Inventory Decisions Research Articles

Inventory routing problem with backhaul considering returnable transport items collection

The Inventory Routing Problem (IRP) has been highlighted as a valuable strategy for tackling routing and inventory problems. This paper addresses the IRP but considers the forward delivery and the use of Returnable Transport Items (RTIs) in the distribution strategy. We develop an optimization model by considering inventory routing decisions with RTIs collection (backhaul customers) of a Closed-Loop Supply Chain (CLSC) within a short-term planning horizon. RTIs consider reusable packing materials such as trays, pallets, recyclable boxes, or crates. The RTIs represent an essential asset for many industries worldwide. The solution of the model allows concluding that if RTIs are considered for the distribution process, the relationship between the inventory handling costs of both the final goods and RTIs highly determines the overall performance of the logistics system under study. The obtained results show the efficiency of the proposed optimization scheme for solving the combined IRP with RTIs, which could be applied to different real industrial cases.

An Ambient Media Advertising Order Snatch System Based on Price Stepping Algorithm

With the rapid development of modern information society, all types of advertisements emerge in an endless flow, where ambient media that emphasizes the multi-sensory experience of users rise as the times require. In this paper, based on the price stepping algorithm, a new ambient media advertising model based on O2O that integrates offline large-screen resources is proposed to further promote the development of ambient media in modern society. By introducing the price stepping (PPA) algorithm for sales and management analysis and sensitivity evaluation of actual goods, we aim to dynamically solve the marketing and inventory decision problem of environmental media advertising products, and on this basis, we use the price stepping (PPA) algorithm to make sales forecasts for high-cost goods, provide decision support for inventory and theoretical balance, and achieve theoretical maximization of environmental media advertising. The simulation results show that the PPA algorithm provides a more accurate sales forecast for high-cost goods, which in turn provides a joint decision idea to achieve inventory optimization and profit maximization, and provides an optimization method for merchants to balance inventory costs and marketing costs to maximize profits.

Pricing and inventory Decision Optimization under subscription mode in Supply chain management

This paper investigates the current situation of subscription mode, applies it to the joint management of pricing and inventory in supply chain management, builds the product pricing and inventory model under subscription mode, and verifies the model derivation and results based on the joint decision model of pricing and inventory, and simulates the decision-making process of the model by computer programming. By comparing the difference between the theoretical model of joint decision of pricing and inventory under uncertain supply and demand conditions and the model of joint decision of pricing and inventory under subscription mode, analyzing the influencing factors and exploring the optimization method of product pricing and inventory model algorithm under subscription mode. The results show that: (1) in supply chain management, it is necessary to solve the problem of supply and demand balance through effective joint decision of pricing and inventory, and effectively improve the competitiveness of logistics enterprises.(2) The introduction of subscription mode in the joint management of pricing and inventory can reduce the uncertainty of both sides, so as to better solve the problem of inventory replenishment strategy and pricing decision under the condition of supply and demand uncertainty than the traditional decision-making model, so as to achieve the coordination of supply chain under the uncertain environment.

A vendor-managed inventory model based on optimal retailers selection and reliability of supply chain

<p style='text-indent:20px;'>One of the most common and successful approaches for the integrated supply chain management (SCM) is the vendor-managed inventory (VMI). In VMI, a vendor takes control of the inventory decisions for retailers. To establish a long-run relationship between the vendor and the retailer, it is necessary to consider two impactful factors: the vendors and retailers' reliability, and the optimal selection of retailers. For this purpose, the redundancy allocation problem (RAP), as an effective technique for increasing the reliability of vendors, is used in this paper. Also, the reliability of retailers as well as the reliability of the relationship between retailers and vendors are considered. In the retailer selection, process decisions on impactful criteria are simultaneously considered. For the retailers' selection, one analysis hierarchical process (AHP) is performed for each vendor, and the weights of retailers are obtained. Then, the obtained weights are plugged into the model as the inputs of the designed model. Since the developed model is a non-differentiable, non-convex, and mixed-integer function, genetic algorithm (GA) and particle swarm optimization (PSO) are leveraged to solve the formulated model. Finally, the efficiency of the presented method is verified through a case study with data collected from the electronic supply chain.</p>

