Inventory Decisions Research Articles

Case study on simulation analysis of a logistics network with multiple products by applying lateral transshipments algorithms

PurposeThe purpose of this paper is to develop a simulation model to evaluate inventory and distribution decisions like lateral transshipments in a network with multiple products.Design/methodology/approachData are collected from a company, and a discrete-event simulation in Python is developed to support the decision-making process of managers through different algorithms of lateral transshipments.FindingsThe numerical results show that the periodic delivery-continuous reorder policy is more robust than the others because the reorder process is not affected by the higher saturation that is achieved by periodic reorder–based policies. The new lateral transshipment algorithm will lead to huge savings in logistics costs for any company and increase truck saturation without causing a decrease in the service level.Research limitations/implicationsThis paper provides a novel institutional perspective on a complex logistics issue where COVD-19 is believed to complicate the context.Practical implicationsThis solution is devised for any company to achieve even greater benefits in terms of customer service improvement and logistics costs reduction.Originality/valueThe main contribution of this paper is the proposal of a new lateral transshipment algorithm that shows performance improvement by simulating distribution network processes according to different configurations.

Pricing, assortment, and inventory decisions under nested logit demand and equal profit margins

We consider the interdependent decisions on assortment, inventory and pricing of substitutable products that are differentiated by some primary and secondary attributes captured by a nested logit consumer choice. We examine a newsvendor-type setting with several products competing for demand over a single selling season. We assume that all products have equal profit margins. The demand has a multiplicative-additive structure where both variance and coefficient of variation depend on the common profit margin, which adds to the model applicability at the expense of tractability. Under a Taylor series-type approximation, we show that the expected profit is unimodal in the common margin of products in a given assortment. Then, we compare the optimal profit margin to the case under ample inventory, which allows understanding the effect of inventory on pricing. We also study the optimal assortment problem under exogenous pricing. We show that the classic result on the optimality of popular sets holds under tight approximations of the profit function. We finally propose a heuristic for jointly deciding on assortment, pricing, and inventory decisions, which assumes equal profit margins of products, and exploit popular sets only. Our detailed numerical study shows that this equal-margin heuristic produces high quality solutions.

Data-driven Wasserstein distributionally robust mitigation and recovery against random supply chain disruption

This paper studies joint robust network design and recovery investment management in a production supply chain, considering limited historical data about disruptions and their possibilities. The supply chain is subject to uncertain disruptions that reduce production capacity at plants, and the cascading failures propagate along the supply chain network. A data-driven two-stage distributionally robust optimization model with Wasserstein ambiguity set (TWDRO) is constructed to determine the strategic location and tactical allocation decisions in the first stage as well as the operational production and inventory decisions and recovery policy by recourse in the second stage. This paper also proposes a model for depicting a recovery-fund based mitigation strategy, and the model is general in depicting the accelerated, constant-speed, and decelerated recovery processes. In addition, partial backorder policy is adopted to depict the customers’ choices upon product stockout. The TWDRO model is solved by converting it to a mixed integer linear programming model and designing a joint solution method using benders decomposition and genetic algorithm. The performance of TWDRO solutions is demonstrated through numerical experiments and a case study, benchmarking on the stochastic programming and robust optimization approaches. This paper shows the effectiveness and robustness of TWDRO and provides managerial implications and suggestions for supply chain disruption and recovery management.

Supplier inventory leanness and financial performance

AbstractNumerous studies have examined the relationship between inventory management and financial performance. However, the focus of such empirical work has primarily been on how a firm's own inventory characteristics affect its performance. Our objective is to extend this body of literature beyond the firm‐level. We draw on inventory theory and resource‐based theories to hypothesize about the effect of supplier inventory leanness on a focal firm's financial performance and how supplier and focal firm inventory leanness interact to affect such outcomes. We test our hypotheses using a large panel dataset of supplier‐focal firm relationships obtained from Compustat's Customer Segment database and aggregated to the focal firm‐quarter level, as well as firm financial information from Compustat's Fundamentals Quarterly database. The econometric analyses provide evidence that supplier inventory leanness influences focal firm financial performance indirectly through the interaction with the firm's own inventory leanness. In particular, our estimation results detail how supplier inventory leanness affects the non‐linearity of the focal firm's inventory leanness‐financial performance relationship and its optimal inventory leanness level. The findings broaden the scope of empirical inventory literature and highlight supplier inventory leanness as an important consideration in firm‐level inventory decision making.

