Supply Chain Inventory Management Research Articles

Supply Chain Optimization in the Package Industry through Machine Learning Analysis

The package industry relies heavily on efficient supply chain management to meet customer demands and maintain profitability. However, managing complex supply chains involving multiple suppliers, transportation networks, and distribution channels poses significant challenges. This research proposes a machine learning-based approach to optimize supply chain operations in the package industry. By analysing historical data on supply chain activities, including procurement, inventory management, and distribution, our system aims to identify patterns and trends to improve decision-making processes. Machine learning algorithms such as support vector machine, naive Bayes, and logistic regression are utilized to forecast demand, optimize inventory levels, and streamline logistics operations. Experimental results demonstrate the effectiveness of the proposed approach in enhancing supply chain efficiency and reducing operational costs in the package industry.

Integrating IoT and blockchain for intelligent inventory management in supply chains: A multi-objective optimization approach for the insurance industry

This research aims to develop and optimize an intelligent supply chain framework tailored for the insurance industry by integrating an innovative inventory management policy supported by the Internet of Things (IoT) and blockchain technologies. Through an extensive literature review, key assumptions are established, needs are identified, and objectives are formulated. A multi-objective mathematical planning model is proposed to minimize cost and time within the supply chain, with optimization conducted under deterministic and fuzzy conditions. The model utilizes augmented epsilon constraint and weighted sum methods to address its multi-objectiveness. Noteworthy is the integration of advanced technologies such as Wireless Sensor Networks (WSN), Radio-Frequency Identification (RFID), blockchain, and online sales, distinguishing it from traditional approaches. Initial validation and testing in smaller dimensions demonstrate the model’s adaptability to precise methods, while larger dimensions necessitate the use of meta-heuristic algorithms for efficient problem resolution. Additionally, a comprehensive sensitivity analysis in the insurance industry on objective function coefficients reveals intricate relationships, offering valuable insights into optimization dynamics across diverse conditions.

Innovation in Cross-Border Supply Chain Inventory Management Driven by Big Data

Innovation in Cross-Border Supply Chain Inventory Management Driven by Big Data

A Deep Reinforcement Learning Approach for Optimizing Inventory Management in the Agri-Food Supply Chain

This research aims to improve inventory management throughout the agri-food supply chain, through the use of Deep Reinforcement Learning algorithms. Three Deep Reinforcement Learning algorithms, including Deep Q-Networks, Deep Deterministic Policy Gradient, and Proximal Policy Optimization algorithms were implemented and tested in order to evaluate their ability to actively manage inventories and improve the performance of supply chains. The results of the experimental phase offered important information regarding the performance of each Deep Reinforcement Learning algorithm. The Deep Deterministic Policy Gradient algorithm was identified as a viable choice, offering the best results in terms of accuracy to set the optimal inventories for the supply chain and improving the efficiency of the supply chain. The percentage of cost efficiency improved from 92.5% to 95.8% in the case of the DDPG model. The inventory turnover was also improved, surpassing the level of 8.1 units from the original level of 7.3 units which means that the system converts the inventor into sales in less time. The metric for on-time delivery also beneficiated from several improvements, reaching 96.5% form the level of 93.2% return. The quality metrics also registered a significant improvement and reported level of 96.2% after the implementation of the DDPG algorithm and compared to the level of 94.6% prior to the implementation of the algorithm. These results suggest that using such a system will bring beneficial changes to the supply chain and will offer the possibility of implementing a data-driven inventory system based on Deep Reinforcement Learning.

Performance of deep reinforcement learning algorithms in two-echelon inventory control systems

This study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies.

Contextual reinforcement learning for supply chain management

Efficient generalisation in supply chain inventory management is challenging due to a potential mismatch between the model optimised and objective reality. It is hard to know how the real world is configured and, thus, hard to train an agent optimally for it. We address this problem by combining offline training and online adaptation. Agents were trained offline using data from all possible environmental configurations, termed contexts. During an online adaptation phase, agents search for the context maximising rewards. Agents adapted online rapidly and achieved performance close to knowing the context a-priori. In particular, they acted optimally without inferring the correct context, but by finding a suitable one for reward maximisation. By enabling agents to leverage off-line training and online adaptation, we improve their efficiency and effectiveness in unknown environments. The methodology has broader potential applications and contributes to making RL algorithms useful in practical scenarios. We have released the code for this paper under https://github.com/abatsis/supply_chain_few_shot_RL.

Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach

This study systematically investigates the pivotal role of inventory management within the framework of “cloud supply chain” operations, emphasizing the efficacy of leveraging machine learning methodologies for inventory allocation with the dual objectives of cost reduction and heightened customer satisfaction. Employing a rigorous data-driven approach, the research endeavors to address inventory allocation challenges inherent in the complex dynamics of a “cloud supply chain” through the implementation of a two-stage model. Initially, machine learning is harnessed for demand forecasting, subsequently refined through the empirical distribution of forecast errors, culminating in the optimization of inventory allocation across various service levels.The empirical evaluation draws upon data derived from a reputable home appliance logistics company in China, revealing that, under conditions of ample data, the application of data-driven methods for inventory allocation surpasses the performance of traditional methods across diverse supply chain structures. Specifically, there is an improvement in accuracy by approximately 13% in an independent structure and about 16% in a dependent structure. This study transcends the constraints associated with examining a singular node, adopting an innovative research perspective that intricately explores the interplay among multiple nodes while elucidating the nuanced considerations germane to supply chain structure. Furthermore, it underscores the methodological significance of relying on extensive, large-scale data. The investigation brings to light the substantial impact of supply chain structure on safety stock allocation. In the context of a market characterized by highly uncertain demand, the strategic adaptation of the supply chain structure emerges as a proactive measure to avert potential disruptions in the supply chain.

Influence of Inventory Management Strategy on the Performance of Retail Chains in Nairobi Central Business District.

Operations strategy is the value-adding process used by the company to guarantee that customers receive value through the conversion of resources into finished goods and services. Operations management procedures include quality control, supply chain management, inventory management, risk management, and information technology. The main objective of this study was to establish the influence of inventory management strategy on the performance of retail chains in the Nairobi Central Business District. The study was anchored on the systems theory. The study adopted a descriptive research design and the target population was 34 retail chains within the Nairobi CBD area. The sample population involved 80 managers who were in charge of the retail chains in Nairobi CBD. Self-administered structured questionnaires were used to collect primary data. Data was analyzed using descriptive and inferential statistics. The study found that inventory management strategy has a positive significant influence on the performance of retail chains in the Nairobi Central Business District. This is because an increase in a unit of inventory management strategy leads to an improvement of the performance of the retail chains. The study recommended that retail chains should implement inventory management practices that aim to reduce deadstock and minimize inventory holding costs. The study also recommended that organizations should utilize inventory forecasting techniques to accurately predict demand and adjust inventory levels accordingly. The study further recommended that retail chains should explore vendor-managed inventory (VMI) arrangements with key suppliers to maintain optimal stock levels. The study recommended that retail chains should regularly assess and update inventory turnover strategies to meet customer demand effectively.

Supply chain disruptions during COVID-19 pandemic: Key lessons from the pharmaceutical industry

Purpose: This study extends previous research by exploring the key lessons learned by the pharmaceutical industry in South Africa from the COVID-19 pandemic supply chain disruptions.Design/methodology/approach: Having embraced the interpretivist paradigm, 25 supply chain professionals were purposively selected on the basis of their experience and deep knowledge of the industry and interviewed using the semi-structured approach of interviews. A qualitative data analysis programme, ATLAS.ti (version 9.1), was used to analyse the transcripts.Findings/results: Following thematic analysis, the following themes emerged from the primary data: supply networks, demand planning and management, supply chain coordination, inventory management, logistics management and local production.Practical implications: It is crucial to build resilience in the design of pharmaceutical supply chains. A comprehensive demand management strategy and use of data analytics need to be adopted by pharmaceutical companies to improve demand visibility, responsiveness and supply chain resilience. The study also revealed the need for local production, as local skills and enterprises are being developed.Originality/value: This study has extended the body of knowledge on the supply chain disruptions, particularly in the pharmaceutical industry by building on the studies that were conducted during COVID-19, many of which were not based on empirical data. It also points supply chain professionals in a direction which enables them to develop a disruption strategy while providing justification for policymakers to support investment in the development of the requite skills for the pharmaceutical industry and the development of local production facilities.

IoT and its Potential for Transforming Industries

The Internet of Things (IoT) includes connected devices that communicate over the Internet. This technology has the potential to change industries by increasing productivity, reducing costs and improving efficiency. In manufacturing, IoT devices improve machine maintenance, supply chain management and inventory management. Healthcare uses IoT for drug tracking and patient tracking. Transportation can benefit from improved visibility and streamlining of operations. In the energy sector, IoT optimizes use and reduces waste. New IoT applications can be used in a variety of industries to increase productivity, efficiency and effectiveness.