A hybrid differential evolution algorithm for a location-inventory problem in a closed-loop supply chain with product recovery

Product recovery is an important business because of its great economic, social, and environmental benefits in practice. In this paper, a location-inventory problem (LIP) in a closed-loop supply chain (CLSC) is investigated to optimize facility location and inventory control decisions by considering product recovery. The objective is to optimize facility location and inventory control decisions to minimize the total cost of business operations in a closed-loop supply chain system. We formulate this problem as a mixed-integer nonlinear programming model and design a modified hybrid differential evolution algorithm (MHDE) to solve it efficiently. Finally, numerical results are presented to validate the performance of the new algorithm. The results show that MHDE is more efficient and effective than Lingo and other algorithms for the research problem under study. Managerial insights are also derived for business managers to improve their supply chain performance.

Optimizing a multi-echelon location-inventory problem with joint replenishment: A Lipschitz [formula omitted]-optimal approach using Lagrangian relaxation

This paper investigates a supply network design problem for cross-border e-commerce. The supply chain consists of suppliers, regional distribution centers, and distribution centers. Our goal is to determine simultaneously location decisions of regional distribution centers, and the inventory decisions for coordinated replenishment and delivery. We formulate this problem as a mixed-integer non-linear program, and develop a Lipschitz optimization algorithm and an iterative heuristic to solve it. Moreover, we propose a tight lower bound via constructing a dual problem. Computational results show that both the Lipschitz optimization method and the heuristic can produce a near-optimal solution with a gap below 1%. Finally, we analyze the influence of integrated decision making and capacity constraints. An interesting finding is that the capacity of regional distribution centers has a critical influence, while the capacity of distribution centers is trivial. Integrated decision making can achieve remarkable cost savings only if the capacity constraints of regional distribution centers are loose. Nevertheless, capacity constraints of distribution centers make almost no impact. This result can be a good guide for a company’s investment plan: one should give top priority to regional distribution centers, and give only low priority to distribution centers.

Evaluation and control of inventory distribution systems with quantity based shipment consolidation

AbstractJoint consideration of inventory and shipment decisions is an important aspect of obtaining economically and environmentally sustainable distribution systems. We consider this issue in the context of a one‐warehouse‐multiple‐retailer inventory system with quantity‐based shipment consolidation to groups of nonidentical retailers facing Poisson demand. The system is centralized, with free information sharing and access to real‐time point of sale data. Thus, demand information at the retailers is immediately conveyed upstream to the warehouse without any fixed ordering costs. However, fixed costs associated with handling and transporting goods from the warehouse to the retailers are reflected in the shipment consolidation policy. Stock replenishments at the warehouse are made from an outside supplier/manufacturer according to an (R, Q) policy. For this system, we derive an exact recursive method for determining the inventory level distributions at the retailers. This allows us to evaluate the expected inventory and shipment costs, fill‐rates, and transport emissions for the entire system. We also show how to optimize the system by providing bounds on the optimal shipment quantities and the warehouse reorder level.

Dynamic supply chains with endogenous dispositions

AbstractThe movement of goods through a supply chain depends on both the physical flow of goods and on the economic decisions of each entity along the chain, including price discovery and inventory disposition decisions. This paper presents a methodological contribution to the system dynamics and supply chain research communities by developing a novel framework for supply chain models by combining three classic modeling methods: co‐flow differential equation structures, spot price discovery, and multinomial logistic choice modeling. The relative economic values of possible dispositions of goods, including outright disposal, are considered. For work‐in‐progress, development is considered in terms of the economic value that an additional unit of time will bring to the finished good, and the interplay of these considerations drive goods through, or out of, supply chains. Incorporating these mechanisms can produce materially different behavior modes and can be applied to multiple levels of aggregation within a production process. © 2022 The Author. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Feature-based intermittent demand forecast combinations: accuracy and inventory implications

Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study examines the empirical outcomes of some existing forecast combination methods and proposes a generalised feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions.

