Traditional Inventory Models Research Articles

Developing a fuzzy logic-based carbon emission cost-incorporated inventory model with memory effects

Inventory control is a widely discussed topic in the real world, and recently, it has become closely linked to concerns about carbon emissions and global warming. Global warming is a pressing issue, mainly due to a lack of awareness and action. Traditional inventory models, which typically use integer-order differential equations, overlook the memory aspect of the system. Addressing inventory management is essential in our efforts to combat global warming. This paper introduces a novel approach by incorporating carbon emission costs within a fuzzy environment. Fractional Calculus, a powerful mathematical tool, is employed to capture the memory effect of the system. This approach distinguishes between long memory, characterized by a strong memory effect, and short memory, associated with a poor memory effect, through the use of fractional derivatives and integrals. Numerical results are analyzed based on these memory concepts. Entrepreneurs often find it difficult to determine exact values for known parameters in the real world. Therefore, this study considers the uncertain nature of ordering costs, the rate of deterioration, and demand rates as triangular fuzzy numbers. The optimal average cost and ordering intervals are determined using a solution methodology. A sensitivity analysis is performed to demonstrate how different system parameters influence the outcomes within a fuzzy environment. Notably, it is observed that profit tends to be higher under conditions of strong memory compared to poor memory effects. Moreover, it's worth emphasizing that profits are notably more favorable when employing the signed distance method compared to the graded mean integration method, especially in situations marked by strong memory effects. This study highlights the importance of considering sensitive parameters in the model, especially under conditions of strong memory effects. Such parameters require careful attention in the pursuit of effective inventory management strategies to mitigate carbon emissions and combat global warming.

Optimizing the Economic Order Quantity Using Fuzzy Theory and Machine Learning Applied to a Pharmaceutical Framework

In this article, we present a novel methodology for inventory management in the pharmaceutical industry, considering the nature of its supply chain. Traditional inventory models often fail to capture the particularities of the pharmaceutical sector, characterized by limited storage space, product degradation, and trade credits. To address these particularities, using fuzzy logic, we propose models that are adaptable to real-world scenarios. The proposed models are designed to reduce total costs for both vendors and clients, a gap not explored in the existing literature. Our methodology employs pentagonal fuzzy number (PFN) arithmetic and Kuhn–Tucker optimization. Additionally, the integration of the naive Bayes (NB) classifier and the use of the Weka artificial intelligence suite increase the effectiveness of our model in complex decision-making environments. A key finding is the high classification accuracy of the model, with the NB classifier correctly categorizing approximately 95.9% of the scenarios, indicating an operational efficiency. This finding is complemented by the model capability to determine the optimal production quantity, considering cost factors related to manufacturing and transportation, which is essential in minimizing overall inventory costs. Our methodology, based on machine learning and fuzzy logic, enhances the inventory management in dynamic sectors like the pharmaceutical industry. While our focus is on a single-product scenario between suppliers and buyers, future research hopes to extend this focus to wider contexts, as epidemic conditions and other applications.

Optimization of Neutrosophic EOQ Model for Effective Demand Management in Uncertain Environment Using Genetic Optimization

Inventory management is characterized by a continuous struggle to lower goods levels and related costs while also providing customers with the goods they need. However, reducing costs while simultaneously striving for ideal inventory levels is difficult, notably in the current situation of high unpredictability of goods demand and lead time. Traditional inventory models are not strong enough to endure changes like goods demand and lead-time demand. As a result, it must be adjusted to achieve results. The oeuvre below presents a new kind of inventory model that deals with uncertainty in the demand for goods and lead time. In this regard, the presented work, the novel Neutrosophic Economic Order Quantity approach is a mechanism to account for the likely imprecision in the model. Specifically, the Neutrosophic set theory is integrated into the EOQ model so that it can handle variations in the demand and lead-time pattern successfully. An objective function is established for obtaining economical order quantities that include demand, lead-time, and other necessary components’ irregularities. The process variables in the model are given the final values using genetic algorithms and simulated annealing. To highlight the impact of the proposed Neutrosophic approach, it is then applied to several realistic examples. This will provide the audience a sense of how effective inventory management may be in high-uncertainty situations. The rapid evolution of organizations necessitates innovative inventory control tactics to meet growing demands

Optimization of price, lot size and backordered level in an EPQ inventory model with rework process

