Retail Inventory Research Articles

Advanced Analytics for Retail Inventory and Demand Forecasting

Achieving operational efficiency and enhancing customer satisfaction levels in the retail sector is directly dependent on efficient inventory management and accurate demand forecasting. The following study employs advanced analytics techniques, such as time series forecasting and machine learning, to bolster these essential functions. By leveraging on historical sales data from 45 retail stores sourced by Kaggle, this paper has constructed predictive models with the aim to optimize inventory levels and forecast demand with precision. The Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model employed in this study adequately captures linear dependencies and seasonal patterns, while Long Short-Term Memory (LSTM) networks are responsible for the management of intricate, non-linear dependencies. The findings from this study depict the significant seasonal trends, the impact of economic factors, the impact of economic variables and the effectiveness of hybrid models in improving forecast accuracy. The integration of such advanced methodologies clearly highlights their massive potential in improving aspects such as inventory management and operational efficiency in the retail domain.

Optimizing Retail Inventory Management Through Time Series Analysis

The purpose of this study is to investigate the application of time series analysis in the management of retail inventory, with a particular emphasis on the precision of forecasting as well as the applicability of ARIMA models and seasonal decomposition techniques. This study aimed to evaluate these models' effectiveness in improving the scientific and precise nature of inventory management. This was accomplished by using legitimate retail data. According to the findings, the ARIMA model is effective in managing data that is stable or does not exhibit seasonal patterns. On the other hand, the seasonal decomposition technique demonstrates strong forecasting accuracy in managing data that exhibits seasonal fluctuations. Not only does this research improve the theoretical implementation of time series analysis in the retail sector, but it also provides retailers with practical strategies for mitigating economic losses that are caused by inventory imbalance. When it comes to the management of retail inventory, the findings of the research are significant for improving both overall cost control and customer satisfaction.

OPTIMIZING RETAIL DEMAND FORECASTING: A PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS INCLUDING LSTM AND GRADIENT BOOSTING

Effective demand forecasting is vital for inventory management in retail. This study evaluates five machine learning models—Linear Regression (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting (GB), and Long Short-Term Memory (LSTM)—for predicting retail demand. Utilizing a dataset with transactional sales, promotions, calendar events, and external factors like weather and economic indicators, we applied rigorous preprocessing and feature engineering. Performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). Results show that LSTM outperforms other models with an MAE of 9.53, RMSE of 14.67, and R² of 0.90, excelling in capturing temporal dependencies and complex demand patterns. Gradient Boosting and Random Forest also performed well, while Linear Regression and Decision Tree Regressor showed limitations. This study highlights the effectiveness of advanced models, particularly LSTM, for enhancing demand forecasting accuracy and offers valuable insights for optimizing retail inventory and operations.

Implementasi Artificial Intelligence (AI) di Obrit Future Academy

The government is obliged to provide the best educational services for the younger generation in order to create superior quality human resources as regulated by Law of the Republic of Indonesia Number 20 of 2003 concerning National Higher Education. The Ministry of Education, Culture, Research and Technology established the Independent Campus Orbit Future Academy (OFA) program which is an institution that is a pioneer in developing adaptive and innovative learning models. There are two main dimensions carried out by OFA to commit to improving the quality of life, namely providing special skills learning and the application of Artificial Intelligence (AI). Skills development at OFA aims to solve complex problems in the context of retail inventory management. Therefore, this study will discuss further the implementation of AI in retail inventory management with computer vision carried out at OFA. This research includes descriptive qualitative research with data collection techniques in the form of observation and research carried out directly at Orbit Future Academy. Data were analyzed using library research techniques. The findings show that the application and modeling of AI systems can be applied in retail inventory management through Python programming. The existence of AI can be applied in developing applications that have a real impact on the business sector and solving retail investment problems. Specifically, Computer Vision was developed as a smart and efficient solution for managing inventory. The resulting technology is a system that can see and recognize products automatically from visual images, minimize slow stock updates, optimize operational efficiency and reduce human error. Thus, overall the application of AI through Computer Vision in OFA can be an innovative solution developed as the latest technology to improve retail inventory.