A multi-start route improving matheuristic for the production routeing problem

This paper considers the multi-vehicle production routeing problem with a maximum-level replenishment policy. This is a well-established problem within vendor managed inventory where production, inventory and routeing decisions are made simultaneously. We present a novel method to solve the problem that outperforms existing methods both in terms of solution gaps and the number of best-known solutions. The proposed matheuristic is tested on three different sets of benchmark instances consisting of 1218 instances and finds or improves the best-known solution for 632 of them. For the remaining instances, the matheuristic is less than 2.5% from the best-known solutions. The method is particularly proficient on large instances and is also efficient for the inventory routeing problem. The success of the method is largely due to its improvement phase where a novel path-flow-inspired mathematical model is introduced. Here, a route set obtained from the current solution is used and retailers can be simultaneously inserted and removed from a route, making the method flexible even when a small route set is used. In addition, we introduce a new production subproblem that approximates the costs of using a vehicle instead of approximating the costs of visiting a retailer, making it very fast to solve.

Transportation strategies for dynamic lot sizing: single or multiple modes?

ABSTRACT The complexity of decision-making for companies buying transportation services has increased due to the presence of more options and pricing schedules for transportation. Many companies make transportation and inventory decisions in an uncoordinated way and select only one transportation mode, missing opportunities for logistics cost savings. The experimental study in this paper is based on a real-world decision problem faced by a Scandinavian company that distributes fast-moving consumer goods and wants to determine its transportation strategy. We propose a novel multi-mode lot-sizing model with dynamic deterministic demand to illustrate the cost impact of accurately modelling piecewise-linear transportation costs and allowing a more flexible usage of transportation modes when planning order replenishments. We compare three transportation strategies with increasing degrees of flexibility: two single mode strategies, where one strategy is more flexible than the other, and a multi-mode strategy. We conclude that managers can significantly reduce costs by increasing the flexibility of mode selection in transportation strategies.

Manage Inventories with Learning on Demands and Buy-up Substitution Probability

Problem Definition: This paper considers a setting in which an airline company sells seats periodically, and each period consists of two selling phases, an early-bird discount phase and a regular-price phase. In each period, when the early-bird discount seat is stocked out, an early-bird customer who comes for the discounted seat either purchases the regular-price seat as a substitute (called buy-up substitution) or simply leaves. Methodology/Results: The optimal inventory level of the discounted seats reserved for the early-bird sale is a critical decision for the airline company to maximize its revenue. The airline company learns about the demands for both discounted and regular-price seats and the buy-up substitution probability from historical sales data, which, in turn, are affected by past inventory allocation decisions. In this paper, we investigate two information scenarios based on whether lost sales are observable, and we provide the corresponding Bayesian updating mechanism for learning about demand parameters and substitution probability. We then construct a dynamic programming model to derive the Bayesian optimal inventory level decisions in a multiperiod setting. The literature finds that the unobservability of lost sales drives the inventory manager to stock more (i.e., the Bayesian optimal inventory level should be kept higher than the myopic inventory level) to observe and learn more about demand distributions. Here, we show that when the buy-up substitution probability is known, one may stock less, because one can infer some information about the primary demand for the discounted seat from the customer substitution behavior. We also find that to learn about the unknown buy-up substitution probability drives the inventory manager to stock less so as to induce more substitution trials. Finally, we develop a SoftMax algorithm to solve our dynamic programming problem. We show that the obtained stock more (less) result can be utilized to speed up the convergence of the algorithm to the optimal solution. Managerial Implications: Our results shed light on the airline seat protection level decision with learning about demand parameters and buy-up substitution probability. Compared with myopic optimization, Bayesian inventory decisions that consider the exploration-exploitation tradeoff can avoid getting stuck in local optima and improve the revenue. We also identify new driving forces behind the stock more (less) result that complement the Bayesian inventory management literature. Funding: Z. Luo acknowledges the financial support by the Internal Start-up Fund of The Hong Kong Polytechnic University [Grant P0039035]. P. Guo acknowledges the financial support from the Research Grants Council of Hong Kong [Grant 15508518]. Y. Wang’s work was supported by the Research Grants Council of Hong Kong [Grant 15505318] and the National Natural Science Foundation of China [Grant 71971184]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1169 .

