مجله مدیریت تولید و عملیات
سال پانزدهم شماره 2 (پیاپی 37، تابستان 1403)

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 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.

Today, the efficient management of supply chains plays a fundamental role in the market and economy. The supply chain is a network of facilities working together to make and move products from upstream to downstream to provide customers with highly qualified products and services. Nowadays, construction has become a growing and huge industry sector worldwide. One of the supply chains that needs proper management is related to the construction industry. The purpose of this article is to optimize this type of supply chain by minimizing its total costs. 

Design/methodology/approach:

 An attempt has been made to develop an optimization model for the construction supply chain, considering all the important elements involved in the construction process, i.e. contractors, designers, suppliers of materials and construction materials, as well as three important and basic flows in the construction industry, i.e. the flow of manpower, the flow of equipment and machinery, and the flow of materials. All indices, parameters, decision variables, objective functions and constraints have been introduced and presented in the proposed model.

The model proposed by GAMS optimization software was solved and the obtained results included the lowest construction cost as well as the optimal amount of construction materials and materials, labour, equipment, and machinery based on the required construction size.

Research limitations/implications:

 The application of the supply chain in the construction industry is a relatively new topic. In the classic supply chain, the flow of materials and output at the end of the chain includes the manufactured product, while in the construction supply chain, the final output includes a building or a structure. Individuals, industries and even countries incur a lot of construction costs to meet their needs in the field of construction. The current study was influenced by limitations such as access to real data and the impossibility of handling a real case study, because the problem of designing the construction supply chain has wide dimensions and requires access to all dimensions of the construction industry chain, from upstream to downstream.

Practical implications: 

With the definition and expansion of the concept of supply chain and the use of supply chain management in manufacturing industries and the positive results it brought in various manufacturing industries, supply chain management emerged in the construction industry. Meanwhile, researchers, major contractors, and large construction companies are trying to find methods to take advantage of the supply chain management approach. Also, the stakeholders of the construction industry can enable active decision-making and agile responses to market fluctuations by continuously monitoring and updating the results of cost sensitivity analysis.

Social implications: 

Optimizing the construction supply chain can lead to reduced costs, improved project timelines, and enhanced sustainability. However, it may also impact local communities through job displacement, environmental concerns, and social inequality. Balancing efficiency with social responsibility is crucial to ensure equitable outcomes in construction projects.

Originality/value: 

By now, there has been no reference available in the literature in the field of construction supply chain considering the designer, the flow of manpower and the flow of drawings and technical documents. The proposed model is comprehensive and includes the construction chain, considering all aspects such as the flow of required materials and materials, the flow of labour, the flow of required equipment and machinery, the flow of plans and documents, and designers and contractors.

Diversifying investment portfolios by incorporating a variety of assets is a well-established strategy for mitigating risk and enhancing returns. Traditionally, mathematical models for portfolio optimization have primarily focused on stock investments within the capital market. However, this study extends the scope of portfolio optimization to encompass both project and stock investments. This is a critical advancement as investors increasingly grapple with allocating budgets across these two asset types simultaneously. Therefore, this paper proposes a novel mixed portfolio optimization model that uses the Mean-SemiVariance-SemiEntropy approach. By incorporating project investments alongside traditional stocks, the proposed model offers more efficient portfolios that can lead to improved return/risk ratios for investors seeking to optimize their overall financial strategy.

Design/methodology/approach:

 An attempt has been made to bridge the gap between the distinct spaces of projects and stocks to facilitate their joint analysis. Subsequently, a Mean-SemiVariance-SemiEntropy approach has been employed to develop a model within a probabilistic framework. For validating this model, a numerical experiment involving three projects and five stocks from the capital market has been tackled, considering the preferences of an investor. Finally, the optimization problem has been solved using three metaheuristic algorithms: Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), and Gray Wolfs Optimization (GWO).

The results obtained by solving the model using the above-mentioned metaheuristic algorithms demonstrated that despite the high speed of the GWO algorithm, the solutions provided by the GWO algorithm were not satisfactory compared to the GA and ICA algorithms. On the other hand, the acceptable speed with nondominated solutions was the advantage of the ICA algorithm over the GA algorithm. The evaluation of various performance metrics also revealed that the ICA algorithm outperformed the GA and GWO algorithms in this problem. Also, the inclusion of semi-entropy as a risk assessment metric led to an improvement in the return on the investment portfolios.

