نشریه مدیریت صنعتی
سال پانزدهم شماره 48 (بهار 1402)

Numerous stakeholders with divergent perspectives have complicated the risk management process in the oil and gas sector. Additionally, systemic risks such as population growth in industrial areas or the severe decline in oil and gas prices have cast doubt on the effectiveness of organizational risk management strategies. As a result, an appropriate framework should be used that incorporates the expertise, values, and interests of many stakeholders into the risk management decision-making process. Although the International Risk Governance Committee (IRGC) framework has been widely implemented in different contexts, it should be customized for different purposes due to cultural and geopolitical concerns. This paper customizes the  IRGC framework to propose a modified framework for the oil and gas industry in Iran. Then, it defines a set of criteria to measure the risk governance framework's performance.
To carry out this research, a fuzzy hybrid multi-attribute decision-making method was applied to prioritize the elements of distinct phases to offer valuable information for resource allocation in different aspects. A risk-governance performance index was then proposed to calculate the performance of such a framework based on the prioritization of the elements. Different interviews were also conducted to collect the required information for the determination of the framework’s elements and calculate their relative importance as well as the selection of evaluation. The model was validated through the face validity method. A case study at three oil and gas industrial sites was conducted to test the applicability of the proposed framework.
To determine the relative importance of each element and optimize resource allocation for the success of the IRGC framework, it is necessary to assess the impact of each element. This article used the DEMATEL-Fuzzy ANP method for this purpose. Two important parts of the risk governance framework include the "realization of selected options" and "identification of options". The oil and gas industry should communicate with all stakeholders regarding the various risk mitigation options, as the options presented usually impact the operation of industrial sites. Implementing the selected options will ensure the framework's survival and contribute to the continuity of operations at industrial sites, while minimizing strategic, operational, safety, and environmental concerns. Additionally, according to the results of this article, "interaction with evaluators and consulting companies" significantly affects the success of the risk governance framework. Therefore, companies in the oil and gas industry should specify the areas of communication and how to communicate with evaluators and consulting companies in writing and evaluate the effectiveness of these communications.
To support all stakeholders, the organization should conduct a thorough exploration of the drivers and advantages of a risk governance framework. Integrating managerial decision-making and risk management, allocating shareholder capital, maintaining production continuity, minimizing safety and environmental risks, and managing emerging risks are all critical in this context. Our proposed framework considers all these drivers to determine the importance of risk governance elements for oil and gas companies. Moreover, this study proposes a set of criteria to measure the risk governance framework's performance.

Today, productivity is one of the most important factors in economic growth. At the organizational level, a high level of productivity indicates optimal performance to gain a competitive advantage. Despite the important role of productivity of service organizations in economic growth, few studies have been conducted in this context. The purpose of this study is to evaluate service productivity by a combined fuzzy best-worst method and data envelopment analysis.
In the first stage, carrying out a comprehensive review of the research literature, indicators of service productivity evaluation were identified. Then, the evaluation indicators were weighed using the fuzzy best-worst method. After weighing the indicators and determining the importance of each of them, using the data envelopment analysis approach, the efficiency, effectiveness, and productivity of Mazandaran Electricity Distribution Company were evaluated for five consecutive years from 2016 to 2020.
In this study, efficiency, effectiveness, and productivity were simultaneously evaluated using quantitative and qualitative indicators affecting the evaluation of the productivity of electricity distribution companies.
The obtained results would help to assess service productivity by emphasizing both quantitative and qualitative aspects of services and paying attention to the dimensions of efficiency and effectiveness simultaneously. Productivity improvement strategies were also suggested based on the achieved results of the current study.