Large-Scale Inventory Optimization: A Recurrent Neural Networks–Inspired Simulation Approach

Many large-scale production networks include thousands of types of final products and tens to hundreds of thousands of types of raw materials and intermediate products. These networks face complicated inventory management decisions, which are often too complicated for inventory models and too large for simulation models. In this paper, by combining efficient computational tools of recurrent neural networks (RNNs) and the structural information of production networks, we propose an RNN-inspired simulation approach that may be thousands of times faster than the existing simulation approach and is capable of solving large-scale inventory optimization problems in a reasonable amount of time. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72091211].

Closed-loop supply chain inventory model in the pharmaceutical industry toward a circular economy

The closed-loop supply chain inventory model is essential in the pharmaceutical industry because it promotes environmental, social, and economic sustainability. Many unused drugs are discarded, damaging the environment. At the same time some patients find it difficult to obtain expensive prescription drugs. The closed-loop supply chain can draw on the principles of the circular economy, which is concerned with the adverse effects of using new materials for environmentally harmful manufacturing processes. Re-dispensing or recycling unused drugs, is one application of the principle of the circular economy. Pharmacies can collect, inspect, and resell drugs. Pharmacies incur high inventory costs related to expensive medications and need efficient inventory management. This study focuses on the recycling of drugs to lighten the economic burden on patients in achieving social sustainability, and to prevent the waste of unused medications in achieving environmental sustainability. This study considers the impact of government incentives on patient demand for recycled and new drugs. In addition, pharmacies' optimal inventory decisions and government subsidies benefit the pharmacies in achieving economic sustainability. This environmental, social, and economic sustainability are pillars of the circular economy. This study determines the impact of inventory, incentives, and subsidy-related decisions on total benefits and costs by considering patients' willingness to use recycled drugs. The integrated pharmacy inventory and government decision model is presented in a non-linear programming model and uses the generalized reduced gradient. The results show the feasibility of the drug recycling program. The theoretical implications involve patients’ willingness to use recycled drugs and the practical implications by examining the role of non-profit pharmacies in achieving maximum environmental and social benefits.

Integrated maintenance, inventory and quality engineering decisions for multi-product systems

Abstract It is essential for manufacturers to consider the interrelation among quality, inventory, and maintenance decisions to detect imperfect quality products, keep the production system in good operating condition, and manage quality and inventory costs. Hence, this paper aims to develop an integrated model of inventory planning, quality engineering, and maintenance scheduling in which the expected total cost per time unit is minimised by determining the sample size, sampling interval, control limit coefficient, along with production cycle time. In this regard, an imperfect multi-product manufacturing system is considered, in which the inventory shortage in satisfying the demand for each product type and the idle time during the production cycle are not allowed. It is assumed that the process starts in an in-control condition where most produced units are conforming. However, due to the occurrence of an assignable cause (AC), the process mean moves to an out-of-control condition in which a significant fraction of non-conforming units is produced. The efficiency of the proposed mathematical model is evaluated by a numerical example, and then the sensitivity of the proposed model to important inputs is analysed. Finally, a comparative study based on the Taguchi design approach is given to confirm the capability of the proposed model to achieve remarkable cost savings.

Dynamic inventory system with pricing adjustment for price‐comparison shoppers

AbstractWith numerous price‐comparison websites and applications, consumers today are frequently conducting price‐comparison shopping. As a result, retailers face an increasing challenge in predicting consumer demand and determining the optimal product price and inventory level accordingly. To address this issue, this paper proposes an inventory model with joint decisions of price and inventory to optimize the retailer's long‐run average profit under price‐comparison consumer shopping. We first formulate the demand arrival process for a retailer under price‐comparison shopping to be affected by not only its own price but also its competitors'. Based on this demand arrival process, we then formulate the retailer's long‐run average profit and derive properties of its optimal solution. Our model focuses on capturing the impact of price‐comparison consumers on a retailer's optimal price and inventory decisions. In particular, we allow competitors' prices to affect the retailer's demand via two key factors: the manufacturer's suggested price and the variability of the outside lowest price. According to our results, when the suggested price increases, the retailer should lower its price to obtain more price‐comparison customers from competitors, whereas when the variability of outside lowest price increases, the retailer should raise its price to increase per unit profit from nonprice‐comparison customers.