A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: Hybrid meta-heuristic algorithms

A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable Closed-Loop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO2)emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran.

Vendor-managed inventory (VMI) deteriorating item model taking into account carbon emissions

This paper investigates the main activities that lead to carbon emissions in a deteriorating items supply chain. This study applies a vendor-managed inventory (VMI) model for deteriorating items with a single-setup-single-delivery (SSSD) policy and a single-setup-multiple-delivery (SSMD) policy. We provide constructive managerial insights to both researchers and industries in their inventory management decisions in minimizing the total deteriorating items cost and the total carbon emissions. A numerical example is developed to optimize the delivery quantity and the number of deliveries per production cycle. The results enable companies to identify an effective inventory management critical to reducing carbon emissions, especially for deteriorating items.

Rethinking Inventory Forecasting Problems in E-commerce: Exploring the effect of integrating forecasting and inventory decisions

Forecasting for inventory control is the process of calculating the inventory needs to fulfill future consumer demand. In general, this process is divided into two sub-processes. The first sub-process receives the current inventory information and forecasts future information, e.g. forecasts future demand from the demand information in the past. The second sub-process uses the forecast information as input to make inventory decisions, e.g. use a product demand forecast to decide how many units of this product to buy. Recent works highlight the importance of integrating forecasting with final inventory decisions, however, there is very little empirical evidence to support that integrating the decision is the best solution. In this work, we propose to explore the effect of integrating the inventory decision into the forecasting problem and compare it with the state-of-the-art approaches. For this, we evaluated the approaches in different operational tasks belonging to our business. Our preliminary findings show that predicting operative decisions instead of demand information could be better and the benefit can be capitalized even in low data scenarios.

Ordering decisions under supply uncertainty and inventory record inaccuracy: An experimental investigation

AbstractUncertainty on the supply side is a common issue planners face. How do decision‐makers incorporate inventory uncertainty when placing orders? We investigate ordering decisions under two forms of uncertainty regarding total inventory available to satisfy demand: supply uncertainty (SU; unreliability in incoming shipments) and inventory record inaccuracy (IRI; internal inefficiencies leading to a discrepancy between physical and recorded inventories). The experimental results reveal behavioral regularities in ordering decisions under both forms of total inventory uncertainty. We find that subjects overstock in settings with low profit margins, and overstocking is more pronounced under IRI than under SU. This overstocking under low profit margins is similar to observed ordering decisions under demand uncertainty. In these settings, subjects show a stronger shortage aversion under IRI (which is internal uncertainty) than under SU (which is external uncertainty). Furthermore, we find that subjects chase past realizations of supply/on‐hand inventory, although the effect depends on the uncertainty type. Although SU and IRI are, in practice, often simultaneously present, their causes are different. By providing insight into the relative effect of the types of uncertainty on the quality of inventory replenishment decisions, this study highlights the importance of reducing SU and IRI for products with low profit margins.

Short lifetime product supply chain coordination and social benefit considering cannibalization effect and market segmentation

Short lifetime product retailers often face the challenge of cannibalization between new and old products, which can negatively impact their profitability. They attempt to influence consumers’ choices through price differentiation, resulting in internal competition regarding products’ age and price. The pricing decisions affect market demand, sales volume, and as a result, the whole supply chain (SC) profit. This paper coordinates inventory and pricing decisions in a short lifetime product supply chain (SLPSC), considering the cannibalization effect. The investigated SLPSC includes a supplier and a retailer operating in a segmented market. Firstly, the optimal decisions of the SLPSC members are obtained under decentralized and centralized decision-making structures. Then, a new coordination contract named wholesale price and double compensation (WPDC) is designed to motivate the SC members to shift from the decentralized structure to the centralized one. The findings indicate that the coordinated model creates more economic profitability for the whole SLPSC than the decentralized one. Furthermore, the proposed WPDC contract is more beneficial for the SLPSC from a social viewpoint, as it increases consumer surplus. The results also demonstrate that when consumers are more sensitive to the product’s freshness, a price differentiation policy is more profitable than the same pricing.