Research limitations/implications: 

Incorporating investor constraints and preferences, such as cardinality and boundary constraints, into the model forms an NP-hard problem. Consequently, exact solution methods are replaced by non-exact methods, such as metaheuristic algorithms. Given the diversity of project contracts, this study concentrated solely on projects with cost-plus contracts, where the entire project or a portion can be selected for partnership. Similar to the Markowitz model, the projects' returns such as stocks' returns were assumed to be normally distributed.Practical implications: This study significantly enhanced diversification, increased potential returns, and reduced risk for investors by introducing a novel mixed project-and-stock portfolio optimization model. The proposed approach can be implemented by a wide range of investors and managers of investment units in various organizations, bringing a new perspective to investment management.

Social implications: 

The far-reaching implications of this study extend beyond the realm of investment management, permeating social, economic, and political areas. The innovative mixed project-and-stock portfolio problem has the potential to positively transform society using fostering innovation, stimulating economic growth, and enhancing financial knowledge. This paper can foster economic growth and job creation by providing new investment opportunities and increasing investment in productive ventures. In summary, this study has taken a significant step towards improving social and economic well-being by introducing an innovative model for resolving investment challenges.

Originality/value:

 The innovation and strength of this research lies in incorporating projects as a new asset class into the traditional portfolio model. This goes beyond simply adding a new asset to an investment portfolio, as the nature of the projects introduces new complexities to the portfolio management process. For this purpose, this study employs a probabilistic approach based on historical data. In addition, the simultaneous use of two risk measures, i.e., semi-variance and semi-entropy, significantly improves the performance of the model by focusing on different risk aspects. This provides a more comprehensive picture of the risks associated with the portfolio and helps investors make more informed and wise decisions.

This study aims to create and develop an approach for the design and gradual delivery of the product in the shortest time and through the balance of the elements of technological readiness, the need statement document, and the characteristics of the product in large and complex air systems.

Design/methodology/approach:

 In this study, the balancing process has been carried out using three stages of the Quality Function Deployment (QFD). In the first stage, product specifications have been prioritized using customer needs. In the second stage, product specifications have been prioritized based on the specifications of the first stage. In the third stage, the required technologies have been prioritized using the specifications of the product in the second stage. To conduct the research, industry experts who were in the unit related to the desired product provided the necessary data. Out of the 10 experts in that unit, seven have collaborated in the design and implementation of the model and three have approved the model. Finally, the discussion model was approved by the company's high committee.

After extracting the priorities of technologies, the final meeting of the QFD group was held with the presence of expert designers and the operator's representative. In this meeting, the process of using QFD and the obtained results were discussed. Then, using the scores obtained for the technologies (priority value) level of technological readiness for each of the product technologies and the approaches of designing, producing and delivering the primary, intermediate and final products were determined. The results of product prioritization based on technology development were also approved by the high committee.

Research limitations/implications:

 In this research, the balance of technological readiness, a document of requirement statement, and product characteristics in the design management of macro systems of defence air base products were analyzed by QFD. There was a need to check if there were other tools for balancing. Also, the scope of this study was limited to product design, and it is necessary to extend the balance to the entire life cycle of the product. In this research, the researchers faced with challenges due to the lack of familiarity with the elites or complete and sufficient research and training of the elites and managers of the country's research and defence industries. Also, the lack of managerial approaches to system and standard design and the integration of the approaches communicated by the regulatory and standardization centres of the country on the system design of large and complex products were the other limitations of this research.

Practical implications: 

The model proposed in this research made it possible to produce large and complex products in the aviation industry due to the existence of restrictions. On the other hand, the gradual design, production, and delivery of big products made the products suitable for the user's scene to be designed and produced first. While maintaining the quality of the product, the time to obtain the products should also be reduced.Social implications: Acquiring large and complex products in the country will accelerate the country's development.

Originality/value:

 The design and production of large and complex products based on technology development with a gradual product delivery approach using QFD is one of the innovations of this research. The application of the proposed approach will resolve some of the problems related to the design of large products in different areas.

The supply chain of cellophane includes an independent set of parts whose relationship has to be properly managed to ascertain efficiency, performance, and sustainability. This research aims to provide detailed information regarding the cellophane supply chain and, using the Supply Chain Operations Reference model, act as a benchmark to enhance the adaptability and growth of the supply chain. Therefore, it can help the cellophane supply chain to remove its performance gap.

Design/methodology/approach: 

Interpretive Structural Modelling (ISM) has been used along with the Multi-Criteria Decision-Making (MCDM) approaches, supported by the Analytic Network Process (ANP). ISM has been applied to analyze the interdependencies among different components of the supply chain; DANP has been implemented to prioritize the processes of the supply chain and potential suppliers. An investigation has been also performed on the intensity of influence and dependence among various methods in the supply chain.

