Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

Methods of vendor managed inventory control in supply chain management

By inducing the typical inventory control problem - the bullwhip effect, this paper presents vendor managed inventory (VMI) control methods on the basis of traditional methods of inventory management methods, constructs a VMI mathematics model, and analyzes the influence of VMI on inventory cost and channel profit. Finally, a special case is studied to verify that VMI is an effective supply chain strategy that can not only increase channel profit of supplier and customer but also improve full channel coordination, thereby reducing the bullwhip effect.

Quantity-based shipment consolidation and stock replenishment policy with lead time in vendor-managed inventory system

In order to let the supplier make more reasonable supply decisions,an integrated continuous replenishment policy for the vendor-managed inventory system is presented,which considers the quantity-based shipment consolidation and stock replenishment with lead time.Then the system cost is analyzed and a mathematical model is built.Since the model is rather complex,the bounds of the optimal policy are first attained,then the problem is solved by a heuristic algorithm.Through experiments the relationship between the order lead time and the corresponding integrated policy is discussed,and the influence on the system cost is also analyzed.The results reveal that the lead time's influence on the system is more serious with the increase of the order lead time,the integrated policy with the order lead time is more reasonable and the optimal policy can minimize the total system cost.Finally,the parameter sensitivity of the model is analyzed.

A Model on Inventory and Supply Management under VMI Tactics

This paper puts emphasis on how to arrange the stock and supply to the customer rationally under the VMI tactics. Having considered the supplier＇s delivery expenses, memory expenses and loss in short supply in model, the minimum total cost goal model of supply chain has been put forward using method of storing theory to charge example test the method of solving and analyzing the conclusion to propose.

Study on Inventory Cost Control under JIT System

Traditional inventory control methods generally emphasize minimizing costs. Under current thinking, we not only consider cost control, but also tie inventory management directly into production scheduling. This system, called ＂Just-in-Time delivery＂, is based on having inventory arrive at the factory door just as it is needed on the assembly line. This inventory management technique of essential zero raw material inventory speeds inventory turnover rate and greatly reduces holding cost. This paper analyzes and compares all aspects related to both traditional and JIT systems, and explores ways of reducing inventory costs. The result is the elimination of waste, the speed-up of inventory turnover and the increase in productivity, all with the objective of maximizing profitability. Using advanced logistics （physical distribution methods） will enhance the business image, strengthen international competitive ability and contribute greatly to increase gross domestic product.

Research on inventory management and demand forecasting

With the rapid economic development and the increasing speed and scale of grid construction, material procurement and management, cost control is facing new demands and challenges.This paper proposes on innovative management and forecasting methods, from inventory management and demand forecasting perspective supplies,through these two key nodes in-depth research and analysis, this paper provides a theoretical support for the realization of effective materials management.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.

Vendor managed inventory and bullwhip effect

This paper studies that the bullwhip effect of order releases and the amplifications of safety stock arise within the supply chain even when the demand model is ARIMA(0, 1, 1) and the forecast method used is a simple exponentially weighted moving average. It also examines a vendor managed inventory (VMI) program to determine how it can help alleviate such negative effects, and gives the theoretical proofs and numerical illustrations. The results show that the effects with VMI are better than the effect without VMI in demand forecasting and safety stock levels, etc.

Comparative research on three-echelon and two-echelon medicine inventory model with positive lead-time

According to the principle of minimizing total cost, the three-echelon optimized medical inventory model with stochastic lead-time and two-echelon optimized medicine inventory model with fixed lead-time are established. The relationship between lead-time and inventory cost is studied by Matlab software. It shows that the variety of lead-time has an important effect on medicine inventory systems. Numerical simulation and sensitivity analysis of two models are presented by Lingo software. Based on analysis, it is concluded that the two-echelon model with lead-time results in inventory cost savings, and keeps the quality of care as reflected in service levels when compared with the three-echelon network structure.

Integrated continuous replenishment planning in VMI system of retail industry

In order to let suppliers monitor customers＇ inventory levels and make supply decisions regarding order quantities and delivery time, an integrated continuous replenishment planning （CRP） strategy of fresh food oriented to retail industry is presented, which integrates the consolidation of shipment and inventory replenishment considering the deterioration of items to guarantee the quality safety of fresh food during the replenishment cycle. A vendor managed inventory （VMI） mathematical model to compute upper-level inventory and delivery cycle is built. Based on the real-time sales data exchange, it enables suppliers to make the decision of the optimal time and the quantities of replenishment for retailers during a delivery cycle, in order to replenish the consumers＇ stock initiatively and minimize the long-run average cost.

Optimization purchase price and the profit policy undervendor managed inventory

The optimization policy of the purchase price and the profit under vendor managed inventory(VMI) is studied. For a salable product, supply chain mode of VMI is established, which is based on deterministic demand, having initial stock and having stock-out cost. With the further analysis of the mode, VMI is found to increase profits of the buyer in the short-term motivation. But VMI will reduce profits of the supplier under the matching condition. And in the short-term motivation, VMI will increase the purchase price to compensate the transfer cost of the supplier. As a result, the foundation of theory is provided to implement VMI in the supply chain, and have some definituded project significance.