Sustainable closed-loop supply chain with energy efficiency: Lagrangian relaxation, reformulations and heuristics

Research on the development of sustainable supply chain models is highly active nowadays. Merging the concept of supply chain management with sustainable development goals, leads to simultaneous consideration of all economic, environmental and social factors. This paper addresses the design of a sustainable closed-loop supply chain including suppliers, manufacturers, distribution centers, customer zones, and disposal centers considering the consumption of energy. In addition, the distribution centers play the roles of warehouse and collection centers. The problem involves three choices of remanufacturing, recycling, and disposing the returned items. The objectives are including the total profit, energy consumption and the number of created job opportunities. As far as we know, these objectives are rarely considered in a sustainable closed-loop supply chain model. The proposed model also responds to the customer demand and also addresses the real-life constraints for location, allocation and inventory decisions in a closed-loop supply chain framework. Another novelty of this research is to develop a set of efficient Lagrangian relaxation reformulations and fast heuristics for solving a real-world numerical example. The results have revealed that the obtained solution is feasible and the developed solution algorithm is highly efficient for solving supply chain models. Finally, a comprehensive discussion is provided to highlight our findings and managerial insights from our results.

Disclosing opaque inventory information in a two-period sales setting: substitute products' selling strategy

PurposeThe purpose of this paper is to investigate the opaque inventory information disclosure strategy for an online retailer who sells two substitutable products to customers in two selling periods.Design/methodology/approachThe authors develop a two-period model where an online retailer sells two substitute products with two inventory composition structures to maximize profits. The authors investigate the optimal inventory disclosure decision from both ex post and ex ante perspectives. Sensitivity analysis is performed to investigate the effects that discount rate, transaction cost and the probability of agreeable inventory situation have on the equilibrium disclosure outcome. The authors also consider risk-averse customers and horizontally differentiated products to highlight the robustness of our results.FindingsThe authors find that the online retailer will choose the opaque information disclosure when attempting to increase revenue and reduce the mismatch of supply and demand in both ex post and ex ante inventory information conditions. Comparing with ex post disclosure strategies, ex ante opaque disclosure is optimal in a larger price region, and the total revenues gap between opaque disclosure and complete disclosure gradually increase as discount rate, transaction cost or the probability of agreeable inventory situation decreases. Furthermore, strategic customers may tend to be risk neutral when faced with opaque inventory information in a two-period sales setting.Originality/valueThis current paper is the first paper to study the online retailer's inventory information disclosure strategy in two selling periods. Moreover, this paper presents the conditions under which the online retailer should share complete or opaque inventory information with customers to maximize the online retailer's total revenues.

Are shore power and emission control area policies always effective together for pollutant emission reduction? – An analysis of their joint impacts at the post-pandemic era

Shore power (SP) and emission control area (ECA) have been two of the main polices for a green maritime logistics. However, it is still not clear how the joint impact of these two polices will influence vessels' shipping and energy consumption plan. The influence could be more complicated at the post-pandemic era of COVID-19 when the vessel's refueling could be risky. This paper develops mathematical model of the vessel's routing and fuel inventory decisions by considering both the SP and ECA. Based on the model, we analyze the optimal decision of a vessel under three different policy conditions, which are non-SP, compulsive utilization of SP at all ports, and optional SP utilization by the vessel. The numerical experiments are conducted based on the information of a vessel delivering cargos along the coastline in China. The results uncover a novel phenomenon that the implementation of the SP might incentive the vessel to travel longer distance outside the ECA for a lower total cost that results in more pollutant emissions. We show that the SP policy would influence the vessel's fuel management and refueling times which can change the route of vessel sailing inside and outside ECAs. Sensitivity analysis also shed light on how the government's decision on the SP policy may influence the overall pollutant emissions of the vessel from both berthing at ports and sailing on the sea. We discussed how the fixed cost of refueling, which is affected by the risk of the COVID-19 condition, will influence the vessel's plan under the SP and ECA polices. This study suggests the government to make an integrated policy of both SP and ECA at the post-pandemic era to achieve an overall reduction of the pollutant emission.

