modular containers for the sake of interconnection in open networks. If from a logical point of view, merging container flows should improve efficiency, no demonstration of its potential has been carried out prior to the here reported research. To reach this potentiality assessment goal, we model the asynchronous shipment and creation of containers within an interconnected network of services, find the best path routing for each container and minimise the use of transportations means. To carry out the demonstration and assess the associated stakes, we use a set of actual flows from the fast-moving consumer goods sector in France. Various transportation protocols and scenarios are tested, revealing encouraging results for efficiency indicators such as CO₂ emissions, cost, lead time, delivery travel time, and so forth. As this is a first work in the field of flows transportation, the simulation model and experiment exposes many further research avenues.     

Robust optimisation for risk-averse multi-period inventory decision with partial demand distribution information

We study robust multi-period inventory decisions for risk-averse managers with incomplete demand information for products with a short life cycle. The three inventory models we developed aim respectively to maximise expected profit, maximise conditional value-at-risk-based profit, and balance between the two objectives. We formulate each objective into an associated robust counterpart model under the assumption of ellipsoid distribution and again under the box distribution. The ellipsoid distribution-based robust model can be mathematically transformed into a non-linear programming which can be solved by finding solutions to some second-order cone programs, while the box distribution-based model can be converted into a general piecewise linear optimisation problem. We prove that the transformed versions are equivalent to the original ones and that both transformed models can be solved efficiently. Numerical examples are given to demonstrate the practicability of the proposed approach for dealing with uncertain demands. We find that the proposed optimisation approaches are robust under both the ellipsoid and box distributions. Finally, sensitivity analysis on the risk-averse degree and optimism index is conducted to validate the proposed models and solution approaches.     

Network positioning and risk perception in servitization: evidence from the UK road transport industry

For manufacturing firms, the integration of advanced services into their customer offerings has become a crucial decision. Such commercial decisions require weighting the risks and rewards of implementing a business model based on advanced services. While academic experts acknowledge uncertainty of returns on investment despite potential advantages, research generally fails to address the challenge of calculating the actual risks involved in ‘servitization’. This paper seeks better understanding of managers’ risk perception and of servitization implications for strategic partnerships and network positioning, while considering the impact of factors such as entry barriers, technological knowledge and position in the supply chain (SC). Qualitative evidence is drawn from an industrial case study involving firms in the UK’s road transport industry: 14 in-depth interviews with senior executives from seven companies (manufacturers, operators, technology providers). During interviews, a payment card exercise measured risk perception and willingness to take strategic ‘make-or-buy’ decisions. Results suggest that implementing advanced services is perceived as a high-risk strategy, especially when firms lack in-house technological knowledge. However, collaborative strategic partnerships within supply chain networks can mitigate this risk and prove crucial to building entry barriers against external competitors. Based on these findings, implications for network positioning are developed.     

Stockout propagation and amplification in supply chain inventory systems

We investigate the existence and magnitude of stockout propagation and stockout amplification in the context of supply chain inventory systems. Stockout amplification is a stage-to-stage increase in overall stockout rates. Stockout propagation is the tendency for stockout at one node to instigate a stockout at a neighbouring node and is conceptually related to the idea of cascading failures in physical systems, such as electrical power grids. We study these concepts in both upstream (‘supply side’) and downstream (‘demand side’) directions in the context of normal operating conditions for an adaptive R, S (periodic, order-up-to) inventory policy. We build a simulation model of a 5-stage serial supply chain that experiences normally distributed customer demands and gamma distributed lead times. We find that stockout propagation exists, but contrary to conventional wisdom, it occurs in the upstream direction. There is little indication that stockout propagation is occurring to any significant degree in the downstream direction. We also find stockout amplification occurring in the upstream direction in scenarios where more aggressively adaptive inventory parameter updating is performed. We discuss implications of this work in the areas of supply chain inventory modelling, ordering decisions, safety stock determination, and the use of adaptive inventory policies.     

Dynamic lot sizing with returning items and disposals

We analyze a version of the Dynamic Lot Size (DLS) model where demands can be positive and negative and disposals of excess inventory are allowed. Such problems arise naturally in several applications areas, including retailing where previously sold items are returned to the point of sale and re-enter the inventory stream (such returns can be viewed as negative demands), and in managing kits of spare parts for scheduled maintenance of aircraft (where excess spares are returned to the depot)among other applications. Both the procurement of new items and the disposal of excess inventory decisions are considered within the framework of deterministic time-varying demands, concave holding, procurement and disposal costs and a finite time horizon (disposal of excess inventory at a profit is also allowed). By analyzing the structure of optimal policies, several useful properties are derived, leading to an efficient dynamic programming algorithm. The new model is shown to be a proper generalization of the classical Dynamic Lot Sizing Model, and the computational complexity of our algorithm is compared with that of the standard algorithms for the DLS model. Both the theoretical worst-case complexity analysis and a set of computational experiments are undertaken. The proposed methodology appears to be quite adequate for dealing with realistic-sized problems.     