In traditional inventory models, the demand rate normally is considered as a constant value, while in marketing and pricing, it is dependent on the selling price. The present study introduces a new type of economic production quantity (EPQ) inventory model. This production-inventory model is among the aspects that make the management of inventories more realistic and practical for managers. The pricing policy, planned backorders, and the rework process are included in the EPQ inventory model. The main contribution is that, in the EPQ inventory model, the price serves as a critical factor that affects the size of demand to maximize total long-term profit. The primarily objective is to determine the optimum selling price, discrete values for the optimal lot size, and the level of optimal backorders so that the total profit is maximized. To accomplish the optimal value for the decision variables an algorithm is developed. The proposed algorithm provides an accurate solution for production managers to jointly decide on lot size, backorder size, and selling price. In addition, a numerical example is solved using real industry data. The results affirm that the total profit obtained using the production-inventory model increases significantly in comparison with the current situation. Furthermore, sensitivity analysis is carried out in order to describe the practical application of the suggested production-inventory model.

Commercial model to optimize supply chains in local markets

Today, inventory management is a highly studied and interesting topic to optimize the cost of managing them. Traditional inventory models generally tend to block inventory optimization, but the more the years have passed, the concept of "supply chain" has been implemented, where the satisfaction of the needs of the final customer is achieved through material flows, information and financial, which are established from the original suppliers to the last consumer, requiring a certain level of cooperation and integration between the participants in the chain. That is why through the present the design of a sustainable model to optimize supply chains in local businesses in Jerez, Zacatecas is investigated, proposed, and exposed.In this paper, the optimization of inventories is studied, creating an effective model that combines the traditional with the current and that reflects the advantages of this approach in the total inventory costs in the chain, that is, the optimization.

A deterministic multi-item inventory model with quadratic demand under neutrosophic and pythagorean hesitant fuzzy programming approach

In this paper, a deterministic fuzzy inventory model with quadratic demand pattern, time-dependent holding cost, and time-dependent deterioration has been created. We have taken the rate of shortage as fully backlogged. In traditional inventory model, most of the cost parameters were taken as crisp. But in real world, fuzzy cost parameters are more realistic to appear instead of crisp parameters. Due to uncertainty we have considered all the cost parameters as hexagonal fuzzy number. We have regarded all of the cost parameters as hexagonal fuzzy numbers due to uncertainty. Our primary goal is to reduce the total average cost, and for that we've covered two optimization techniques: the Pythagorean Hesitant Fuzzy Programming Approach (PHFPA) and the Neutrosophic Hesitant Fuzzy Programming approach (NHFP).Through extensive numerical experiments and sensitivity analysis, the effectiveness and practicality of the proposed model are demonstrated. Comparative studies with traditional inventory models underscore the advantages of the Neutrosophic and Pythagorean Hesitant Fuzzy Programming Approach in managing multi-item inventory systems with quadratic demand patterns, highlighting its robustness in handling uncertain and ambiguous information.

“Cultural inventories”: How dimensions of national culture moderate the effect of demand unpredictability on firm-level inventories

While national culture has been recognized as an important driver of business decisions, our understanding of its effects on firm-level inventories is limited. In this paper, we argue that the national culture dimensions of uncertainty avoidance and long-term orientation influence biases and behaviors that affect how managers make inventory decisions in light of demand unpredictability. These dimensions of national culture thereby explain variations in the strength of the demand unpredictability-inventory relationship at the firm level. Using a dataset of 9127 firm-year observations from 25 countries and 20 manufacturing industries between 2006 and 2019, we find that the positive effect of demand unpredictability on inventories is augmented in high uncertainty avoidance cultures and weakened in long-term oriented cultures. These results are consistent with the notion that demand unpredictability is differentially interpreted across national cultures, thus explaining variations in firms’ inventory levels beyond what can be predicted by traditional inventory theory models. Implications for future research and managerial practice are discussed.

Stochastic Programming Model for Lateral Transshipment Considering Rentals and Returns

Supply chain management is a large scale planning under uncertainty. It is significant that building an efficient supply chain under uncertain condition; however, it is difficult. There are many traditional inventory transshipment models targeting only rental, not including return. This study provides the uncertainty between rentals and returns by scenarios with multi-period stochastic programming model of inventory transshipment problems. The moment matching method was used to reduce the amount of the scenarios, and the comparativeexperiment shows the utility of this study model.