A green inventory model considering environmental emissions under carbon tax, cap-and-offset, and cap-and-trade regulations

ABSTRACT This study assesses green technology investment as well as carbon pricing regulations, including carbon tax, cap-and-offset, and cap-and-trade, to control carbon emissions from retailer inventory. A green inventory model is studied due to a lack of investigation on the simultaneous effect of green technology investment and variable demand. Prior studies neglected the demand pattern that is sensitive to product quality, pricing, and the promotional effort of a green inventory model. This study contributes guidance to retailer managers on the optimal price and replenishment cycle given a carbon pricing regulation and green technology investment plan. This study maximizes the total profit using the classical optimization technique. Two cases are involved in representing the inventory model with and without green technology investment. The carbon pricing regulations were studied. Essential theoretical derivations have been provided to show the concavity of the anticipated model. The results prove the advantages of green technology investment and carbon pricing implementation. The numerical example and sensitivity analysis are shown, and the cap-and-trade policy works efficiently within constraints to curb carbon emissions. The manager established an optimal selling price and the replenishment cycle period by forecasting the amount of investment in green technology and some transportation cost variables.

Coordination of a single-manufacturer multi-retailer supply chain with price and green sensitive demand under stochastic lead time

When dealing with uncertainties in the supply chain and ensuring customer satisfaction, efficient management of lead time plays a significant role. Likewise, besides managing inventory and pricing strategies adeptly in multi-retailer supply chains, it has become inevitable for firms to embrace green and sustainable business practices. In this context, this paper considers a two-level supply chain consisting of a single manufacturer and multiple retailers in which the manufacturer produces a single product and delivers it to the retailers in equal-sized batches. Each retailer faces a price and green-sensitive market demand. The lead time is assumed to be a random variable that follows a normal distribution. Shortages for retailer inventory are allowed to occur and are completely backlogged. The centralized model and a decentralized model based on the leader-follower Stackelberg gaming approach are developed. A price discount mechanism between the manufacturer and retailers is proposed. For the acceptance of this contract, the upper and lower limits of the price discount rate are established. Numerical outcomes exhibit that the price discount mechanism effectively coordinates the supply chain and enhances both environmental and economical performances. A sensitivity analysis with respect to some key parameters is performed, and certain managerial insights are emphasized.

Impact of advertisements on retailer's inventory for time-varying perishable rate with preservation technology investment under customer return and trade credit policy

In any business worldwide, inventory management is a challenging task for every organisation. This article demonstrates an inventory model for deteriorating products from the retailer's point of view in which: 1) demand is determined by price and frequency of advertisement; 2) the influence of trade credit on expanding small business and their consumers is the focus of this research; 3) product deteriorates with an expiry date; 4) retailer invests money in preservation technology to preserve the deterioration of a product; 5) customer returns the product to the company are the significant factors among the interrelation of supplier and retailer for a highly demanding product. The main objective of this article is to optimise the retailer's total profit in terms of cycle length, price, frequency of advertisement, and capital in preservation technology. Also, for optimality, an algorithm is created. Finally, the results were validated by solving two numerical illustrations and conducting sensitivity analysis of the main factors resulting from the following managerial implications: 1) if the retailer receives a longer allowable delay period from the supplier, the retailer can maximise total profit; 2) an increase in the ordering cost per order the overall profit decreases.

A Retailer's Inventory Model for Deteriorating Items under Power Pattern Demand with Shortages Partially Backlogged in both Crisp and Fuzzy Environments

A Retailer's Inventory Model for Deteriorating Items under Power Pattern Demand with Shortages Partially Backlogged in both Crisp and Fuzzy Environments

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 .

Optimizing the inventory and fulfillment of an omnichannel retailer: a stochastic approach with scenario clustering

We study an inventory optimization problem for a retailer that faces stochastic online and in-store demand in a selling season of fixed length. The retailer has to decide the initial inventory levels and an order fulfillment policy such that the expected total costs are minimized. We approximate the problem by a two-stage stochastic optimization on a reduced number of scenarios. For deciding the representative scenarios, we propose a new similarity measure and a novel technique that combines the framework of Good–Turing sampling and Linear Programming. On randomly generated instances, the proposed algorithm obtains an average cost reduction of 7.56% compared to a state-of-the-art algorithm in the literature. The proposed algorithm works considerably better for short time horizons and a relatively large proportion of in-store customers.