The value of point of sales information in upstream supply chain forecasting: an empirical investigation

Traditionally, manufacturers use past orders (received from some downstream supply chain level) to forecast future ones, before turning such forecasts into appropriate inventory and production optimisation decisions. With recent advances in information sharing technologies, upstream supply chain (SC) companies may have access to downstream point of sales (POS) data. Such data can be used as an alternative source of information for forecasting. There are a few studies that investigate the benefits of using orders versus POS data in upstream SC forecasting; the results are mixed and empirical evidence is lacking, particularly in the context of multi-echelon SCs and in the presence of promotions. We investigate an actual three-echelon SC with 684 series where the manufacturer aims to forecast orders received from distribution centres (DCs) using either aggregated POS data at DCs level or historical orders received from the DCs. Our results show that the order-based methods outperform the POS-based ones by 6–15%. We find that low values of mean, variance, non-linearity and entropy of POS data, and promotion presence negatively impact the performance of the POS-based forecasts. Such findings are useful for determining the appropriate source of data and the impact of series characteristics for order forecasting in SCs.

Two-stage risk-averse stochastic programming approach for multi-item single source ordering problem: CVaR minimisation with transportation cost

Integrating inventory and transportation decisions is vital in supply chain management and can enable decision-makers to achieve competitive advantages. This study considers a multi-item replenishment problem (MIRP) with a piece-wise linear transportation cost under demand uncertainty, which usually occurs both in retail and production environment when several items must be ordered from a single supplier. Conventionally, two-stage stochastic programming formulation is risk-neutral, and it lacks robustness in the presence of high data variability. Hence, we introduce the Conditional Value at Risk (CVaR) approach for MIRP. Additionally, we deploy both single and multi-cut L-shaped and the sample average approximation method to circumvent the computational complexity to solve large-scale instances. The data-driven simulation study is used to benchmark the results from deterministic, risk-neutral, and risk-averse stochastic models. The results indicate that under higher data variations, the risk-averse model provides better perspectives for a decision-maker. The results show a 40–50% reduction in lost sales with marginal growth in total cost while considering CVaR instead of a risk-neutral approach.

Driving Supply to Marketplaces: Optimal Platform Pricing When Suppliers Share Inventory

Problem definition: Marketplace platforms such as Amazon or Farfetch provide a convenient meeting point between customers and suppliers and have become an important element of e-commerce. This sales channel is particularly interesting for suppliers that sell seasonal goods under a tight time frame because they provide expanded reach to potential customers even though it entails lower margins. In this dyadic relationship, a supplier needs to optimize when to share inventory with the platform, and the platform needs to set the right commission structure during the season. Academic/practical relevance: We characterize supplier participation into the platform in a dynamic setting and link it to inventory levels, demand rates, time left in the season, and commission structure. This directly drives the commission structure decision made by the platform. We, thus, provide a framework to evaluate platform commission fee policies, taking into account supplier responses. Methodology: We use an optimal control framework with limited inventory supply and a stochastic demand process. We study the conditions under which the supplier accepts participation and use the platform as a sales channel. We also study the optimal commission structure that the platform should employ and the supplier procurement response. Results: We find that suppliers only participate if inventory is high relative to the time left to sell the items. As a result, the platform can only offer limited supply at the beginning of the season. Given this behavior, we find that the platform and the system are always better off with flexible pricing via fully dynamic commissions, which hurts the supplier the most (better off with less flexible commission fees). Interestingly, when the inventory decision is contingent on the platform pricing policy, the platform often finds it beneficial to commit to a static fee to incentivize the supplier to stock up, highlighting that inability to commit to fixed commissions may destroy value through double marginalization effects. Managerial implications: Our work suggests that short-term profit for the platform is maximized with fully dynamic commission fees at the expense of supplier profit. If inventory is endogenous, suppliers can retaliate by reducing their commitment at the start of the season. Despite the increased revenue obtained with the fully dynamic commission fee, the lost sales from the inventory drop incentivize the platform to opt for supplier-friendly commission fees, which are better for long-term profit.

Manufacturer returns: An empirical study

Manufacturer returns, or buyback, have been increasingly observed in manufacturer-retailer channels. Using proprietary contract and sales data, this study empirically examines two key questions associated with buyback: (1) why does a manufacturer offer buyback to a retailer and (2) how are a manufacturer’s buyback decisions associated with its own and a downstream retailer’s marketing strategies? The existing research on the topic has been dominated by theoretical work. Instead, we use rich data to provide validations for the analytical models, as well as new insights above and beyond what is known from them. Our empirical findings suggest that buyback plays multiple critical roles simultaneously for a manufacturer, including risk-sharing role and informational role. Our results also show that whether a manufacturer offers buyback to the retailer is significantly associated with its own and the retailer’s pricing, promotion, and inventory decisions. Furthermore, the impacts vary across product categories that exhibit distinct characteristics.