Queuing Models for Analyzing the Steady-State Distribution of Stochastic Inventory Systems with Random Lead Time and Impatient Customers

In material management, the inventory systems may have good management aspects in terms of materials; however, this negatively affects the relationship between the facility and customers. In classical inventory models, arriving demands are satisfied immediately if there is enough on-hand inventory. Traditional inventory models consider optimization problems and find the optimal policy of decision variables without computing the stationary distribution of the inventory states for random demand. Hence, a detailed analysis of inventory management systems requires a joint distribution of system stock levels and the number of requests to be investigated thoroughly. This research provides a new stochastic mathematical model for inventory systems with lead times and impatient customers under deterministic and uniform order sizes. The proposed model identifies the performance measures in a stochastic environment, analyzing the properties of the inventory system with stochastic and probabilistic parameters, and finally, validating the model’s accuracy. To analyze the system, balance equations were derived from a mathematical characterization of the underlying queuing model dependent on the Markov chain formalism. The precise performance was achieved by examining the graphical representation of the service process in a steady-state as a function of both arrival distribution and the customer patience coefficient, while it was challenging to derive an optimal curve fit in a three-dimensional space that features two input variables and a single output variable.

Hybrid Fuzzy and Data-Driven Robust Optimization for Resilience and Sustainable Health Care Supply Chain with Vendor-Managed Inventory Approach

One of the problems that government managers deal with are medical inventory management in COVID-19 conditions. Based on this situation, the best strategy for managing and reducing inventory costs can be Vendor-Managed Inventory (VMI) policy in the recent decade. Therefore, a hybrid fuzzy and data-driven robust optimization for Resilience and Sustainable Health Care Supply Chain (RSHCSC) with VMI approach is appropriate for improving the inventory management system and tackling uncertainty and disruption in this situation. Three RSHCSC models are suggested using hybrid fuzzy and data-driven robust optimization with a stochastic programming approach. The first model is average and mean absolute function, the second model is Conditional Value at Risk (CVaR), the third model is Minimax model, and the final model is the traditional inventory model. Each of the proposed models has advantages and disadvantages that depend on the conservative level of decision-maker. Sensitivity analysis is done on essential parameters like fuzzy cut, confidence level, robust and resilience coefficient, and size models. The results show that increasing fuzzy cut, confidence level, robustification coefficient, resiliency coefficient, and CVaR confidence level amount of costs grows. The Minimax function is suitable for conservative decision-makers.

Fuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages

In this paper, a fuzzy inventory model with a Weibull deterioration rate, a quadratic demand rate, and a variable holding cost under permissible shortages has been developed. The deterioration rate is expressed by a two-parameter Weibull distribution. During a shortage, some buyers wait for the actual product, while others do not. This shortfall is considered partially backlogged in this model. Some buyers wait for the actual product during such shortages, but many do not. Therefore, partially backlogged shortages are taken into account in this approach. In a traditional inventory model, all parameters such as purchasing cost, shortage cost, holding cost, etc. are predetermined. However, there will be some variations. As a result, fuzzy factors are more accurate to deal with the real world?s problems. This research attempts to cut down the cost in a fuzzy environment by using quadratic demand, shortage, Weibull deterioration rate, and variable holding cost. Costs such as ordering, shortage, and deterioration are addressed as pentagonal fuzzy numbers that are defuzzified using a graded mean representation approach. Finally, sensitivity analysis was carried out to investigate the influence of cost parameters on total inventory cost. A numerical example is used to validate the proposed model in a real-world system.

Using blockchain technology to extend the vendor managed inventory for sustainability

Nowadays, Information Technology (IT) is changing the way traditional enterprise management concepts work. One of the most dominant IT achievements is the Blockchain Technology. This technology enables the distributed collaboration of stakeholders for their interactions while fulfilling the security and consensus rules among them. This paper has focused on the application of Blockchain technology to enhance one of traditional inventory management models. The Vendor Managed Inventory (VMI) has been considered one of the most efficient mechanisms for vendor inventory planning by the suppliers. While VMI has brought competitive advantages for many industries, however its centralized mechanism limits the collaboration of a pool of suppliers and vendors simultaneously. This paper has studied the recent research for VMI application in industries and also has investigated the applications of Blockchain technology for decentralized collaboration of stakeholders. Focusing on sustainability issue for total supply chain consisting suppliers and vendors, it has proposed a Blockchain based VMI conceptual model. The different capabilities of this model for enabling the collaboration of stakeholders while maintaining the competitive advantages and sustainability issues have been discussed.