Seat inventory control methods for Chinese passenger railways

Railway seat inventory control strategies play a crucial role in the growth of profit and train load factor. The railway passenger seat inventory control problem in China was addressed. Chinese passenger railway operation features and seat inventory control practice were analyzed firstly. A dynamic demand forecasting method was introduced to forecast the coming demand in a ticket booking period. By clustering, passengers' historical ticket bookings were used to forecast the demand to come in a ticket booking period with least squares support vector machine. Three seat inventory control methods: non-nested booking limits, nested booking limits and bid-price control, were modeled under a single-fare class. Different seat inventory control methods were compared with the same demand based on ticket booking data of Train T15 from Beijing West to Guangzhou. The result shows that the dynamic non-nested booking limits control method performs the best, which gives railway operators evidence to adjust the remaining capacity in a ticket booking period.

Causes of Restocking Delays in Absence of Real Time Inventory Tracking of Airtel Airtime

The purpose of this research was to ascertain causes of Restocking Delays in a Distributor Company of Airtel Airtime (AA) that give justification for benefits of using Real Time Inventory Tracking (R.T.I.T) in an attempt to mitigate Restocking Delays. From a study out at the Private Marketing and Trading Services (PMTS) an Authorized Distributor of Airtel Products undertaken in 2017 evidenced by Airtime scratch card and Electronic, E-Recharge Airtime among other forms to encourage R.T.I.T among other products in Telecom Companies and other Business Enterprises. The research comprises of the following areas among which included a detailed focus on a Qualitative and Quantitative approach in obtaining different categories of Restocking Delays in form of Themes and Sub Themes encountered in the Distribution Supply Chain (SC) of AA that is contained in this paper. This research continues to capture an in-depth explanation of the Managerial and Operational causes of restocking delays in respect to AA. Similarly, fast consumer products and services other than AA require a solution to Restocking Delays through implementation of Real Time Inventory Tracking Model (R.T.I.T.M) of AA among Distributor Companies (DCs). This paper also elaborated on Literature, Methodology and Findings obtained from the study. The results were obtained from regression analysis by using the Statistical Package for Social Sciences (SPSS) that showed a higher significance of Stock Turnover Period and Airtime Denomination as a contributor to Restocking Delays whereas Messages from Airtel Head office to the Distributor had a non-significant contribution to restocking Delays as in Figure 9. The research recommends a Model for R.T.I.T in Telecom Distribution SC of AA and Omnichannel Inventory Management (OIM) as a significant contributor to timely reliable inventory restocking and promotes higher sales among DCs and retailers through minimized Restocking Delays. It shows that the forces of Demand and Supply change over time with different tastes and preferences of customers. The imbalance in AA stock levels changes at given times due to unforeseen forces of consumer demand experienced by DCs, explained by the “Bullwhip Effect” due to information distortion in the Supply Chain (SC).

Logistics Management in the Ghanaian Construction Industry

In the field of the construction industry, logistics management has become an issue of concern which needs to be focused on in order to ensure effective, successful and timely execution of projects in the construction industry. Logistics management in construction is a multidisciplinary process which makes sure things are done at the right time, cost and quality as well as material supply and handling, schedule control, site infrastructure and equipment location, site physical flow management, traffic arrangements on and around the construction site, and information management related to all physical and services flow. This paper seeks to identify and evaluate the possible factors that affect logistics management in the construction industry and also to ascertain the current practices of inventory management and the effect on logistics management envisage by construction personnel’s and stake holders. A descriptive survey research was conducted with both structured and unstructured questionnaire and administered both via e-mail and by sending questionnaire link onto a WhatsApp platform groups of the respondents, and out of the 187 respondents that took part in the survey, 22 respondents feedback were incomplete so 165 member respondents results were analyzed representing 88.2% response rate. The survey results were analyzed, displayed and interpreted in the form of tables, bar charts, column chart and pie chart with the use of SPSS software. The research survey respondents were comprised of Project Managers, Project Coordinators, Foremen, Site Managers/ Officers, Logistics Managers, Inventory Managers and Laborers. The survey results revealed that majority of the total respondents are in the capacity or position as Logistics Managers and also have from 5 to 10 years working experience, and as well majority of the respondents have also attained Master’s degree qualification. It was also revealed that logistics management is greatly affected by the inability or the failure to embrace, adopt and integrate new and emerging technology and its approach into the logistics management system. Majority of the respondents also indicated that in general the adequacy of inventory management system was dissatisfied representing 63.8% of the total respondents. The construction industry not only needed to improve its inventory management systems but also to ensure appropriate inventory management techniques for enhancing high degree of accuracy, reduced cycle times and replenishment of stock. This usually accounts for the poor delivery and quality of work, accompanied with delays and other associated losses in project construction. For effective Logistics management to be achieved in the construction industry, the introduction of modern technology to carry out logistics functions in the construction industry should be paramount to the industry players.

