Excess Inventory Research Articles

Enhancing Supply Chain Resilience Through Artificial Intelligence: Developing a Comprehensive Conceptual Framework for AI Implementation and Supply Chain Optimization

Background: Amid growing global uncertainty and increasingly complex disruptions, the ability of supply chains to rapidly adapt and recover is critical. The incorporation of artificial intelligence (AI) into supply chain management represents a transformative strategy for enhancing resilience. By harnessing advanced AI technologies, such as machine learning, predictive analytics, and real-time data processing, organizations can more effectively anticipate, respond to, and recover from disruptions.AI improves demand forecasting accuracy, optimizes inventory management, and increases real-time visibility across the supply chain, reducing the risks of stockouts and surplus inventory. Furthermore, I-driven automation and robotics enhance operational efficiency by minimizing human error and streamlining processes. Methodology/Approach: This paper proposes a conceptual framework for strengthening supply chain resilience through AI integration. The framework leverages AI technologies to improve key aspects of supply chain resilience, including risk management, operational efficiency, and real-time visibility. Result/Conclusions: Additionally, it underscores the importance of collaborative relationships with supply chain partners, enabled by AI-powered data-sharing and communication tools that foster trust and coordination within the network. Originality/Value: This comprehensive framework offers a strategic approach to integrating AI into supply chain management, highlighting its potential to significantly enhance resilience, operational efficiency, and sustainability, thereby empowering organizations to navigate the complexities of modern supply chains more effectively.

Identifikasi Pemborosan dalam Proses Produksi Pakan Ternak Ayam di PT.XYZ dengan Menggunakan Metode Value Stream Analysis Tools (VALSAT)

This research aims to identify various types of waste in the chicken feed production process at PT. XYZ and provide improvement recommendations to reduce production costs. The method used is the Value Stream Analysis Tools (VALSAT) to map and analyze the value stream in the production process. This research includes direct observation and data analysis to find the main wastes. The results show seven main types of waste in the chicken feed production process: waiting time, unnecessary movements, product defects, excess inventory, inefficient processes, overproduction, and transportation. However, the research encountered several challenges, such as data limitations and difficulties in collecting accurate data. The largest waste found was excess inventory, with an average score of 9.13. This research provides clear insights into areas in the production process that require improvement. The implementation of the improvement recommendations is expected to increase the company's efficiency and productivity. Despite facing some challenges, the use of VALSAT proved effective in identifying and analyzing waste. It is hoped that the provided improvement recommendations can reduce waste and enhance operational efficiency at PT. XYZ.

Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study

Forecasting sales trends is a valuable activity for companies of all types and sizes, as it enables more efficient decision making to avoid unnecessary expenses from excess inventory or, conversely, losses due to insufficient inventory to meet demand. In this paper, we designed a personalized cost function to reduce economic losses caused by the excessive acquisition of products or derived from their scarcity when needed. Moreover, we designed an LSTM network integrated with Glorot and Orthogonal initializers and dropout to forecast sales trends in a lumber mill in Tamaulipas, Mexico. To generalize and appropriately forecast the sales of the lumber mill products, we optimized the LSTM network’s hyperparameters through a genetic algorithm, which was essential to explore the solution space. We evaluated our proposal in instances obtained from the historical sales of the five main products sold by the lumber mill. According to the results, we concluded that for our case study the proposed function cost and the hyperparameters optimization allowed the LSTM to forecast the direction and trend of the lumber mill’s product sales despite the variability of the products.

Study on Optimizing Inventory to Minimize Waste in the Fresh Produce Supply Chain

Abstract: The increasing demand for fresh produce, particularly greens, presents unique challenges in inventory management due to their short shelf life and susceptibility to spoilage. Inefficient management often results in significant waste, leading to financial losses for retailers and increased environmental burdens from the disposal of spoiled goods. Despite various strategies being employed in the agri-food sector, gaps remain in understanding how to optimize inventory levels to minimize waste at the retails level while maintaining high service levels for customers. This research aims to develop an inventory management model tailored to fresh produce that can mitigate these issues by balancing stock availability and minimizing wastage, particularly in large retail shops. To address the research problem, a mixed-integer linear programming (MILP) model was developed, focusing on three service levels—90%, 92%, and 95%—to evaluate the trade-offs between customer satisfaction and total inventory costs. The model incorporates key constraints, including stock balance, shelf-life limitations, and demand satisfaction. The model was validated using actual data, including stock, sales, and wastage records for the month of August, 2024. The objective function aimed to minimize total costs, encompassing holding costs, wastage, and lost revenue. The findings indicate that higher service levels, while reducing stockouts, significantly increase costs due to excess inventory and waste. The most cost-effective solution was observed at the 90% service level, which balanced waste reduction and customer satisfaction. The study also emphasizes the importance of integrating total cost reduction and minimize wastage. Future work should focus on incorporating consumer behavior patterns and extending the model to multi-echelon inventory systems, which include both distribution centers and retail outlets, to capture a more holistic view of the supply chain.

