Inventory Strategy Research Articles

Inventory Control Model of Beef for Rendang Products

The definition and function of inventory management reveal the importance of modern industry in formulating policies that regulate the supply of raw materials, semi-finished products, and finished products. Unfortunately, most companies fail to consider the characteristics of their raw materials when determining inventory strategies. Raw materials that are perishable are those that consider their service life and storage time. Inventory management plays a crucial role in supply chain management, especially for perishable raw materials, such as food products. PT X, a food business in Padang, experienced difficulty in meeting the demand for rendang products due to a lack of raw materials. Therefore, this study aims to develop an inventory management model that takes into account the perishable raw materials' expiration time. The model development consists of three stages: model development design, inventory model formulation, and model testing. The proposed model resulted in a storage time interval of five days and an optimal order quantity of 34 kg of meat with a safety stock of 14 kg. Implementing this model led to lower total inventory costs for PT X than the actual conditions of the company. The total inventory cost obtained using this model is Rp279,797,822. This study emphasizes the importance of considering the characteristics of raw materials in determining inventory strategies to optimize inventory management effectively and efficiently. The study's findings can serve as a reference for other food businesses encountering similar inventory management challenges in the perishable food industry.

A Comparison of Inventory Strategies for a Manufacturer within the Air Conditioning Industry

The present manuscript describes the application of two inventory strategies to optimize the supply process within an air conditioning manufacturing facility. These approaches were considered as the air conditioning industry involves variable demand patterns due to its large market (automotive, home, cooling/heating systems, among others). Because the components which are assembled in this factory involve 45 components which have different demand, lead times and inventory management costs, the strategies were adjusted to reduce costs and management complexity. First, inventory management costs were optimized according to the classification of the most important components. Then, periodic and continuous review models were adapted to optimize the management costs of these components. Validation of these strategy through discrete-event computer simulation led to determine their suitability for this case study, significantly reducing inventory management costs while increasing service level. Simulation also led to determine that for specific components, a model can lead to better (and feasible) results than the other and showing no stock-out events. This is an outcome which can be considered by companies to make a better evaluation of the supply strategies during the planning process.

Ramification of information asymmetry on a green supply chain management with the cap-trade, service, and vendor-managed inventory strategies

The competition in the supply chain business has become considerably challenging. The environment protection is another problem adjacent to the competitive business. The supply chain players follow carbon policies to reduce carbon emissions along with profit maximization. But, asymmetric information sharing among supply chain players affects supply chain profit. This information asymmetry may be profitable for a few supply chain players but not beneficial for an entire supply chain and it may cause a non-coordination within them. A green supply chain management is developed with carbon tax & cap and trade regulations, where information asymmetry is reduced by the technology support through a radio frequency identification. This study finds the best service and distance between radio frequency identification readers to reduce asymmetry, and the optimum order quantity to obtain the maximum total profit for coordination or non-coordination policy. The objective function becomes a non-linear function with inequality constraint and the optimum solutions are derived using Kuhn–Tucker conditions. The optimum values of the objective function are found by using two subproblems because of the alternative scenarios of the cap and trade policy. Results indicate that a non-coordination policy is a better strategy than a coordination policy when information and product security play a major role in a green supply chain management. The effects of changes in cost parameters are identified via sensitivity analysis. The profit of the non-coordination policy is 6.07% more than the coordination profit.

Prospects for the Development of Transport in Poland during the Energy Crisis

Recent years have been times of crisis triggered by the Covid-19 pandemic and the Russian attack on Ukraine. Energy markets have seen dynamic increases in commodity prices, which have continued to this day. The prices of oil, whose rise fuels inflation are particularly important for the world economy. The paper presents an overview and per- prospects for the development of transport in Poland during the energy crisis and the Covid-19 pandemic. The analysis carried out on the example of Poland clearly shows that in the near term there is no chance to reduce fuel prices in Poland, which would result in a decrease in transport costs. It seems that the only chance for companies to reduce transport costs is to change their supply and inventory management strategies to take advantage of economies of scale and maintain high inventory levels. On the other hand, in world markets, oil prices will fall only if the war in Ukraine finishes.