The Mediating Effect of Customer Loyalty in the Relationship Between Supply Chain Performance, Inventory Management and Quality Management

Supply chain management is the systematic, strategic coordination of business tasks engaged in a supply chain to enhance a company’s and a supplier chain’s overall long-term performance. It is also a set of approaches used effectively to integrate suppliers, manufacturers, distributors, or customers to improve the long-term performance of the companies involved and the supply chain itself as a whole. A lack of financial resources is the main obstacle to supply chain management development. This study intends to investigate the mediating effect of customer loyalty on the relationships among supply chain performance, inventory management, and quality management. An electronic manufacturing facility in Penang, Malaysia, served as the site of this investigation. Consequently, 700 individuals were working at this industrial company. This industrial company’s employees were given the questionnaire, which was created. An analysis of the relationship and effects of quality management, inventory management, supply chain management, and customer loyalty on the performance of the product market was performed via nonprobability sampling on a population of 700 employees to generate a sample of 65 respondents based on the PLS-SEM. Several statistical methods were employed in this study, and SMART PLS3 was utilised to analyse the data. The effectiveness of a product in the market was found to be significantly influenced by variables such as customer loyalty, inventory management, supply chain management, and product market performance. The results showed that seven hypotheses had significant connections for all variables. Business owners and other relevant stakeholders are suggested to improve the supply chain, inventory management, and quality management systems in their organisation’s business operations to promote customer loyalty and product market performance for long-term viability. The effort continued during the RMK-9 period by implementing comprehensive policy because more intense competition had to be faced by the industry, including SMEs, due to the existence of globalisation and trade liberalisation

Exploring the key enabling role of digital technology for enhancing supply chain performance through supply chain collaboration, inventory management and supply chain resilience

Exploring the key enabling role of digital technology for enhancing supply chain performance through supply chain collaboration, inventory management and supply chain resilience

Research on Supply Chain Inventory Management Strategies of Manufacturing Enterprises in the Context of Digitization

Research on Supply Chain Inventory Management Strategies of Manufacturing Enterprises in the Context of Digitization

Constrained continuous-action reinforcement learning for supply chain inventory management

Reinforcement learning (RL) is a promising solution for difficult decision-making problems, such as inventory management in chemical supply chains. However, enabling RL to explicitly consider known environment constraints is crucial for safe deployment in practical applications. This work incorporates recent tools for optimization over trained neural networks to introduce two algorithms for safe training and deployment of RL, with a focus on supply chains. Specifically, we use optimization over trained neural-network state–action value functions (i.e., a critic function) to directly incorporate constraints when computing actions in a continuous action space. Furthermore, we introduce a second algorithm that guarantees constraint satisfaction during deployment by directly implementing actions from constrained optimization of a trained value function. The algorithms are compared against state-of-the-art algorithms TRPO, CPO, and RCPO using a computational supply chain case study.

Combining deep reinforcement learning and multi-stage stochastic programming to address the supply chain inventory management problem

We introduce a novel heuristic designed to address the supply chain inventory management problem in the context of a two-echelon divergent supply chain. The proposed heuristic advances the current state-of-the-art by combining deep reinforcement learning with multi-stage stochastic programming. In particular, deep reinforcement learning is employed to determine the number of batches to produce, while multi-stage stochastic programming is applied to make shipping decisions. To support further research, we release a publicly available software environment that simulates a wide range of two-echelon divergent supply chain settings, allowing the manipulation of various parameter values, including those associated with seasonal demands. We then present a comprehensive set of numerical experiments considering constraints on production and warehouse capacities under fixed and variable logistic costs. The results demonstrate that the proposed heuristic significantly and consistently outperforms pure deep reinforcement learning algorithms in minimizing total costs. Moreover, it overcomes several inherent limitations of multi-stage stochastic programming models, thus underscoring its potential advantages in addressing complex supply chain scenarios.

Complex effects of cooperative behavior on authorized remanufacturing supply chain decisions under subsidies.

In this study, the dynamic effects of new product supply chain cooperation behavior on optimal government subsidies and supply chain decision-making are studied by establishing a nonlinear discrete inventory decision system; In this system, the government subsidizes authorized remanufacturers to promote remanufacturing, and cooperative behavior exists in the supply chain of new products. The research method is modeling and simulation of a supply chain system based on nonlinear system dynamics theory. The complexity analysis includes the stability analysis of the decision system, the path of the system into chaos, the change of entropy of the system and the performance in chaos system. Our findings indicate that the optimal government subsidy in the cooperative model is lower than that in the non-cooperative model. Consumer surplus is the main reason for the subsidy difference between the two models. In comparison with the cooperative supply chain, the stability of the non-cooperative supply chain is more easily affected by government subsidies. Further, the market is more likely to enter chaos due to improper adjustment of the new products' inventory with cooperative behavior in the supply chain of new products. When the system enters chaos, the new product supply chain's profit in the cooperative system is more likely to be far lower than the equilibrium profit. This study provides a theoretical reference for supply chain inventory management and government subsidy remanufacturer decision-making from the perspective of dynamic systems science.

