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Purpose: In general, inventory optimization is one of the most important techniques in the production system, because the high cost of an empty warehouse and the cost of losing customers can cause serious damage to a system. The basic idea for inventory is to provide flexibility for a system and protect the system against events such as stock out. Inventory capacity for each product or part is defined by demand, delivery time and part price. By balancing the supply and demand rates, the optimal inventory capacity can be achieved. It is necessary to use practical and effective techniques and solutions to reduce breakdowns to optimally use existing equipment and resources and reduce large costs in terms of energy wastage and repairs and repurchase of equipment. In this context, spare parts are one of the most important links in performing optimal maintenance and repairs and quickly returning equipment to the production line. In good management of spare parts, the inventory system of the warehouse will lead to the reduction of maintenance and repair costs, manpower and the duration of equipment failure and will ultimately help to increase productivity. This study aims to optimize the inventory level of spare parts for military vehicles using a mixed probability distribution. Design/methodology/approach: First, based on the literature review and selected basic articles, the research gaps have been identified, and accordingly, a mathematical model has been developed and solved to optimize the spare parts of military vehicles. Then, a meta-inventive method has been used to solve the problem. This is because the use of meta-heuristic methods to check and analyze the sensitivity of inventory optimization models can lead to better and more accurate results, and also the use of these methods is relatively new and helps in solving optimization problems. Also, the problem studied in this research is a non-linear integer programming model, which has been used for the problems of medium and large dimensions due to the complexity of the problem. The meta-heuristic method based on the genetic algorithm has been applied to save the total costs. Finally, to prove the effectiveness of the model, the proposed model has been implemented in a case study on the parts of military vehicles in the 177th Brigade of Torbet Heydarieh. Findings: Findings indicated that the optimal system cost value and the economic order value were obtained during specific iterations of the model, i.e., the first, fourth, and tenth iterations. These points implied the effectiveness of integrating Poisson and Exponential distributions in the model and optimizing the system performance in different scenarios. Such results emphasize the consistency and robustness of the proposed inventory management strategy, especially when demand fluctuates and supply challenges. As the model is subjected to more iterations, differences in results are observed, indicating the potential for variability with increasing iterations. For example, if the model considers 100 different problems or scenarios, different results may appear, although a general consistency in system behaviour is noted. This indicates flexibility in the modelling approach, where even significant changes in parameters such as inventory costs or lead times are unlikely to drastically change the economic value or efficiency of the system. Research limitations: This research was conducted on a case-by-case basis on the parts of military vehicles of the 177th brigade of Torbat Heydarieh city, so it should be possible to generalize it to other organizations, and because it was typically a cross-sectional study, conclusions about on the causality might seem difficult. Practical implications: The main challenge in the supply chain is to control inventory levels by determining the size of orders for each department during each period to optimize the objective function, which has been investigated in various studies because inventory optimization is one of the important and practical techniques for optimizing the economic value of the order and realizing a stable situation in a production system. This is particularly important since the high cost of an empty warehouse and the cost of losing customers can cause serious damage to a system Social implications - Due to the specific conditions of embargo and restrictions on access to international markets, accurate inventory management can serve as a key tool to maintain efficiency and sustainability in military operations. This research recommends that relevant organizations continuously analyze and optimize their inventory levels using mathematical models and optimization algorithms such as genetic algorithms to avoid additional costs and at the same time, to ensure the supply of parts in times of need. Originality/value: Predicting exactly what and how many spare parts are needed for the necessary equipment in a business and when they are needed to be available in its warehouse is an important issue to consider. These parts are identified and managed to support the functions of critical equipment, and the lack of critical spare parts during planned or unplanned repairs will significantly influence the overall effectiveness of the equipment. [ABSTRACT FROM AUTHOR]

We witness an increased emphasis on the integration of different areas of manufacturing firms with the intention of avoiding suboptimal solutions. In particular, due to new technologies which make it easy to change the price of a product in real time, the integration of pricing and production planning may be garnering the most interest. We are proposing in this paper a way to model the dynamics of the price. Thus, the price and the inventory level are considered as state variables whereas the supply (production) rate is the control variable. The demand rate is dynamic and state-dependent. Using a model predictive control approach, the optimal supply rate, and thus the optimal price and inventory level, are obtained. Different examples are provided under different scenarios for the supply rate and for the demand rate.