Managing Operations of a Hog Farm Facing Volatile Markets: Inventory and Selling Strategies

Problem definition: We study a dynamic finishing-stage planning problem of a pork producer who at the beginning of each week gets to see how many market-ready hogs she has available for sale and the current market prices. Then, she must decide which hogs to sell to a meatpacker and on the open market and which hogs to hold until the following week. The farmer is contracted to deliver a fixed quantity of hogs to the meatpacker each week priced according to a contractually predetermined market index. If the farmer underdelivers to the meatpacker, she pays a contractually predetermined unit penalty also linked to a market index. Biosecurity protocols prevent the farmer from buying hogs on the open market and selling them to the meatpacker. The farmer can, however, use the open market to sell hogs for prevailing market prices. Methodology/Results: We treat the problem as a dynamic, multiitem, nonstationary inventory problem with multiple sources of uncertainty. The optimal policy is a threshold policy with multiple price-dependent thresholds. The computational complexity required to evaluate the thresholds is the biggest impediment to using the optimal policy as a decision-support tool. So, we utilize an approximate dynamic programming approach that exploits the optimal policy structure and produces a sharp heuristic that is easy to implement. Managerial implications: Numerical experiments calibrated to a pork producer’s data reveal that the optimal policy with the heuristically estimated thresholds substantially improves the existing practice (around 25% on average). The success of the proposed model is attributed to recognizing the value of holding underweight hogs and effectively hedging supply uncertainty and future prices—an insight missed in the planning actions of the current practice.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1216 .

Logistics management in e-commerce: challenges and opportunities

Logistics management is a crucial component of success for companies operating in the retail industry. As the popularity of online shopping continues to grow, businesses must implement efficient logistics practices to satisfy customers, cut costs, and maintain a competitive edge. This article offers a comprehensive overview of logistics management in e-commerce, with a focus on identifying challenges, opportunities, strategies, and solutions that can help companies improve their logistics processes and succeed in a highly competitive market. It was employed the methodology of a literature review, which involves a structured and organized analysis of published scientific articles related to the topic. The study follows a five-step process for conducting a comprehensive and critical review of knowledge related to logistics management. The literature review conducted analyzed a selection of 70 studies and highlighted numerous noteworthy challenges in logistics management faced by e-commerce companies, such as managing order volume and storage, accurately tracking inventory, and ensuring timely shipping and delivery. To overcome these challenges, companies can invest in infrastructure, inventory management strategies, transportation management systems, optimization of delivery routes, and emerging technologies such as drones and robots for deliveries. The adoption of these technologies can significantly enhance logistics process efficiency, minimize errors, and boost customer satisfaction. By implementing efficient logistics practices, companies can offer their customers a smooth and hassle-free shopping experience, which can translate into long-term customer loyalty and higher sales.

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.

Inventory strategy of fresh products for omni-channel supply chains

By constructing the consumers’ utility function, this article calculates the optimal order quantity and optimal expected profit of a fresh retailer under the centralized and decentralized inventory strategy in an omni-channel supply chain. In addition, the impact of consumers’ channel preference, consumers’ demand transfer behavior, and the "buy-online-pickup-in-store" (BOPS) service on retailers’ inventory strategy are analyzed. The results show that opening a BOPS service is not always beneficial to retailers. However, it is always beneficial for retailers to eliminate the differences between channels. Under the centralized inventory strategy, the optimal order quantity and expected profit are only affected by the freshness-keeping cost, while under the decentralized inventory strategy, they are affected by various consumer behaviors. A reasonable inventory management strategy should be adopted according to the product type, inventory cost, consumer preferences, and order quantity.