The globalization of the economy and the improvement of data innovation has caused the supply-oriented market to alter to the demand-oriented market. Furthermore, organizations ought to prioritize the desires of clients to proceed and survive in the competitive market, which requires the presence of supply chain management. Considering the wide assortment of products and today's competitive market, choosing the correct strategies to provide the required services according to a few primary components such as price, quality level, and the warranty period is one of the most concerns of company managers and dealers. This study sought to consider the increase in the profit of each part of the chain along with providing appropriate services and keeping up product quality, extracting and applying suitable implementation strategies. It also tried to present pricing strategies in a two-echelon supply chain comprised of one manufacturer and two competing retailers, with warranty period, quality, and price-dependent demands.
The strategies presented and discussed in this research have several implications; in addition; their improvements are practical. The profit functions of the manufacturer and the retailers were maximized under centralized and decentralized approaches. Mathematical models were developed in four distinctive cases (i) two non-cooperative frameworks, (ii) a channel-cooperative framework, and (iii) a global-cooperative framework. The producer intended to maximize his benefit by setting a diverse wholesale price for each of the retailers and the length of the common warranty period. On the other hand, retailers maximized their profit by setting the selling price for consumers. This can be explained through the models related to general cooperation strategies, cooperation within the channel, and non-cooperation (Stackelberg of the leading producer and Stackelberg of the leader retailers), furthermore; the profit function of each case and its formula is presented.
For each case, the values of the main decision variables and the profit of each member of the supply chain were calculated and appeared in a numerical case. Moreover, the total profit was compared with each other through two solution methods i.e., the Mathematica computer program and the Championship Algorithm in sports leagues (LCA). The championship algorithm in sports leagues was presented as a population-based algorithm for worldwide search in continuous space and inspired by sports competitions within the real world. During this algorithm, distinctive solutions that can be given to a problem were compared and each one was improved based on its suitability, last; a solution close to the optimum was selected.
A research gap was recognized by studying the research conducted within the field of pricing, warranty, and product quality using the game theory approach. On the other hand, the models were developed by considering all decision variables at the same time as well as between a manufacturer and two competitive retailers deciding on the retail price to maximize their profit. In this regard, four models of Stackelberg manufacturer leader, Stackelberg retailer leader, participation within the channel, and overall cooperation were considered. In all models, the LCA algorithm resulted in better answers compared with Mathematica software. In this study by investigating four techniques, we realized that the best reply can be achieved through the cooperation strategy which is more profitable than the others. Finally, sensitivity analyses were also performed on various model parameters.

Among the issues that have recently received more attention from many researchers in the multi-channel supply chain stands the supply chain considering the omnichannel distribution channel. This study analyzes a pricing problem in a two-level supply chain consisting of a manufacturer and several retailers as well as traditional, electronic, and omnichannel. The omnichannel includes "Buy-Online-Pick-Up-in-Store", "Buy-Online-Deliver-to-Home", and "Order-in-Store-Deliver-to-Home" has been studied. In addition, a demand function dependent on product price and return policy, as well as delivery time for products are developed. Additionally, it is assumed that the supply chain experiences demand leakage from channels with higher prices and longer delivery periods to channels with lower prices and shorter delivery periods. Accordingly, the present study aims to investigate and evaluate a pricing model considering the omnichannel in the case of single-period single-product and also providing a solution approach with valuable performance. Furthermore, this solution method can be applied to real problems as a helpful decision-making tool. Also, investigating the impact of various factors on the number of decision variables and the profit of the entire supply chain is part of the research objectives. The problem is formulated as a non-linear mathematical programming model and coded and implemented using GAMS software. Furthermore, several numerical examples have been presented to investigate the effect of changes in some parameters on the values of decision variables, retailers' demand, and the total profit of the supply chain.The results show that the two parameters of price sensitivity of demand and return sensitivity of demand have a critical impact on supply chain decision variables. These variables include the retailer's sales price, return price, delivery time, and total supply chain profit. This means that with the increase of demand return sensitivity parameter, the selling price of retails and the return price increase, and this parameter has a critical effect on the omnichannel. This research proposes an integrated approach to evaluate the impact of pricing decisions and return policies in different retail channels in the supply chain. Therefore, the results of the present study can be used as a useful guide for the operational decisions of companies in terms of simultaneous use of pricing decisions, product return policy, and delivery time. Accordingly, online retailers can make operational decisions regarding the application of the product return policy according to the presented analysis. In addition, this study creates an excellent opportunity for retailers to review their performance and make decisions about applying this policy to their business. Consequently, considering the improvement of the consumer experience in the omnichannel, this research can be a guide for consumers to use the retail integration services of the omnichannel.