We witness an increased emphasis on the integration of different areas of manufacturing firms with the intention of avoiding suboptimal solutions. In particular, due to new technologies which make it easy to change the price of a product in real time, the integration of pricing and production planning may be garnering the most interest. We are proposing in this paper a way to model the dynamics of the price. Thus, the price and the inventory level are considered as state variables whereas the supply (production) rate is the control variable. The demand rate is dynamic and state-dependent. Using a model predictive control approach, the optimal supply rate, and thus the optimal price and inventory level, are obtained. Different examples are provided under different scenarios for the supply rate and for the demand rate. [ABSTRACT FROM AUTHOR]

We study the effect of managerial time horizon on two key operations decisions: inventory levels and capacity investment. For identification, we exploit a quasi‐natural experiment provided by the staggered adoption of constituency statutes, which alleviate managerial short‐termism by providing legal protection to executives adopting a long‐term approach in their corporate decisions. Using a staggered difference‐in‐differences design, we find that, after the reforms, firms incorporated in constituency states (treated firms) increased inventory and capacity investment by 5.2% and 15.4%, respectively, relative to firms not incorporated in constituency states (control firms). We also find that these increases are gradual and persist over time, suggesting that they are structural in nature. We further show that the effect of constituency statutes on inventory levels and capacity investment are stronger for firms with ex ante higher level of managerial short‐termism, such as firms with low institutional ownership. Performance also increases relatively more for affected firms with higher ex ante managerial short‐termism. Our results pass a battery of robustness and validity tests. Interestingly, while constituency statutes are intended to protect executives from short‐term oriented shareholder sanctions, our findings suggest that these statutes ultimately benefited not only executives, but also potentially the long‐term interest of shareholders. Even in the absence of regulation, executives facing short‐term pressure could intensify communication efforts with shareholders on how specific operational decisions and investments would be beneficial to the company. Notably, executives could use various media channels to help shape retail investors' attitude toward operational investments that may create future value. [ABSTRACT FROM AUTHOR]

In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality of service at best and a loss of customers and system failure at worst. This paper focuses on using machine learning (ML) classifiers, most notably random forest and gradient tree boosting, for estimating the inventory level from available features including historical data. However, while similar approaches adopted in the context of bike sharing assume the data to be well-balanced, this assumption is not met in the case of the inventory problem. Indeed, as the demand for bike sharing is sparse, datasets become biased toward low demand values, and systematic errors emerge. Thus, we propose to include a new iterative resampling procedure in the classification problem to deal with imbalanced datasets. The proposed model, tested on the real-world data of the Citi Bike operator in New York, allows to (i) provide upper-bound and lower-bound values for the bike-sharing inventory problem, accurately predicting both predominant and rare demand values; (ii) capture the main features that characterize the different demand classes; and (iii) work in a day-to-day framework. Finally, successful bike-sharing systems grow rapidly, opening new stations every year. In addition to changes in the mobility demand, an additional problem is that we cannot use historical information to predict inventory levels for new stations. Therefore, we test the capability of our model to predict inventory levels when historical data is not available, with a specific focus on stations that were not available for training. [ABSTRACT FROM AUTHOR]