Modelling and Analysis of Hospital Inventory Policies during COVID-19 Pandemic

The global coronavirus pandemic (COVID-19) started in 2020 and is still ongoing today. Among the numerous insights the community has learned from the COVID-19 pandemic is the value of robust healthcare inventory management. The main cause of many casualties around the world is the lack of medical resources for those who need them. To inhibit the spread of COVID-19, it is therefore imperative to simulate the demand for desirable medical goods at the proper time. The estimation of the incidence of infections using the right epidemiological criteria has a significant impact on the number of medical supplies required. Modeling susceptibility, exposure, infection, hospitalization, isolation, and recovery in relation to the COVID-19 pandemic is indeed crucial for the management of healthcare inventories. The goal of this research is to examine the various inventory policies such as reorder point, periodic order, and just-in-time in order to minimize the inventory management cost for medical commodities. To accomplish this, a SEIHIsRS model has been employed to comprehend the dynamics of COVID-19 and determine the hospitalized percentage of infected people. Based on this information, various situations are developed, considering the lockdown, social awareness, etc., and an appropriate inventory policy is recommended to reduce inventory management costs. It is observed that the just-in-time inventory policy is found to be the most cost-effective when there is no lockdown or only a partial lockdown. When there is a complete lockdown, the periodic order policy is the best inventory policy. The periodic order and reorder policies are cost-effective strategies to apply when social awareness is high. It has also been noticed that periodic order and reorder policies are the best inventory strategies for uncertain vaccination efficacy. This effort will assist in developing the best healthcare inventory management strategies to ensure that the right healthcare requirements are available at a minimal cost.

Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses

Small and medium-sized businesses are constantly seeking new methods to increase productivity across all service areas in response to increasing consumer demand. Research has shown that inventory management significantly affects regular operations, particularly in providing the best customer relationship management (CRM) service. Demand forecasting is a popular inventory management solution that many businesses are interested in because of its impact on day-to-day operations. However, no single forecasting approach outperforms under all scenarios, so examining the data and its properties first is necessary for modeling the most accurate forecasts. This study provides a preliminary comparative analysis of three different machine learning approaches and two classic projection methods for demand forecasting in small and medium-sized leathercraft businesses. First, using K-means clustering, we attempted to group products into three clusters based on the similarity of product characteristics, using the elbow method's hyperparameter tuning. This step was conducted to summarize the data and represent various products into several categories obtained from the clustering results. Our findings show that machine learning algorithms outperform classic statistical approaches, particularly the ensemble learner XGB, which had the least RMSE and MAPE scores, at 55.77 and 41.18, respectively. In the future, these results can be utilized and tested against real-world business activities to help managers create precise inventory management strategies that can increase productivity across all service areas.

Improving Warehouse Efficiency Through Effective Inventory Management Practices

Several business establishments have used inventory management strategies to strike a balance between responsiveness and efficiency. This study's primary objective was to assess the effect of inventory management practices on the warehouse efficiency of supermarkets and retail stores in Kedah and Perlis, specifically supermarkets. This study utilises a quantitative method by using primary data from questionnaires and secondary data compiled from prior studies' results. Statistical Packages for the Social Science (SPSS) version 25 software was used to analyse the data. The relationship between the independent and dependent variables was determined using regression analysis. The outcome showed that the inventory reorders point and supplier partnership management variable were accepted. However, the information technology hypothesis was rejected. This study recommends that supermarkets and retail stores utilise the best inventory management strategies to increase warehouse efficiency. It enables the tracking of stock unit movement in the warehouse, facilitates stock counting in the warehouse, maximises the use of available warehouse space, improves inventory data accuracy, reduces inventory theft, and enhances inventory cycle counting.

Sustainability of Supply Chain Performance Through Inventory and Collaboration Management: A Case of Transportation Fuel Companies in Harare, Zimbabwe

The study was based on the theory of constraints which acknowledges that bottlenecks are systemic, unavoidable, and affect the performance of the organisational supply chain processes. In the study, poor inventory and collaboration management were identified as bottlenecks in the transportation fuel retail sector. Thus, identifying the indicators of poor fuel supply chain performance can assist the industry in achieving a sustainable performance. In the descriptive design, the researchers distributed objective questionnaires among fuel industry supply chain staff whose roles include inventory and collaboration management decisions which are strategic to supply chain performance. The respondents gave their views on the effect of selected inventory and collaboration management indicators on supply chain performance. The analysed results indicate that there is a positive relationship between both inventory and collaboration management and the fuel supply chain performance. These findings led to the conclusion that, achieving supply chain performance goals of profitability, customer satisfaction, internal process effectiveness and company growth requires management to implement inventory and collaboration management strategies. Therefore, fuel industry supply chain stakeholders including government, fuel companies, fuel suppliers, customers and third party logistics service providers need to cooperate and work together to pull the struggling fuel industry from the doldrums.