The optimal delivery time and order quantity in an oligopoly market with time-sensitive customers.

With the development of e-commerce, delivery time is regarded as a key competitive advantage in an oligopoly market, as shortening the delivery time can stimulate demand for products. Many firms adopt a variety of strategies to shorten delivery time, and holding sufficient inventory is reported as an effective way. This study integrates a market share attraction model based on delivery time competition with the traditional inventory model to determine the optimal delivery time and order quantity. With the use of supermodular game method, we investigate the effect of changes in marketing and operations factors on the equilibrium delivery time and order quantity in non-dominated and dominated oligopolistic markets. The results reveal that the equilibrium delivery time and order quantity exhibit a directional response to changes in marketing and operations factors, and the response differs between the non-dominated oligopoly and the dominated oligopoly. Furthermore, under a cooperative oligopolistic market with asymmetry, it is beneficial for the firms with high competitive strength to adopt the delivery time strategy, but it fails to do so for the firm with the low competitive strength. Lastly, numerical analysis suggests that marketing factors play a more important role in affecting equilibrium measures than operations factors.

Optimal ordering policy for short life-cycle products under credit financing with dynamic adoption in supply chain

Traditional inventory models are mostly ignorant of the life cycle dynamics of a technology product; hence, they often fail to identify different dimensions of inventory research. This paper attempts to investigate the relationship between adoption behavior of customers using life cycle dynamics and associated trade credit policies in order to optimize the total inventory cost. The demand model used in this paper treats sales as a function of awareness diffusion and adoption. Awareness is considered as a function of feedback effects from users/customers. Retailer’s optimal strategies for short life cycle product under credit financing were determined analytically. Finally, numerical examples have been used to support the theoretical results. Theoretical results have further been used to gain some managerial insights.

State-of-the-art review of customer to business (C2B) model

With the continuous improvement of living standards, customers are ever more looking for products which are characterized by individualization, entertainment and scene. The production model of the enterprises is moving from the traditional inventory model to the manufacturing model driven by the customer orders. In this paper, we have summarized the birth, concept, characteristics, classification, evolution, related technologies and application of C2B (Customer to Business) models. Through the analysis of C2B which is based on e-commerce, mass customization and mass personalization, we have summarized and redefined the several forms of C2B: such as Customer to Business to Manufactory- Mass Customization (C2B2M-MC) model, Customer to Business to Manufactory- Mass Personalization (C2B2M-MP) model, Customer to Manufactory (C2M) model, CPM (Customer Platform-connecting Manufactory) model, Customer to Manufactory to Service (C2M2S) model, and have made a comparative analysis on these models. Finally, the implementation stages were proposed and effects of C2B were summarized. In addition, in the prospect of subsequent research, combining with the existing analysis and C2B projects we involved in, we will derive an overall framework of C2B. The research theory of this paper can give a certain reference to enterprises in the new period from the traditional mode to the network innovation mode. At the same time, it also has a good implementation effects in the related C2B projects we have participated in.

Coordination of a three-level supply chain with variable demand and order size dependent trade credit in healthcare industries

In a traditional inventory model, the customer who purchases the goods is expected to pay the seller immediately. However, in many practical situations, the seller will give the customer a reasonable postpone period when the customer’s request amount surpasses a given limit. Now-a-days, the time-dependent demand plays a vital role in the healthcare industries. This paper, we generalized an order linked trade credit approach in three-level supply chain system, including the pharmaceutical manufacturer, the wholesaler and, the hospitals are involved. The pharmaceutical manufacturer gives a permissible delay period to the wholesaler and, the wholesaler likewise gives an order linked trade credit policy on the hospital. This entire investigation is examined in a pharmaceutical stock model for time-dependent production and time-dependent demand of healthcare industries. We outline the pharmaceutical model of three-level supply chain system with cost minimization to decide the optimal cycle time. Further, we decide the optimal cycle time, optimal order quantity and optimal payment time. The model is solved analytically by minimizing the total annual inventory cost of the centralized supply chain model. To delineate the pharmaceutical inventory models the numerical examples are provided. The sensitivity analysis is carried out for parameters and, the managerial implications are also obtained.