Findings indicated that the current market, demand, and competition are the biggest known factors impacting supply chain management. While this is more about learning the present state of the market, it is also important to realize future trends, customer needs, and competition. It includes information on marketing research, customer surveys, and competitive studies. A five-level hierarchical structure of the supply chain processes is reported, with levels one and five as the key drivers. The first level is wherein the market understanding, customer demand, and competitor practices are known. The fifth level involves the identification of potential risks and working out strategies to mitigate the risks. This level is all about active risk management. This involves developing ways through which contingency plans and strategies can be drafted to ensure the impact of the identified potential hazards is reduced. This level is so critical because it has a tremendous impact on performance and sustainability in the supply chain. Identifying potential risks and formulating strategies to reduce them are seen as independent variables with enormous impacts on other processes. These are very important processes as they could affect the effectiveness and efficiency of the entire chain. Both require careful management and continuous improvement to guarantee the smooth operation of the chain.

Research limitations/implications:

 This study concentrated on possible constraints due to supply chain complexity and market changes. Since the cellophane supply chain is dependent on several factors, further research on optimization methods and improvement of decision-making processes can lead to more effective operational results.

Practical implications:

 Findings are likely to give insight into the supply chain of cellophane, with an understanding of the market, customer demand, and competitive landscape. It also goes on to highlight potential risks, and the strategies formulated to reduce them. The use of the DANP technique helped rank the supply chain processes and identify potential suppliers, hence assisting in the effective allocation of needed resources such as materials, labour, and capital. This will assist those individuals concerned with the cellophane supply chain in the process of planning and making strategic decisions. This study contributes to the knowledge of SCM since it has elicited how much one needs to understand the market, manage risks, and ensure compliance.

Social implications: 

Given the significance of sustainability and the need to lessen effects in the packaging sector, this study seeks to enhance procedures and mitigate risks, within the cellophane supply chain. Such efforts aim to safeguard the environment and uphold accountability.

Originality/value:

 This study presented a comprehensive analysis and optimization of the cellophane supply chain using ISM and MCDM approaches. The new integrated approach of ISM and DANP helps to improve management processes and reduce supply chain risks.

Today, due to the increase in complexities and uncertainties faced by manufacturing organizations, the production of products faces planned/unplanned events that happen during the production life cycle. Meanwhile, some factors can have a significant impact on the success or failure of the organization, and the most effective of such factors is the occurrence of delays and disruptions in the production and delivery schedule. Some of these factors are caused by the nature of the industry, which is difficult to reduce/eliminate. The effect of many other factors can be reduced and sometimes eliminated. Most of the manufacturing-related literature focuses on providing solutions to the issues that lead to delays. The neglected point in the literature is to identify the underlying factors that lead to delays in the production schedule and delivery of products and to provide a comprehensive framework for identifying delays in production environments. This study aims to reduce delays and optimize production time by identifying and ranking the possible factors causing delays in production organizations based on the review of the literature related to production and empirical research.

Design/methodology/approach

: This research includes a combination of quantitative and qualitative methods to provide a more detailed analysis of the dimensions related to the problem. First, four types of production systems, i.e., engineering to order, manufacturing to order, assembly to order and production to warehouse have been described, and then these production environments have been compared with different aspects. In the following, by reviewing previous research and benefiting from experts, factors affecting delays in production projects have been identified and localized, and the weight of each criterion has been determined using Shannon's entropy method. Finally, by applying this model in the manufacturing industries of Iran and using the TOPSIS technique, the causes of delay have been ranked.

Due to their nature, production systems are exposed to risks caused by various delay factors. The results indicated that engineering based on order, manufacturing based on order, assembly based on order, and production for warehouse are exposed to more risks, respectively. The most important causes of delay in each production system were among the other results. Based on the findings, it seems that manufacturing organizations, knowing the nature of the system, rank their system in terms of delays, and prevent unfortunate events in this field by careful planning.

Research limitations/implications:

 The extent of industries and manufacturing companies in identifying comprehensive criteria contributed to the study. However, from the point of view of specialization of the results, every industry must obtain relevant results by implementing the proposed approach according to the type of its production system. Therefore, more context-specific research is needed to examine the unique aspects of organizational cultures that include social, political, economic, technological, personnel, and personal considerations. Therefore, future studies should provide the analysis and findings in each case because each one will bring distinctive and unique findings in its case.

Practical implications:

 Accurate identification of the production system used in manufacturing industries and the main causes of delay can provide managers with a more effective view to prevent destructive consequences.

Originality/value:

 This study investigated the causes of delays in various production projects in Iranian manufacturing organizations. No research has been performed on investigating the reasons for delays in various types of production environments in the manufacturing organizations in Iran, as a developing country exhibiting differences in the aspects of cultures, social, political, economic, technological, personnel and personal.