The Impact of Lead Time Variability on Supply Chain Management

Purpose: The impact of lead time variability on supply chain management (SCM) is a critical factor affecting operational efficiency, cost management, and service delivery. This study examines how variations in lead time affect key aspects of supply chain performance, including inventory management, production scheduling, and order fulfillment. Methodology: A mixed-methods approach is adopted, combining quantitative analysis through simulation modeling and qualitative insights derived from case studies of companies in the manufacturing and retail sectors. The simulation model accounts for different levels of lead time variability and its influence on stockouts, excess inventory, and overall supply chain responsiveness. Case studies are used to illustrate real-world challenges and strategies employed by businesses to mitigate the effects of lead time fluctuations. Findings: Findings indicate that higher lead time variability leads to increased inventory costs, stockouts, and delays in product deliveries. Companies that rely heavily on just-in-time (JIT) systems are particularly vulnerable to these fluctuations, whereas firms with flexible inventory strategies or buffer stock perform better in managing variability. Additionally, the study reveals that advanced demand forecasting and more resilient supplier relationships can significantly reduce the negative impact of lead time uncertainty. The analysis also highlights the importance of integrating lead time variability into the broader risk management framework of supply chains. Unique Contribution to Theory, Practice and Policy: Based on these findings, recommendations include implementing more robust demand forecasting systems, optimizing safety stock levels, diversifying suppliers, and using advanced inventory management technologies. Moreover, it is advised that businesses foster stronger communication and collaboration with suppliers to reduce variability and improve lead time predictability. Future research should explore the potential of real-time data and AI-driven supply chain management systems to further mitigate lead time variability's impact on SCM.

Financial Analysis of Apple's Product Pricing Strategy

Abstract: Due to the rise of other domestic mobile phone brands, iPhone updates have slowed down and become less attractive to consumers, resulting in excess iPhone inventory. In response to this situation, in early 2024, Apple began to implement a price reduction strategy, and the price of Apple's iPhone dropped significantly. Given this phenomenon, this paper studies the financial status of Apple in the past three years, including profitability, operating ability and debt-paying ability, and puts forward the problems of Apple's declining cash flow ability in recent years, weakening short-term debt-paying ability, and unsalable inventory products. However, gross profit margin increased slightly and net profit margin on sales was also little changed. Finally, the paper analyzes the reasons for the sharp price reduction of the iPhone and puts forward some suggestions for Apple's future development, such as strengthening product innovation, promoting old products, and reducing the backlog of old products.

SISTEMA TOYOTA DE PRODUÇÃO

Although the Toyota System has many issues, it has still managed to establish itself by using a strategy to shorten production time and, more importantly, to be precise in terms of quantity while formulating a policy to prioritize waste reduction. In the Toyota Production System, its development is supported by two essential pillars in communication. Both systems are well-known and are called Just in Time and Autonomation, also known as Jidoka. The Toyota Production System developed a method for detecting seven types of waste that large industries face in the production system, which are Waiting, Defect, Transportation, Motion, Excess Inventory, Overproduction, and Over/Under Processing.

Lean process mining: adopting process mining in lean manufacturing for dynamic process mapping and avoiding waste occurrence in real time

Purpose This paper aims to recommend a method entitled “lean process mining (LPM)” for mapping, analyzing and improving the material/information flows in the value stream of manufacturing processes. Design/methodology/approach The method is developed based on literature review and in-depth explorative research in value stream mapping and process mining approaches. The proposed LPM framework consists of three phases including as-realized process state, improvement strategies and reengineered process state. Hence, firstly, extracts the as-realized model, measures the identified wastes and identifies the sources of wastes. Secondly, implements prediction-recommendation-prevention strategies. Thirdly, reengineers the process model and measures the improved wastes. Findings It presents the applicability of the proposed approach in (1) online observation of manufacturing process behavior and tracing the process deviations dynamically in real time to identify the sources of waste; (2) avoiding defective products occurring during the production and eliminating the relevant derived wastes including wasted material, wasted energy, waste of labor, excess inventory, increased production lead time and wasted operational costs. Originality/value The practical application of LPM is illustrated through implementing it in a real-life manufacturing case. The outcomes prove the remarkable applicability of this method in lean manufacturing to avoid waste occurrence in the value stream.