Simulation-Based Approach for Multi-Echelon Inventory System Selection: Case of Distribution Systems

Due to the current complexity of the supply chain, multi-echelon inventory management has become challenging while also being an interesting field of research as it allows efficient control of supply chain interdependencies. It became clear to many researchers that analytical models are no longer effective for addressing the multi-echelon inventory management problem. Simulation can be used to assess and quantify the impact of each inventory strategy on a supply chain performance. Our paper aims to provide a simulation-based approach to guide decision makers select and validate a multi-echelon distribution inventory system. The proposed approach is composed of four major steps that involve characterization of the current supply chain, conceptual modeling of the multi-echelon inventory system alternatives, and finally, simulation modeling using appropriate simulation software to compare and test different options. The approach was also tested and validated through an application to the case of the Moroccan pharmaceutical products supply chain in the public sector. The results of the simulation demonstrated that adopting an installation stock policy at all levels of the supply chain with an allocation of safety stocks in the most downstream stages is the best and most appropriate alternative for the pharmaceutical supply chain under study.

Psychological factors associated with bruxism in a sample of university students from the Colombian Caribbean

IntroductionWithin the theories that explain tooth grinding are dental factors, skeletal malocclusions, occlusal anomalies and defective reconstructions, however, there would be psychological factors that explain this phenomenon.ObjectivesThe aim of the study is to evaluate anxiety, depression and coping responses in university students who self-report clenching or grinding teeth.MethodsDesign: Non-experimental, cross-sectional, and quantitative.Sample: University students (n=25) aged between 18-25 years (mean: 25.3; SD: 2.39), purposive sampling. Participants completed a self-reported questionnaire reporting teeth clenching/grinding habits.Instruments: Standardized instruments were used, such as the State Trait Anxiety Inventory (STAI), Beck’s Depression Inventory (BDI) and Coping Strategies Scale Modified (CSS-M).Procedure: First, the study was disclosed, the participants were informed and signed informed consent. They were asked to complete an instrument on teeth grinding/clenching habits and if it was fulfilled, it was included in the study and the psychological evaluation instruments were administered.Data analysis: An analysis was made using descriptive statistics.ResultsThe STAI results showed a high Anxiety-State in all the participants and the Anxiety-Trait had a prevalence of 92.3%. Regarding the levels of depression, it was evidenced that 7.7% presented moderate depression and 31.6% showed mild symptoms.The most used coping strategies were problem solving (87.2%), positive reappraisal (74.4%) and religious support (71.8%), while the least used were seeking professional help (92.3%), waiting (76.9%), aggressive reaction (74.4%) and expression of coping difficulty (71.8%).ConclusionsUniversity students must cope with an academic load that exceeds their capacity to face academic challenges (Wikes et al., 2019). This demand causes significant discomfort that increases emotions with a high negative charge and favors the appearance or intensification of mental health problems, such as chronic stress, anxiety, depression, nervousness and behavioral disorders.Disclosure of InterestNone Declared

An integrated approach for optimizing location-inventory and location-inventory-routing problem for perishable products

The grocery market, especially the perishable product market is under amazing developments in China, accounting for half of overall food expenditure. However, the supply chain of perishable products has encountered serious problems, including significant freight losses, high inventory and transportation costs, etc. To solve these problems, the current paper proposes an integrated approach for simultaneously optimizing the decisions involved in the supply chain, typically including location, inventory, and routing strategy. A three-echelon supply chain including suppliers, DCs and retailers is structured in this paper, with the impacts of whether allowing direct shipment from supplier to retailer being considered. Two typical problems are investigated: location-inventory problem (LIP) and location-inventory-routing problem (LIRP). Mixed integer nonlinear programming model for LIP is proposed while Mixed integer linear programming models for LIRP is proposed, with the objective of minimizing the overall costs of supply chain, which are composed of infrastructure construction costs, transportation costs, inventory costs, and freight losses. CPLEX is used for LIP model solution and a two-stage heuristic algorithm based on Simulated Annealing is designed to solve the LIRP model. The proposed mathematical models and solution method are then applied to a real supply chain network for fresh milk. The optimized results suggest that considering the direct shipment from supplier to retailer is beneficial in terms of reducing the overall supply chain costs by 31%, with 43% reductions in transportation costs, 69.4% reductions in freight losses, 99.4% increases in inventory costs. With the developments of e-retailers for perishable products, allowing the direct shipment from supplier to retailer will bring more benefits in the future.