Sustainable production lot sizing problem: A sensitivity analysis on controlling carbon emissions through green investment

Improving inventory decisions and developing better approaches to invest green technology have been the prime contributions in the existing relevant literature. Besides proposing better inventory decisions and investing on green technologies, the financial performance of a business have also been considered as a focal point. However, none of the existing works on sustainable lot-sizing has considered a simultaneous reduction of carbon emission, product deterioration rate and set-up costs to maximize total profit. Thence, a sustainable lot-sizing model is proposed in this work by incorporating strategies to reduce the setup cost, to optimize the inventory of a greenhouse farm and simultaneous investments in green and preservation technologies to deal with the carbon emission and waste. This study shows the variation of total profit due to changes in investment level and production cycle length. The numerical examples demonstrates the threshold for green technology investment. The study also proves the vital role of preservation technology in deteriorating product inventory, as it gives a higher impact than the other investments.

Applications of Stochastic Orders and Stochastic Functions in Inventory and Pricing Problems

Uncertainties involved in decision making often impose challenges to our ability of modeling and analyzing the reality. The purpose of this study is to demonstrate how one can go beyond the conventional optimization thinking, via taking a stochastic viewpoint, to push the boundaries of the analysis. We review the basic notions of stochastic orders and stochastic functions, with which the conventional (deterministic) way of modeling can be generalized. Based on the orders among distribution functions associated with random variables, we can treat the input–output relationship as stochastic functions in our models. This viewpoint, leads to extended functional properties in the stochastic sense. Taking the existing models involving inventory and pricing decisions, we show how the stochastic notions are powerful in simplifying and generalizing the analysis, as well as generating new insights.

M-Commerce, Sales Concentration, and Inventory Management

Problem definition: The mobile commerce (m-commerce) channel is poised to be the future of online markets. It offers search features distinct from conventional personal computer (PC)-based e-commerce channels. Its easy accessibility extends the time available for customers to search, although its shopping environment constraints (e.g., small screen size, single-tab browsing) may inflate search costs. Collectively, these competing features cause ambiguity about the mobile channel’s true effect on sales concentration—a key criterion in managing retail operations. The focus of this study is to understand the net impact of the mobile channel on driving sales concentration. Academic/practical relevance: Our study extends the empirical literature focusing on online retailers’ channel strategies and their implication on operations practice. It examines differences in primary online channels—mobile and PC—on shaping sales concentration across products and the cost of ignoring such a difference on inventory management, a core decision for operations managers. Methodology: We collaborate with a large online apparel retailer to access customer-level transactional data. We identify the mobile channel’s effect on sales concentration using a difference-in-differences strategy that leverages a quasi-experiment stemming from the retailer’s decision to discontinue its PC sales channel. Results: We find that the mobile channel increases the share of popular products by 6.4% compared with the PC channel. We also identify scenarios where ignoring this significant sales concentration difference will yield suboptimal inventory stocking by 4.2%–12.9%. Managerial implications: Our paper highlights that the mobile and PC channels have different sales concentrations because of different search features. Ignoring this difference affects inventory decisions, such as safety and cycle inventory levels. Therefore, it is imperative for managers to revise their status quo strategies, such as on inventory procurement, assortment planning, and product display, when integrating m-commerce with e-commerce.

Cognitive Stress and Learning Economic Order Quantity Inventory Management: An Experimental Investigation

We use laboratory experiments to evaluate the effects of cognitive stress on inventory management decisions in a finite horizon economic order quantity (EOQ) model. We manipulate two sources of cognitive stress. First, we vary individuals’ participation in a 6-digit mixed number and letter memorization task involving their personal identification numbers. This exogenously increases cognitive load. Second, we introduce an intervention to reduce cognitive stress by only allowing participants to order when inventory is depleted. This restricts the order choice set. Increases in cognitive load negatively impact earnings with and without the intervention, with these impacts largely occurring in the first attempt of the task. With repetition, participants’ choices in all treatments trend to near-optimal policy adoption. However, only in the intervention and low cognitive load treatment do the majority of choices reach the optimal policy. We estimate the learning dynamics of order decisions using a Markov learning model. Estimates suggest increased cognitive load reduces the probability of switching to more profitable policies. Choice set complexity increases biases for smaller order size adjustments, leading to greater policy lock-in.