基于供应商管理库存模式的配送路径优化问题

基于供应商管理库存(vendor managed inventory,VMI)模式制定科学的产品配送决策,可以合理优化库存水平、有效提高物流运输效率。针对VMI模式下的配送路径优化问题开展研究,考虑客户的产品消耗率和服务时间窗等因素,以总配送成本最小为目标建立混合整数规划模型,并设计了贪婪算法。利用Gurobi求解器对小规模算例进行求解,分析了VMI模式的优越性,结果表明:基于VMI模式的配送路径规划方案可以有效减少库存水平,降低总配送成本;然后利用中等规模算例分析贪婪算法的求解效果,结果显示,对于40个点的中等规模算例,求解器在240 min内无法得到最优解,而贪婪算法可以在0.2 s之内得到近似最优解。

Implementing VMI service pricing strategy for 3PL corporation considering recovery costs

The collector managed inventory(CMI)management idea is introduced based on the comparison of manufacturer and third-party logistics(3PL)implementing vendor managed inventory(VMI)services.Considering recovery costs,the service pricing strategies for 3PL corporations implementing VMI are studied to meet two conditions of participation constraints and incentive-compatibility constraints.The numerical simulation results indicate that the supply chain partners' profits change after considering recovery costs,and the 3PL corporation's profits and the total profits increase first,and then decrease.The retailers' and manufacturers' profits also increase.The total profits of the supply chain have a characteristic of increasing first and then decreasing with the increase of the callback ratio of unsold products.The concrete extremum point is codetermined by price flexibility,service pricing of the 3PL corporation,callback price and callback ratio.

Downside Risk-Aversion Analysis for a Single-Stage Newsvendor Problem

We extend the classical newsvendor problem by introducing a downside risk constraint from the perspective of inventory control. At the beginning of a replenishment period the newsvendor will place an order, then he will review the inventory level at the end of the period. If the inventory level is positive then he will bear the holding cost and if the inventory level is negative then he will bear the backorder cost. The optimal order quantity has a simple form. We analyze the form of the optimal order quantity when we restrict that the probability that the cost level is larger than or equal to a fixed cost constant is less than a fixed value of probability. At last, we analyze the case that the fixed cost constant is equal to the expected cost.

INTEGRATION OF STORAGE CHARACTERISTIC WITH ON-LINE SERVICE CHARACTERISTIC OF AS/RS

Directed at how to integrated storage characteristic with on-line service characteristic of AS/RS, a solution based on database design and programming method is proposed.The results show that this model is reasonable and applicable while using in some small or middle enterprises material handling system.

Solving Inventory Management Problems through Deep Reinforcement Learning

Inventory management(e.g.lost sales)is a central problem in supply chain management.Lost sales inventory systems with lead times and complex cost function are notoriously hard to optimize.Deep reinforcement learning(DRL)methods can learn optimal decisions based on trails and errors from the environment due to its powerful complex function representation capability and has recently shown remarkable successes in solving challenging sequential decision-making problems.This paper studies typical lost sales and multi-echelon inventory systems.We first formulate inventory management problem as a Markov Decision Process by taking into account ordering cost,holding cost,fixed cost and lost-sales cost and then develop a solution framework DDLS based on Double deep Q-networks(DQN).In the lost-sales scenario,numerical experiments demonstrate that increasing fixed ordering cost distorts the ordering behavior,while our DQN solutions with improved state space are flexible in the face of different cost parameter settings,which traditional heuristics find challenging to handle.We then study the effectiveness of our approach in multi-echelon scenarios.Empirical results demonstrate that parameter sharing can significantly improve the performance of DRL.As a form of information sharing,parameter sharing among multi-echelon suppliers promotes the collaboration of agents and improves the decisionmaking efficiency.Our research further demonstrates the potential of DRL in solving complex inventory management problems.

Combined heuristics for determining order quantity under time-varying demands

The time-varying demands for a certain period are often assumed to be less than the basic economic order quantity （EOQ） so that total replenishment quantity rather than economic order quantity is normally considered by most of the heuristics. This acticle focuses on a combined heuristics method for determining order quantity under generalized time-varying demands. The independent policy （IP）, abnormal independent policy （AIP） and dependent policies are studied and compared. Using the concepts of normal/abnormal periods and the properties of dependent policies, a dependent policy-based heuristics （DPH） is proposed for solving the order quantity problems with a kind of time-varying demands pattern under which the first period is normal. By merging the Silver-Meal （S-M） heuristics and the dependent policy-based heuristics （DPH）, a combined heuristics （DPH/S-M） is developed for solving order quantity problems with generalized time-varying demands. The experimentation shows that （1） for the problem with one normal period, no matter which position the normal period stands, the DPH/S-M could not guarantee better than the S-M heuristics, however it is superior to the S-M heuristics in the case that the demands in the abnormal periods are in descending order, and （2） The DPH/S-M is superior to the S-M heuristics for problems with more than one normal period, and the more the number of normal periods, the greater the improvements.