Integration design and control strategy of sCO 2 Brayton cycle for concentrated solar power system

The concentrated solar power (CSP) system integrated with supercritical carbon dioxide (sCO 2) Brayton cycle is considered as the major development trend of clean energy technology in mitigating climate change and promoting sustainable energy owing to its more compact structure, higher efficiency and lower cost. While the system integration and control are more complex because the thermal energy storage and heat exchange units are obliged to be provided with capacity and property matching with sCO 2 compression and expansion units. Therefore, there is an urgent need for the whole system modeling and dynamic simulation of sCO 2-CSP plants. In the paper, running logic of the integrated system is determined and novel system dynamic simulation platform is built. Moreover, for the sake of optimizing the system design, different control strategies are further studied. When the extremum-seeking control strategy is used, the average daily generating capacity of the system is 2.06% higher than that of the traditional fixed inventory control strategy; and when iterative learning control strategy is used, the average daily generating capacity of the system is 2.15% higher than that of the traditional fixed inventory control strategy. The progress of the technology will promote the development of new power systems and early realization of carbon neutrality.

Development of city-scale air pollutants and greenhouse gases emission inventory and mitigation strategies assessment: A case in Zhengzhou, Central China

The development of anthropogenic air pollutants and greenhouse gases (GHGs) emission inventory is important for air pollution and climate change. This study developed a high-resolution emission inventory of consistent sources of air pollutants and GHGs in Zhengzhou. Designed scenarios with 2030 as the target year to estimate future emissions from power plants and on-road mobile. The 2019-based total emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, CO2, CH4, and N2O were 39.2, 174.6, 586.9, 134.1, 59.2, 176.8, 19.3, 83,014.8, 239.3, and 4.3 kt, respectively. Stationary combustion and mobile source were the top contributors to SO2, NOx, CO, and CO2 emissions. PM10 emissions were mainly from fugitive dust, while CH4 emissions were other sources. Most PM2.5 and VOCs emissions came from industrial process. Agriculture was the primary source of NH3 and N2O emissions. Most air pollutants and GHGs were mainly concentrated in the main urban area, which were closely related to population density, traffic flow, and industrial activities, while winter heating and higher electricity consumption in summer were the main reasons for higher emissions during these seasons. Air pollutants and GHGs from power plants and on-road mobile are reduced by 16.0%–84.7% in the best reduction scenario.

Unsupervised Anomaly Detection for Intermittent Sequences Based on Multi-Granularity Abnormal Pattern Mining

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results.

Effect of Future Price Increase for Products with Expiry Dates and Price-Sensitive Demand under Different Payment Policies

The current study works with an inventory management strategy under the discount cash flow approach for perishable commodities with expiry dates, price-sensitive demand, and investment in preservation technology. In addition, this study examines the probable influence of price-increase on the replenishment strategy of the retailer where specific delivery units can be purchased. Furthermore, in this model, two circumstances are deliberated: (I) when the time of the specific delivery matches with the reordering time of the retailer or (II) when the time of the specific delivery emerges within the duration of the sale. Before the price increase, the supplier provides two payment policies to the retailer from which they can choose one. The policies are either: (1) a permissible delay in payment on regular orders or (2) a discount in payment for the specific delivery. The key goal is to optimize the overall profit for the retailer with respect to the sales price, investment in preservation technology, and cycle time during the depletion time of the specific delivery. In addition, an algorithm is created to optimize the results and seven numerical illustrations are discussed to explain the results along with the special case. Finally, to display the pertinence of this model, a sensitivity analysis of the main parameters is performed with important managerial implications. The key findings of this research are (1) before the price increase, the retailer gets the maximum profit if the retailer chooses a discount in payment policy on the specific delivery; (2) how much to order from the supplier and when to place a specific delivery to generate a maximum profit; and (3) the price-sensitive demand and assumption of future price increase negatively affect the retailer’s overall profit, and the retailer gets maximum benefits if the retailer initially orders the maximum number of units from the supplier before the price increase.

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.