The Impact of Trade Credit Provision on Retail Inventory: An Empirical Investigation Using Synthetic Controls

Trade credit is an important source of short-term financing and an integrated part in supply contracts. Although a number of theories have been proposed on how trade credit could improve supply chain efficiency, causal studies on the impact of trade credit on operational decisions are scarce. In this study, we examine the impact of trade credit on inventory decisions using an empirical strategy that leverages (i) an exogenous shock imparted by the French government’s intervention to impose a ceiling on trade credit duration, (ii) a triple difference-in-differences identification strategy, and (iii) synthetic controls (SCs). By considering the 60-day ceiling coverage and SC construction requirements, we identify four French retail sectors as our main sample. Among them, in the postregulation period, the hardware retail sector firms on average exhibited a significant 16% decline in trade credit usage. Correspondingly, these firms also displayed a significant 11% decline in inventory level. In the remaining three sectors, we found mixed results in the main sample. All the four sectors, however, show consistent support for a causal link between trade credit and inventory in a subsample compiled using a stringent 90-day ceiling criterion. Collectively, our findings offer direct evidence that trade credit is an indispensable financing source for inventory procurement. Finally, in the postregulation period, the hardware retailers exhibited a 15.5% decline in revenue and 3.5% reduction in gross profit. This cautions policy makers that regulations limiting the use of trade credit may have unintended consequences on downstream firms, and may harm overall supply chain efficiency. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported by Indiana University Bloomington and the London Business School. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4600 .

Live commerce retailing with online influencers: Two business models

Selling during online influencers’ livestreaming has become prevalent in the retailing sector. Critical decisions for firms to keep the business sustainably profitable include whether it is worth collaborating with influencers, collaboration approaches, and the corresponding pricing and inventory decisions. Based on industrial practice, we investigate two business models: the Influencer-oriented Short-window (IOSW) model and the Market-oriented Long-window (MOLW) model, to understand how the models benefit a firm and how decision-making affects profit sustainability. To this end, we build stochastic optimization models and derive managerial insights. We find that neither of the two models dominates the other, so firms should not irrationally follow a case with successful influencer collaboration experiences. Moreover, a firm’s innovation capacity is the critical competence in the IOSW model, and an influencer’s fan base size is the fundamental driver for profits in the MOLW model. Firms should prioritize the “lowest price” strategy if they are not over-optimistic about the success of the livestream shows.

Intermittent Demand Forecasting Using LSTM With Single and Multiple Aggregation

Intermittent demand data is data with infrequent demand with varying number of demand sizes. Intermittent demand forecasting is useful for providing inventory control decisions. It is very important to produce accurate forecasts. Based on previous research, deep learning models, especially MLP and RNN-based architectures, have not been able to provide better intermittent data forecasting results compared to traditional methods. This research will focus on analyzing the results of intermittent data forecasting using deep learning with several levels of aggregation and a combination of several levels of aggregation. In this research, the LSTM model is implemented into two traditional models that use aggregation techniques and are specifically used for intermittent data forecasting, namely ADIDA and MAPA. The result, based on tests on the six predetermined data, the LSTM model with aggregation and disaggregation is able to provide better test results than the LSTM model without aggregation and disaggregation.

Inventory pooling and pricing decisions in multiple markets with strategic customers

This study considers the pricing and inventory decisions for the retailer selling in multiple markets with strategic customers. The impact of strategic customer behavior on inventory pooling is examined. The equilibrium decisions, in which strategic customers tend to purchase early, are subsequently characterized in the pooled/non-pooled systems. Our results highlight the role of the strategic customer in each market. Compared with myopic customers, the retailer is prone to reduce its inventory for strategic customers. The retailer’s optimal inventory for high-profit products is lower in the pooled system than in the non-pooled system. However, the result is reversed for low-profit products. The analytical and numerical results simultaneously demonstrate that, in the high-profit condition, the retailers’ inventory in the pooled system increases with the correlation coefficient of different markets, while the retailers’ profit decreases with the correlation coefficient. There is an opposite relationship in the low-profit condition. When the markets are less correlated, the retailer owns low inventory but high profit. Moreover, the retailers’ profit is always higher in the pooled system than in the non-pooled system.