The purpose of this study is to present a new mathematical model to design a supply network by considering the strategic alliance and the relationships between the supply chain members under uncertainty. This study attempts to create a suitable decision-making environment for managers to optimize the network and make appropriate strategic decisions accordingly. Since the mathematical model of network design has computational complexity, providing a suitable solution method for the proposed model is another goal of this research.
As this paper is an applied study, a new mixed-integer linear programming model (MILP) has been presented. The robust optimization method has been used to deal with uncertainty risks such as the risk of changing sales and the return of products. In the mathematical model, strategic alliance levels and the level of risk for each of the partners are considered. The model has two objectives; minimizing the cost, and minimizing the risk of establishing a strategic alliance. The optimal location of the facilities, the selection of production methods, the capacity of facilities, the selection of colleagues, the level of strategic alliance, and their control level have been determined in the presented mathematical model. Considering the computational complexity of the mathematical model, The Benders decomposition method has been applied, and a solution for the proposed mathematical model has been developed and localized by using acceleration mechanisms.
The results show the effectiveness of strategic alliances in reducing the costs of the production and distribution network of goods. The quantification of the concepts related to the strategic alliance in the supply chain, and the establishment of a trade-off between the risk and benefits of the strategic alliance are other research findings. Considering the different levels of strategic alliance and risk for each partner, the results of the current research show that a strategic alliance reduces the cost, and this cost reduction depends on the risk level of the partners. In addition, the computational results show the efficiency of the accelerated Benders decomposition algorithm for solving mathematical models in large-scale problems. In some problems that the Gams software is not able to provide the right answer in the appropriate time, the algorithm based on benders methods provided acceptable answers in a shorter time frame.
Applying the industry data shows the effectiveness of the model in creating a decision-making environment for managers and decision-makers. Also, the results show the appropriate performance of the solution method. Therefore, the finding of this research indicates a new research viewpoint in the field of network design under strategic alliance for the production and distribution of products.

This paper seeks to find the optimal number of hubs and their location to keep the preparation time and congestion in the hubs and costs at a minimum. Also, this study considers the tardiness time for the conditions that the customer's need is not answered in the specified time, which can help to make the problem conditions more realistic. Therefore, in this study, the scheduled time and the real-time system are considered. This paper considers the tardiness and congestion time for hub optimization problems with single, multiple, and multiple direct transport allocations. The decision variables in this model determine the number of hubs, the capacity of the hubs, and their location. Congestion and tardiness also affect service time, especially if the capacity and cost of hubs are limited.
This paper uses a mathematical model to solve the hub problem of optimizing the allocation – location of single, multiple, and multiple with direct transportation. GAMS software is used to find the optimal number of hubs and locations as two objective functions are optimized. The first objective function includes transportation costs, hub setup, and tardiness costs, and the second one consists of the handling time in the hubs and the congestion inside the hubs. The sensitive analysis is investigated for the service time based on the congestion and tardiness time.
This model is tested on AP (Australian Post) data for single, multiple, and multiple with direct shipping allocation models. This study also solves the exact model for 100 nodes allocated to all three models. The hubs' capacity, congestion, and tardiness determine the number of hubs. In this paper, hubs are considered small, medium, and large. Congestion levels are also considered changeable. In addition, a comparison is made between single and multiple allocations concerning cost and capacity limitation to investigate service time. The findings indicate that a hub with limited cost and capacity needs more service time. The lexicography method is also used to convert objective functions into one function.
The more the number of hubs increases, the total costs, including the transportation and hub establishment costs will also increase. Therefore, considering the transportation costs and the establishment of the hub, it can be said that single and multiple allocations can be used in some situations. However, multiple allocations with direct transport have the lowest transportation costs because goods based on the costs are transported through the non-hub and hub nodes. In general, the results indicate that using the multiple allocation model with direct transport can reduce the total transport cost, and a single allocation has the highest transport costs. This paper is helpful for managers and business owners who first want to identify points for building their product or service warehouse. Secondly, they want to have the most optimal type of allocation for transportation from different cities.