考虑修复成本的逆向物流多级库存优化研究

通过对逆向物流多级库存的提法引入,提出了逆向物流多级库存总成本最小化问题．在对产品召回的条件下回收系统模型图简化的基础上,提出了考虑修复成本的逆向物流的多级库存成本优化模型,并用MATLAB编程计算实际算例,得出总成本的优化值．研究结果表明,考虑了修复成本的逆向物流多级库存成本优化更具实际应用意义．     

An optimal consignment stock production and replenishment policy with controllable lead time

This paper considers a single-vendor and single-buyer production system in which the lead-time is controllable with an extra investment under a long-term agreement between the two trading partners. The vendor produces at a finite rate, ships the outputs in lots of equal size within a production cycle, and delays those shipments for a certain period when the buyer’s inventory approaches the capacity limits. Therefore, the arrival of these shipments does not lead to an increase in the buyer’s inventory. Meanwhile, the buyer holds the payment until the complete consumption of the products. The holding cost consists of a storage component and a financial component. A joint EOQ/EPQ model is then established under cases where the buyer’s unit storage holding cost might be greater or less than that of the vendor to jointly determine the number of shipments, the size of each shipment, the number of delayed shipments, and the lead time that minimise the yearly joint total expected cost (JTEC) of the system. An efficient solution procedure is provided to solve the non-linear integer optimisation model that defined the system under consideration. A method to determine the integer global optima from the real global optima is also presented. Two numerical experiments are conducted to illustrate the procedure and the results show that considering the combined effect of adopting a consignment stock policy and lead time crashing opportunities may lead to a better result than any of these two policies considered separately.     

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.     

需求受相关性影响的设备备件库存优化

备件多级库存模型通常基于备件需求相互独立的假设，但随着库存系统层次的增加以及协同管理方式的应用，备件需求的相关性将显著影响库存优化决策。针对需求具有相关性的备件库存问题，以服务响应时间为约束条件，以库存成本及缺货成本最小化为目标建立备件两级库存决策模型。引入Nataf概率变换法，利用已知的备件需求边缘概率密度函数构造满足特定相关性条件的随机需求样本，并将蒙特卡洛仿真与遗传算法相结合求解最优库存分配方案。仿真算例证明，设备备件库存的最优决策随着需求相关性系数的增大而发生变化，根据需求相关性的变化适当地调整库存决策，有利于降低备件库存系统总成本，提高库存系统对顾客需求的响应能力。     

Supply chain design to guarantee quoted lead time and inventory replenishment: model and insights

Do lead time constraints only lead to re-think and re-optimise the inventory positioning along the supply chain or can they impact on the design of the supply chain itself? To answer such a question, we integrate the lead time constraints in a multi-echelon supply chain design model and challenge the difficulty of combining in the same model the long-term decisions (facility location, supplier selection) with the midterm decisions (inventory placement and replenishment, delivery lead time). The model guarantees the respect of the quoted lead time associated with each customer order and the replenishment of the different stocks (raw materials, intermediate and final products) in the different stages of the supply chain between any pair of consecutive orders. We use the model to investigate the impact of the quoted lead time and customer’s order frequency on supply chain design decisions and costs. Some of our results indicate that the lead time constraints can lead to bringing the sites of manufacturing and distribution close to the demand zone and to select local suppliers in spite of their higher cost.     

Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors

Physical Internet (PI, π) has been widely used for transforming and upgrading the logistics and supply chain management worldwide. This study extends the PI concept into manufacturing shop floors where typical logistics resources are converted into smart manufacturing objects (SMOs) using Internet of Things (IoT) and wireless technologies to create a RFID-enabled intelligent shop floor environment. In such PI-based environment, enormous RFID data could be captured and collected. This study introduces a Big Data Analytics for RFID logistics data by defining different behaviours of SMOs. Several findings are significant. It is observed that task weight is primarily considered in the logistics decision-making in this case. Additionally, the highest residence time occurs in a buffer with the value of 12.17 (unit of time) which is 40.57% of the total delivery time. That implies the high work-in-progress inventory level in this buffer. Key findings and observations are generated into managerial implications, which are useful for various users to make logistics decisions under PI-enabled intelligent shop floors.     

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     

Inventory performance of some supply chain inventory policies under impulse demands

This paper attempts to study the impact of impulsive demand disturbances on the inventory-based performance of some inventory control policies. The supply chain is modelled as a network of autonomous supply chain nodes. The customer places a constant demand except for a brief period of sudden and steep change in demand (called demand impulse). Under this setting, the behaviour of each inventory policy is analysed for inventory performance of each node. It is found that the independent decision-making by each node leads to a bullwhip effect in the supply chain whereby demand information is amplified and distorted. However, under a scenario where the retailer places a constant order irrespective of the end customer demand, the inventory variance was actually found to decrease along the supply chain. The variance of the inventory remained constant along the chain when only the actual demands are transmitted by each node. The results also showed that the inventory policy which is best for one supply chain node is generally less efficient from a supply chain perspective. Moreover, the policy which performs poorly for one node can be most efficient for the supply chain. In a way, our results also provide a case for coordinated inventory management in the supply chain where all members prepare a joint inventory management policy that is beneficial for all the supply chain nodes. The results have significant industrial implications.