Retailer's credit preference under perfect competition Based on the Stackelberg model

Trade credit is provided by suppliers to meet the purchase needs of retailers, playing a more and more important role in trade. In this paper, the effects of preferential credit on retailers' business decisions are investigated under capital constraints in a perfectly competitive market. In this game model, the loan competition conditions are considered when banks and manufacturers offer preferential credit to capital-constrained retailers. In the proposed model, different credit lines and preferential credit discount rates are mainly related to the exogenous collateral of retailers and the risk appetite of banks and manufacturers. The effects of bank financing and trade credit on a retailer's inventory decision are also discussed under different circumstances of whether the retailer's financing amount exceeds the credit limit. The equilibrium wholesale price and optimal order quantity of retailers under different circumstances are deduced. Moreover, the effects of discount rate, institutional risk preference and collateral quality on the two are obtained. These results reveal that the factors affecting the decision-making of retailers and manufacturers are also different in different situations.

Multiperspective Analysis of Pandemic Impacts on U.S. Import Trade: What Happened, and Why?

U.S. container ports have experienced unpresented congestion since mid-2020. The congestion is generally attributed to import surges triggered by heavy spending on consumer goods during the COVID-19 pandemic. Port congestion has been compounded by the inability of importers to retrieve, receive, and process all the inbound goods they have ordered, resulting in supply chain shortfalls and economic disruption. How can the shipping industry and government organizations predict the end of the current surge and anticipate future surges? Expected seasonal variations in import volume are associated with peak holiday shopping periods; nonseasonal import surges are signaled by other factors. The research goes beyond transportation data sources to examine broader connections between import volume and indicators of economic and retail industry conditions. The strongest and most useful relationship appears to be between retail inventory indicators and containerized import growth. From January 2018 through July 2021, there was a relatively strong negative correlation between retail inventory- and import TEU indices with a 4-month lag (corresponding roughly to the time between import orders and -arrival). In the 2020 to 2021 pandemic period the negative correlation was stronger, again with a 4-month lag. These findings suggest that observers might anticipate import surges after marked, nonseasonal drops in retail inventories, and that import surges are likely to last until target inventory levels are restored. In a broader sense, an awareness of the linkages between consumer demand, retail chain responses, and containerized import volumes could better inform port, freight transportation, and government planning and policy choices.

A shortfall modelling-based solution approach for stochastic cyclic inventory routing

This paper studies the cyclic inventory routing problem with stochastic demand. A geographically dispersed set of retailers with stochastic demand rates is replenished from a single depot using vehicles with limited capacity. For an infinite horizon, a fixed-partition policy is adopted that partitions the retailers into subsets that are always replenished together in the same route being cyclically repeated. The objective is to provide cost efficient buffering of the demand variability within a cyclic distribution plan by providing carefully calibrated safety stock levels at the retailers. In doing so, the vehicle capacity needs to be taken into account, since cumulative demand during a cycle of the retailers in a route may exceed this capacity. In that case, shortfall remains at the retailer inventories because they are not fully replenished, which affects the service level (and cost balance) in the consecutive cycle(s). An approximate method is presented for determining the safety stock levels and is integrated into a state-of-the-art metaheuristic solution approach for cyclic inventory routing. An illustrative example and experiments on benchmark instances show (i) the effect of the vehicle capacity on the cost balance in a route, (ii) the accuracy of the approximation, and (iii) the added value of taking demand variability and shortfall due to limited vehicle capacity into account during the route design.