Managing Resources for Shared Micromobility: Approximate Optimality in Large-Scale Systems

We consider the problem of managing resources in shared micromobility systems (bike sharing and scooter sharing). An important task in managing such systems is periodic repositioning/recharging/sourcing of units to avoid stockouts or excess inventory at nodes with unbalanced flows. We consider a discrete-time model; each period begins with an initial inventory at each node in the network, and then, customers (demand) materialize at the nodes. Each customer picks up a unit at the origin node and drops it off at a randomly sampled destination node with an origin-specific probability distribution. We model the above network inventory management problem as an infinite horizon discrete-time discounted Markov decision process (MDP) and prove the asymptotic optimality of a novel mean-field approximation to the original MDP as the number of stations becomes large. To compute an approximately optimal policy for the mean-field dynamics, we provide an algorithm with a running time that is logarithmic in the desired optimality gap. Lastly, we compare the performance of our mean field-based policy with state-of-the-art heuristics via numerical experiments, including experiments using Austin scooter-sharing data. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02023 .

COMPARING OF ARTIFICIAL NEURAL NETWORK AND MULTIPLICATIVE HOLT WINTERS EXPONENTIAL SMOOTHING METHODS IN FORECASTING DEMAND

General Background: Fluctuations in raw material orders pose significant challenges for business operators, especially during peak seasons like holidays and the new year, often resulting in shortages or excess inventory. Specific Background: This study focuses on forecasting demand for wallet products from UMKM Pengerajin Dompet Khas Tanggulangin (PDKT) by comparing two forecasting methods: Artificial Neural Networks (ANN) and the Multiplicative Holt-Winters method, which is tailored for seasonal data. Knowledge Gap: While existing literature recognizes the effectiveness of various forecasting techniques, there is limited comparative analysis of ANN and Holt-Winters specifically in the context of UMKM wallet production, highlighting the need for empirical validation. Aims: This research aims to identify the most accurate forecasting method to optimize raw material usage and production planning. Results: The findings indicate that the ANN method yields a superior Root Mean Square Error (RMSE) of 14.249, compared to 93.436 for the Holt-Winters method, establishing its higher predictive accuracy. Novelty: The study contributes to the field by providing a comparative analysis of forecasting methods tailored to the specific context of UMKM, demonstrating the efficacy of ANN over traditional methods. Implications: These results suggest that adopting ANN for demand forecasting can significantly enhance inventory management and production efficiency for PDKT MSMEs, ultimately leading to better resource allocation and reduced operational costs.

Applying Neural Networks to Predict Ventilator Demand: A Study of Pakistan's Healthcare Sector

The distribution companies that deal with ventilators in Pakistan face challenges related to inventory control because of inadequate product shelf life, shortages, excess inventory, and unnecessary stock. This study, which focuses on Pakistani ventilator distribution companies, aims to offer a novel approach to sales estimation, avoid unnecessary stock expenditures, and stop clientele loss brought on by ventilator shortages. The results of this study will help determine key elements and standards that Pakistani distributors of ventilators might employ to boost sales. Most ventilator distribution businesses in Pakistan are independent wholesalers that purchase stock from their stores and distribute it to customers. To maximize ventilator distribution firms' sales for various products, this study examined distribution and sales data from 2019 to 2024 for many locations and dates. To create an accurate sales forecasting model for a ventilator distribution company, this research also aims to apply artificial neural networks (ANN) for effective sales prediction. An Artificial Neural Network (ANN) model was trained using a dataset from ventilator distribution businesses and the proposed model produced an accuracy of 90%, which is good for early prediction.