An Integrated Optimization Model of Green Supply Chain Network Design with Inventory Management

Supply chain network design and inventory management are both significant for improving the core competitiveness of enterprises. This study investigates the joint optimization problem of facility locations and inventory for assembly manufacturing enterprises’ multi-echelon supply chain networks, considering the locations of facilities, the selection of suppliers, transport mode choices, and inventory decisions simultaneously. A corresponding integrated optimization model is proposed, which aims to minimize the total cost, consisting of the fixed open cost of facilities, the inventory cost of the open plants and distribution centers, and the transportation cost of vehicles in the entire supply chain network as well as the cost of CO2 emissions. Based on the characteristics of the proposed optimization model, a hybrid genetic algorithm embedded with a local search is developed to solve the proposed model. Numerical examples and a case study are provided to illustrate the effectiveness of the proposed model and the corresponding algorithm. The findings show that the model is reasonable and applicable, and hybrid genetic algorithm (HGA) is more efficient than the standard genetic algorithm (SGA). In addition, plants’ maximum lead-time has a significant impact on the total cost of the supply chain.

Inventory model involving reworking of faulty products with three carbon policies under neutrosophic environment

The increasing in industry as well as usage of vehicles for transporting products generate many problems to the environment such as greenhouse gas emission and global warming. As nowadays environmental pollution has become a crucial problem, production companies have now shifted their attention to lower the carbon emission level along with their financial goal. In reality, the values of some parameters such as production cost, holding cost, demand rate, deterioration rate may not fixed due to several factors. Reworking of defective products reduces production cost. This study aims at developing a multi-objective supply chain inventory management by considering deteriorating products together with imperfect quality production under neutrosophic environment. Several carbon reduction policies such as carbon tax, carbon cap-and-trade and carbon cap-and-offset and green technology are implemented here to mitigate the influence of carbon emission by curbing amount of carbon emission. To reduce the deterioration rate, preservation technology is utilized. In real-world situations, all parameters related to an inventory management are not handled as a crisp value, a fuzzy number or an intuitionistic fuzzy number. To confrontation this issue, single valued trapezoidal neutrosophic number is chosen for investigating this type of an inventory problem. A ranking approach is designed to transform all uncertain parameters into deterministic values. A comparison between two types of reworking processes, and a comparison among crisp, intuitionistic fuzzy and neutrosophic models are drawn. A sensitivity analysis regarding important parameters is also taken into consideration to accentual managerial intuitions.

A Supply Chain Inventory Management Method for Civil Aircraft Manufacturing Based on Multi-Agent Reinforcement Learning

Effective supply chain inventory management is crucial for large-scale manufacturing industries such as civil aircraft and automobile manufacturing to ensure efficient manufacturing. Generally, the main manufacturer makes the annual inventory management plan, and contacts with suppliers when some material is approaching critical inventory level according to the actual production schedule, which increases the difficulty of inventory management. In recent years, many researchers have focused on using reinforcement learning method to study inventory management problems. Current approaches were mainly designed for the supply chain with single-node multi-material or multi-node single-material mode, which are not suitable to the civil aircraft manufacturing supply chain with multi-node multi-material mode. To deal with this problem, we formulated the problem as a partially observable Markov decision process (POMDP) model and proposed a multi-agent reinforcement learning method for supply chain inventory management, in which the dual-policy and information transmission mechanism was designed to help the supply chain participant improve the global information utilization efficiency of the supply chain and the coordination efficiency with other participants. The experiment results show that our method has about 45% performance improvement on efficiency compared with current reinforcement learning-based methods.

Estimating Demand for Passenger Cars : A Model for the Indian Market

Purpose : This study analyzed trends in Indian passenger car sales for the decade 2010–2011 to 2019–2020. These trends were decoded via demand functions estimated using multiple linear regression. This has crucial theoretical and managerial implications. Methodology : Multiple linear regressions were carried out using SPSS for different segments of the passenger car industry, namely, mini, compact, super compact, mid-size, executive, and premium. While sales of passenger vehicles were the dependent variable, personal disposable income, product price, fuel price, and interest rates were explanatory variables. Secondary data from the Society of Indian Automobile Manufacturers (SIAM), State Bank of India (SBI), Indian Oil Corporation Limited (IOCL), and the Ministry of Statistics and Programme Implementation (MoSPI) were sourced. Findings : Demand for most types of passenger cars in India has slowed down, except for compact cars. The regressions show that the mini segment is affected by vehicle price and interest rates, and a lower Goods and Services Tax (GST) for small cars could boost sales. In the compact segment, the price remains a significant factor. Interest rates matter most for the super compact segment, while the preference for better category cars or utility vehicles increases with rising income for mid-size and executive segments. Practical Implications : A demand-side analysis can benefit companies and inform policy decisions to overcome the recent slowdown. The regression model can advance theory and academic research and aid in supply chain management, demand forecasting, and inventory management studies. Originality : Unlike prior research on the Indian automobile sector, this study bifurcates the heterogeneous market into segments and conducts econometric analyses.