Optimizing service level, price, and inventory decisions for a supply chain with retailers’ competition and cooperation under VMI strategy

In a vendor-managed inventory (VMI) system, a manufacturing vendor manages their retailer inventories. Studies on VMI-type supply chains mostly have not considered competition between retailers. There are few works on the price competition; however, to the best of the authors’ knowledge, none of the papers formulated a service competition strategy. The service level is one of the competitive factors among competing retailers. Sometimes retailers choose to compete cooperatively instead of competing independently with the manufacturer. The present work investigates service, price, and inventory decisions under retailers’ competition and cooperation. Considering the manufacturer and retailers as the leader and followers, respectively, a Stackelberg game model of the problem is developed. The present study proposes a solution algorithm to search the Stackelberg–Nash equilibrium in the retailer cooperation and retailer independence models. The algorithm is numerically demonstrated to explore the impacts of decision parameters. To validate the model, a number of parameters are subjected to sensitivity analyses. It was found that a higher self-service (cross-service) level parameter would lead to higher (lower) profits of the retailer and manufacturer and the total profit in the two models. Retailer cooperation enhances retailer performance; however, manufacturer and system profits decline. Furthermore, when retailers cooperate, they are motivated to offer lower service levels.

Retailer Inventory Sharing in Two-Tier Supply Chains: An Experimental Investigation

When multiple retailers hold inventory to satisfy random demand, retailer inventory-sharing strategies can potentially reduce the supply-demand mismatch and increase overall supply chain performance. In this paper, we experimentally investigate alternative inventory-sharing strategies in a two-tier supply chain with an upstream manufacturer and two downstream retailers. In one setting, retailers act as if they are centralized and use a single quantity to fulfill joint demand. In the other, retailers are decentralized and face separate demands, but they can transfer inventory after demands are realized. In this latter decentralized scenario, we also consider whether the manufacturer or retailers have decision authority over the inventory transfer price. One key result is that when the retailers are decentralized and the manufacturer sets the transfer price, both retailers and the manufacturer earn higher profits than in the centralized retailer strategy, which runs counter to theory. We also find that when retailers are decentralized and set their own transfer price, the most equitable distribution of profits is achieved. In an effort to account for these results, we find that a model of fairness captures decisions well. Overall, by investigating how different inventory-sharing strategies affect the distribution of profits in a two-tier supply chain, our results provide guidance to firms considering how, if at all, they should enter such arrangements.This paper was accepted by Jay Swaminathan, operations management.Funding: The authors acknowledge financial support from Cornell University and the University of Virginia.Supplemental Material: The data files and electronic companion are available at https://doi.org/10.1287/mnsc.2022.4323 .

Commercialising traditional market shares: A qualitative study of impacted peasant livelihoods in Copperbelt province, Zambia

ABSTRACT The perceived impact of commercialisation on peasant livelihoods was examined in this qualitative study. To understand the scope of commercialisation for open-air goods, an inventory of large grocery retailers and open-air markets (OAMs) in Zambia’s Copperbelt Province was completed. Interviews were conducted with sellers (n = 100) and urban consumers (n = 40). More than 80% of market sellers reported a reduction in sales and more than 80% of households indicated they shopped less often at OAMs because they could buy similar items cheaper at large retailers. Findings suggest targeted investment in seller cooperatives and small retailers would improve livelihoods and urban food security.

A Multi-Objective Model for Inventory Management of the Integrated Multi-Product Supply Chain Considering Central Warehouse and Potential Demand

In this research, an inventory management model is developed by the seller for a three-echelon supply chain, which also includes a central warehouse, along with order constraints. In this method, the seller monitors the level of inventory of the retailer so that it determines the time and amount of products for the seller and the retailer and reduces the inventory costs for the seller and the retailer. Sales inventory management in the supply chain has an integrated approach to planning and controlling materials and information in the production and distribution chain, transportation, warehouses and customers. One of the objectives of the proposed mathematical model is to determine the inventory of retailers, vendors, central warehouse and the optimal refund rate for the seller and central warehouse, which ultimately leads to a reduction in the cost of the entire system. In this study, after presenting a three-objective mathematical model to study the three-level supply chain by considering several retailers and multiple vendors and the central warehouse, the model is solved using GAMS software by the Epsilon constraint method and to test. The validation of the proposed model is examined by providing an example of the proposed model.