Mitigating data inaccuracy and supply chain challenges in Western Romania's automotive industry

The purpose of this paper was to emphasize the importance of data accuracy within internal logistics systems and their extended influence on supply chains in automotive industry through 6-month multiple case studies conducted on 3 first tier original equipment manufacturers (OEM) based in Western Romania. The study investigates the most common causes of data inaccuracies among automotive suppliers and their approaches to reduce consequential supply chain issues and be more agile. Data collection and analysis revealed that main issues arise due to ordering quantities mismatching actual customer demand, a wide range of order lot sizes, lead times and delivery reliability concerns and the reluctance to shift away from mainstream cost-effectiveness and towards strategic added value thinking. These issues sourced significant other related operational challenges such as excessive inventory, short-term stockouts and subsequent express shipping services or product-related inconveniences (quality and capacity levels, contracted volumes and dedicated lines). The paper sources different logistics and supply chain strategies used by the 3 OEMs, their features and operational performance, as well as their overall effectiveness, which can be applied by other automotive industry suppliers to improve own results. Introducing more reliable real-time data collection tools and performance metrics has started hauling more focus towards solving these prevalent issues with some ongoing improvement projects showing up to 25% better results. For one of the 3 OEMs introducing a new warehouse management system has already sourced an overall quality increase (5 percentage points) due to a 60% higher utilization of its production equipment.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

The benefits (or detriments) of adapting to demand disruptions in a hospital pharmacy with supply chain disruptions.

Supply chain disruptions and demand disruptions make it challenging for hospital pharmacy managers to determine how much inventory to have on-hand. Having insufficient inventory leads to drug shortages, while having excess inventory leads to drug waste. To mitigate drug shortages and waste, hospital pharmacy managers can implement inventory policies that account for supply chain disruptions and adapt these inventory policies over time to respond to demand disruptions. Demand disruptions were prevalent during the Covid-19 pandemic. However, it remains unclear how a drug's shortage-waste weighting (i.e., concern for shortages versus concern for waste) as well as the duration of and time between supply chain disruptions influence the benefits (or detriments) of adapting to demand disruptions. We develop an adaptive inventory system (i.e., inventory policies change over time) and conduct an extensive numerical analysis using real-world demand data from the University of Michigan's Central Pharmacy to address this research question. For a fixed mean duration of and mean time between supply chain disruptions, we find a drug's shortage-waste weighting dictates the magnitude of the benefits (or detriments) of adaptive inventory policies. We create a ranking procedure that provides a way of discerning which drugs are of most concern and illustrates which policies to update given that a limited number of inventory policies can be updated. When applying our framework to over 300 drugs, we find a decision-maker needs to update a very small proportion of drugs (e.g., ) at any point in time to get the greatest benefits of adaptive inventory policies.

State of the International Wine Markets in 2023. The wine market at a crossroads: Temporary or structural challenges?

The global wine trade experienced a decline in volume and value in 2023 due to reduced real incomes, post-pandemic overstocking, and shifting consumer preferences. Non-sparkling red wines were more affected than sparkling wines, white wines and wines in bag-in-box formats, with notable declines in non-sparkling bottled wine imports across major markets. Despite this, an exceptionally low wine harvest in 2023 prevented a global oversupply. Regionally, some countries and appellations face an imbalance where grape supply exceeds demand due to misalignment with current consumption trends. The critical question for the wine sector is whether these declines are due to temporary factors, structural changes, or a combination of both. Excessive inventories accumulated in the post-pandemic period suggest a temporary decline in trade. However, there are growing concerns about changing consumption habits, particularly among younger consumers in Europe and the US. The relative influence of these factors remains uncertain. Premium wines still enjoy demand among traditional aficionados, but there is a clear shift in consumer preferences towards more refreshing products, including white wines, sparkling wines, and low-alcohol wines. To address these challenges, the wine industry must innovate and adapt its regulatory framework to support consumer-oriented innovation and diversified portfolios. The global wine industry is at a pivotal transition point, requiring a strategic response to evolving consumer preferences to maintain its market position.

Inventory Planning of Raw Materials for Furniture Products (Case Study: CV XYZ Furniture)

The burgeoning industrial sector in Indonesia has intensified competition among businesses vying for market share. The furniture industry is a prime example of this dynamic landscape. To expand their market reach, companies must demonstrate their ability to fulfill every consumer demand. Failure to meet these expectations will prompt consumers to seek alternatives. One crucial factor in meeting consumer demands is product availability, which is directly influenced by the availability of raw materials within the company. CV XYZ Furniture, a furniture company based in Padang City, exemplifies this challenge. The company produces a diverse range of furniture, primarily utilizing rattan as its raw material. However, CV XYZ Furniture currently lacks a structured inventory planning method for raw materials, resulting in excess inventory for each period. To address this issue, this research aims to establish an optimized inventory planning strategy for raw materials used in furniture production at CV XYZ Furniture. The continuous review system method serves as the foundation for this endeavor. The research outcomes encompass the determination of optimal demand quantity, reorder point, and safety stock. The implementation of the continuous review system model yielded a significant reduction in total inventory costs, amounting to Rp8,625,594 or 2.03%. It can be proven that the continuous review system model is suitable for calculating raw material inventory at CV XYZ Furniture for raw material planning in the following years.