A new customization model for enterprises based on improved framework of customer to business: A case study in automobile industry

With the continuous improvement of living standards, customers are gradually pursuing products which are individualization, entertainment, and scene. The production model of the enterprise has changed from the traditional inventory model to the manufacturing model driven by the customer orders. By analyzing the evolution process of customer to business, we get the overall framework of customer to business based on customer platform-connecting manufactory platform-connecting service. A case study in the automotive industry is introduced, which takes customer to business as the implementation of enterprise strategy. The detailed implementation process is discussed from the four aspects of open and networked demand for personalized customer (C), open and networked manufacturing (M), open and networked service (S), open and networked collaborative platform (P). The objective of this article is to provide general references for enterprises to change from traditional business-to-customer model to open and interconnected customization model by combining the improved customer-to-business framework and its application in automotive industry. The framework we put forward has achieved good results in related customer-to-business projects we participated in. The research results of this article can be used as a reference for enterprises to design, set, and carry out the model of customer to business.

Research on Configuration Optimization of Civil Aircraft Spare Parts with Lateral Transshipments and Maintenance Ratio

A research method for multi-echelon inventory allocation of civil aircraft spare parts with lateral transshipments and maintenance ratio is proposed in this paper, and the maintenance rate is considered into the inventory model, aiming at the problem of multi-echelon inventory allocation with lateral transshipments for civil aircraft spare parts. Firstly, the principle of the multi-echelon inventory model with lateral transshipments and maintenance ratio is proposed. Then, by considering the fleet availability and the satisfaction ratio as the constraints, a mathematical model is built with the inventory system total cost as the optimization objective. Secondly, the procedure of solving multi-echelon inventory allocation model considering the influence of lateral transshipments and maintenance ratio is discussed based on the marginal analysis method. Finally, some components of Boeing 737 are selected as the research objects, the multi-echelon inventory allocation is investigated by considering lateral transshipments and maintenance ratio. The analysis results in this paper show the best number of multi echelon inventory allocation, the minimum inventory system total cost and the fleet availability are 1.205 million dollars and 0.9909 respectively. Comparing the traditional inventory model without considering lateral transshipments and maintenance ratio, and the model which only considers the maintenance ratio, the results indicate that the model offered in this paper could reduce the total cost of inventory system significantly, and provides theoretical support and method guidance for the accurate allocation of multi echelon inventory optimization for civil aircraft spare parts.

Inventory model optimization revisited: Understanding service inventories to improve performance

Services are increasingly important in the modern economy, for both service and manufacturing firms, yet inventory literature has been focused mainly on physical goods and, only a few studies have included services in optimization. On the other side, traditional service management literature relies on an extremely narrow definition of inventory that excludes services, because they are perishable. Thus, there is a lack of research in the link between inventory optimization and service management. However, according to a new service inventory approach, businesses components such as tasks or information, as different from physical goods, can be performed and stored in anticipation of service demand as a form of service inventory—that is, inventorying by anticipation rather than delaying the service. This article aims to contribute to this lack of research by proposing a service inventory optimization model that integrates a service orientation to optimize tasks and information to be performed in advance. In contrast with traditional inventory models, where the objective is to optimize physical items, in this approach physical products whenever included, constitute only mechanisms for service provision. This service inventory model contributes to optimize the quantity of tasks or information to be anticipated and thus provides benefits for customers.

Research on Ordering Strategy of Perishable Products with Variable Demand

This paper mainly considers the influence of price on demand for perishable products. According to the characteristics of products and the condition of demand, demand is a function of price elasticity. Under the premise of allowing the shortage, this study constructs optimal inventory control model of perishable products by introducing the lost order coefficient and solves the model, improves the traditional inventory model. Using optimization method, which deduces the optimal ordering cycle, the optimal order quantity and the total inventory cost, etc. Through the example for the deterioration rate and service level to sensitivity analysis, the results show that as deterioration rate increases, the total inventory cost and sale price are on the rise, while the unit profit is decreasing; The service level is directly proportional to the selling price, inversely proportional to the total inventory cost and unit profit. This research provides a theoretical basis for firms in the efficient inventory management.