In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality of service at best and a loss of customers and system failure at worst. This paper focuses on using machine learning (ML) classifiers, most notably random forest and gradient tree boosting, for estimating the inventory level from available features including historical data. However, while similar approaches adopted in the context of bike sharing assume the data to be well-balanced, this assumption is not met in the case of the inventory problem. Indeed, as the demand for bike sharing is sparse, datasets become biased toward low demand values, and systematic errors emerge. Thus, we propose to include a new iterative resampling procedure in the classification problem to deal with imbalanced datasets. The proposed model, tested on the real-world data of the Citi Bike operator in New York, allows to (i) provide upper-bound and lower-bound values for the bike-sharing inventory problem, accurately predicting both predominant and rare demand values; (ii) capture the main features that characterize the different demand classes; and (iii) work in a day-to-day framework. Finally, successful bike-sharing systems grow rapidly, opening new stations every year. In addition to changes in the mobility demand, an additional problem is that we cannot use historical information to predict inventory levels for new stations. Therefore, we test the capability of our model to predict inventory levels when historical data is not available, with a specific focus on stations that were not available for training.

The United States Marine Corps (USMC) Installation and Logistics Command requested a study for determining appropriate inventory levels of war reserve materiel to meet future operational needs under surge demands in uncertain environments. This study sought to explore a potential approach by using the common newsvendor model, but modified for a military scenario. The authors' novel version of this core concept considers the purchase and storage costs of an item and proposes an intangible cost function to capture the consequences of a shortage. Further, they show a sample application of the model using a ubiquitous military item-the BA-5590/U battery. The output of the model provides USMC with a new tool to optimize inventory levels of a given item of interest, depending on scenario inputs. [ABSTRACT FROM AUTHOR]

Purpose: Demand Driven Material Requirements Planning (DDMRP) aims to deal with variability by adjusting inventory levels while maintaining, or even increasing, customer service levels. This approach bridges the push and pull approaches. Even though it first made its appearance in 2011, research in this field remains relatively limited. This paper aims to analyze the temporal evolution of the DDMRP, its context of implementation, and the research themes studied in that field to identify areas that still need to be addressed by future researchers. Design/methodology/approach: The systematic literature review approach used in this paper examines research dealing with the DDMRP published in different languages between 2011 and 2020. To date, published papers focused on performance analysis and comparison of the DDMRP with other methods. This study focuses on the DDMRP itself and its capacity to answer the different operations management functions under a broader vision (context, implementation, setting, etc.). Thus, we analyze the evolution of the method in the literature, considering different languages, and present a taxonomy of published scientific works. Then, we identify gaps that require further research. Findings: DDMRP is not sufficiently studied in the literature and the proposed tools still need to be tested. More research is required to evaluate its adaptability in different and complex environments, address its ability to cope with product variety, complex BOMs, and other methods. A proposal of new approaches to define and adjust DDMRP parameters and evaluate their sensitivity is also required to make the method more scientifically sound. Originality/value: Based on the analysis of papers published in different languages, this paper outlines the current state of the art of DDMRP, its shortcomings, and identifies further research to make DDMRP more robust from both a scientific and industrial perspective. [ABSTRACT FROM AUTHOR]

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This paper addresses a dynamic capacitated production planning (CPP) problem in small to medium-sized enterprises (SMEs). These companies practise their businesses based on a manufacturing paradigm called one-of-a-kind production (OKP). Typically, they employ outsourcing in their CPP practice. Therefore, the CPP problem in this paper includes outsourcing, which is called the outsourcing CPP problem. The outsourcing strategy considered in this paper has the following features: (1) outsourcing takes place when the demand exceeds the capacity; (2) all excessive demands are outsourced without backlogging; and (3) production, inventory, and outsourcing levels all have a limit. One important difference of the problem considered in this paper from other problems is that the cost functions are considered arbitrarily time-varying. The outsourcing CPP problem is formulated into an optimization problem which captures the mentioned features. The objective of the problem is to minimize the total cost. It is very likely that there is no solution in our model so we propose a necessary and sufficient condition for feasible solutions. Dynamic programming techniques are employed in searching for a global optimal solution with a pseudo-polynomial time. This paper also presents a case study for a real-life problem in an OKP company to illustrate our approach and to show its effectiveness. The case study has shown that our approach is very effective. [ABSTRACT FROM AUTHOR]