Lean Management and Firms Competitive Advantage

This study focused on lean management and firms' competitive advantage in Akwa Ibom State. Two hypotheses and two research questions were developed for the study, using a survey methodology. The population consisted of 59 employees in top management, middle management, and lower management positions at particular manufacturing companies in the state of Akwa Ibom. Utilizing census techniques, data on the total population was gathered. The two hypotheses were tested at the 0.05 level of significance using the Ordinal Regression Tool Analysis. The findings indicated that the Lean Management dimension (employee involvement: 8.298, PV = 0.003; Just-in-Time: 7.246, PV = 0.002) and firms' competitive advantage in Akwa Ibom State were significantly correlated. Just-in-time and employee involvement are relational characteristics that might improve enterprises' competitive advantage and organizational performance in Akwa Ibom State. The study’s management was advised to improve their policies to support manufacturing and operations by figuring out what the customer values, mapping out the value stream the complete sequence of steps necessary to deliver a good or service—and analyzing the value stream to find and get rid of wasteful practices like overproduction, waiting times, needless motion, defects, and excess inventory. To boost overall cost efficiency, it was also advised that manufacturing companies integrate waste elimination into their operational processes.

The Utilization of Time Series Data Forecasting Techniques on Hospitals Pharmaceutical Inventory

Good inventory management is essential in the hospital industry to overcome inventory problems. Ineffective inventory prediction methods can lead to shortages or excess stock inventory. Ultimately, this can impact the budget and availability of pharmaceutical items in the hospital. Previous traditional prediction methods often show inaccuracies. This research utilizes ARIMA (Autoregressive Integrated Moving Average) and FB Prophet methods to predict the demand for pharmaceutical items in hospitals. In an attempt to evaluate the effectiveness of both methods, an experiment was conducted on five pharmaceutical items. The results showed that the ARIMA method produced better performance compared to the FB Prophet method, with the smallest error of 0.07310.

Enhancing Enterprise Efficiency through Cross-Domain Supply Chain Linkage Management

In recent years, with the complexity and variability of international finance, debt, and geopolitical risks, the growth rate of domestic economic operations has slowed down. More enterprise groups are facing the transformation and upgrading from domestic leadership to internationalization, achieving revenue growth of nearly 10 billion yuan per year against the trend. The severe and complex internal and external environment has brought many challenges to the supply chain for enterprises. This article focuses on the goal of an “enterprise supply chain system focusing on value creation, strengthening system linkage, and improving overall chain efficiency,” aiming to find solutions to problems such as strong demand variability, frequent emergency needs, coexistence of project shortages and inventory surplus, long material turnover cycles, and high organizational complexity. By searching for key activities in the entire business chain of demand, design, production, service, and finance, implementing source planning, process control, system linkage, analysis and summary, and team collaboration, we have achieved more scientific demand forecasting, more accurate planning, more effective procurement cost reduction, more stable production performance supply, more convenient inventory sharing services, and more efficient integration of business and financial policies.

Navigating Inventory Management Challenges in Contemporary Fashion Retailing: A Circular Economy Approach

Our research focuses on decreasing the pollution caused by excess inventory in the fashion retailing industry. We choose to focus on this problem due to the lack of solutions in this region. In the past, people just focused on how to use environmentally friendly materials and how to produce with the lowest amount of pollution. To find a new method to solve the pollution problem during and after the consumers purchasing, we built a model containing the primary fulfillment, retailers, secondary fulfillment, secondary retailers, brands and factories, etc. We use them as examples to find out how to end the combustion of excess inventory. We try to change the linear (in the end, the clothes are useless and combusted) to a circle(we could use one raw material several times) to avoid lots of pollution. The final answer is that we did that by increasing the service age of the products and information transparency inside the fashion retailing industry.