Inventory financing under Risk‐Adjusted‐Return‐On‐Capital criterion

AbstractRisk‐Adjusted‐Return‐On‐Capital (RAROC) is a loan‐pricing criterion under which a bank sets the loan term such that a certain rate of return is achieved on the regulatory capital required by the Basel regulation. Some banks calculate the amount of regulatory capital for each loan under the standardized approach (“standardized banks,” the regulatory capital is proportional to the loan amount), and others under the internal rating‐based (IRB) approach (“IRB banks,” the regulatory capital is related to the Value‐at‐Risk of the loan). This article examines the impact of the RAROC criterion on the bank's loan‐pricing decision and the retailer's inventory decision. We find that among the loan terms that satisfy the bank's RAROC criterion, the one that benefits the retailer the most requires the bank to specify an inventory advance rate in addition to the interest rate. Under this loan term, the retailer's inventory level is more sensitive to his asset level when facing an IRB bank compared to a standardized bank. An IRB (standardized) loan leads to higher profit and inventory level for retailers with high (low) asset. For retailers with medium asset, an IRB loan results in a higher retailer profit but a lower consumer welfare. Calibrated numerical study reveals that the benefit of choosing standardized banks (relative to IRB banks) can be as high as 30% for industries with severe capital constraints, volatile demands, and low profit margins, highlighting the importance for retailers to carefully choose the type of banks to borrow from. When the interest rate is capped by regulation, retailers borrowing from a standardized bank are more likely to be influenced by the interest rate cap than those borrowing from an IRB bank. Under strong empire‐building incentives (the bank will offer loan terms to maximize the size of the loan), retailers with medium initial asset level shift their preference from IRB banks to standardized banks.

SISTEM PENGENDALIAN INTERNAL MONITORING INVENTORY OBAT MENGGUNAKAN SUPPLY CHAIN MANAGEMENT

Purna Husada merupakan Apotik yang menyediakan berbagai macam obat-obatan, prosedur pengendalian intern dan monitoring persediaan yang dilakukan hanya menggunakan nota barang dan kartu stok, proses permintaan barang yang dilakukan oleh perusahaan masih menggunakan media telepon dengan memberikan data obat yang akan dipesan serta stok barang yang masih diolah pada buku catatan dan pentingnya kebutuhan akan obat untuk konsumen harus dapat terpenuhi. Supply Chain Management merupakan salah satu proses dimana aliran bahan baku, informasi, serta keuangan antar perusahaan terjadi dalam tujuan memenuhi pemesanan konsumen. Proses yang dilakukan untuk memenuhi pasokan bahan baku dimulai dari permintaan retail oleh distributor dan permintaan konsumen oleh retail, proses rantai pasokan yang baik dapat memenuhi pasokan yang setabil dengan tujuan tidak ada kekurangan bahan baku atau bahan jadi. Supply chain management dilakukan untuk memperbaiki proses pasokan yang dilakukan oleh bagian pemasok, produsen, distributor, pengecer, dan pelangga, konsep tersebut merupakan rantai pasokan multi-layer yang terdiri dari material akhir yang memiliki banyak produk yang siap jual. Proses tersebut masih memiliki masalah seperti ketidak stabilan persediaan karena proses konfirmasi mengenai permintaan obat ke distributor harus mengecek ketersediaan yang ada di distributor serta ksalahan penulisan persediaan pada kartu stok dan kesalahan perekapan data persediaan obat, sehingga perlu adanya pemanfaatan teknologi berupa sistem informasi untuk mempermudah proses permintaan dan monitoring oleh pimpinan dengan menggunakan pendekatan Supply Chain Management yang mampu mengkoordinasikan pasokan dari beberapa bagian seperti retail, distributor dan konsumen dengan tujuan untuk menstabilkan persediaan pada retail. Hasil pengujian functionality mendapatkan hasil persentase sebesar 98,46%, serta hasil pengujian usability mendapatkan hasil persentase sebesar 98,33%

A Simulation for Managing Retail Inventory Flow Using RFID and Bar Code Technology

ABSTRACTThe increasingly complex retail environment faces many inventory management challenges. Automatic identification and data capture (AIDC) technologies have been applied by retailers to address these challenges, with mixed levels of success. This article presents a classroom simulation of a retail environment in which students utilize a bar code scanner and a radio frequency identification (RFID) reader to manage inventory for the simulated retailer. From the simulation, students gain hands‐on experience with these technologies, learn about the advantages and disadvantages of the technologies, and discuss additional cutting‐edge AIDC technology.