نشریه مدیریت صنعتی
سال چهاردهم شماره 45 (تابستان 1401)

The Fourth Industrial Revolution affected all industries and transformed the digital, cyber, and real worlds in the supply chains of corporations. Internet of Things (IoT) is one of the emerging technologies that mostly manifests the fourth industrial revolution. As the future of the food industry is tied to the design and management of supply chains enabled by technologies such as the IoT, this paper is to provide a model for developing an IoT-based intelligent transportation technology roadmap based on alternative IoT scenarios in the food-producing cold supply chain.

In this study, scenarios were developed using qualitative methods such as the PESTEL framework and content analysis, based on the critical uncertainty method or GBN, through open semi-structured interviews with experts in the food industry and IoT. After identifying the best IoT technology stock for each selected scenario, a roadmap was developed using the T-plan quick-start method during a tow-day-interactive workshop.

Communication infrastructure and time horizon of technology development were recognized as the most important uncertainties for applying IoT technology in refrigerated transportation of food cold supply chain in producing companies. Finally, “Mutation Alone” and “Ascent Slow” scenarios were selected and technology roadmaps were developed for each scenario in three layers.

Food companies with cold supply chains can use one of the roadmaps presented in this paper as guidelines to equip their transport fleet with IoT technology based on their current situation and choose one of the two selected scenarios. This would enable them to plan, control, and manage the cold transportation chain process by digitally monitoring, tracing, and information sharing in an efficient and effective way.

The electric power industry is one of the vital arteries contributing to the growth and development of countries and the electricity access index is one of the main components of assessing the industrial competitiveness in each country. Given the importance of this strategic industry, this study sought to develop an analytical-mathematical model for evaluating the efficiency of the power production, transmission, and distribution companies in the electric power industry of Iran.

In this study, the network data envelopment analysis (NDEA) approach with undesirable outputs was used. Additionally, a model was developed to evaluate the efficiency of the power production, transmission, and distribution companies in the electric power industry of Iran.

By solving the mathematical model used in this study, the efficiency of 43 governmental power plants, 16 regional electricity companies, and 39 power distribution companies in Iran were evaluated. The findings demonstrated that the average efficiency of the power production, transmission, and distribution companies in the Iranian electric power industry stands at 0.83, 0.6, and 0.71, respectively.

The results of this study indicated that the efficiency of the power transmission companies in the electric power industry of Iran is lower than those of the power production and distribution companies. The study also identified the main reason for the inefficiency of the power production, transmission, and distribution companies of Iran's electricity industry.

Applying the system dynamics approach in businesses requires specialized knowledge, in particular, of defining mathematical relationships among variables. This stud seeks to make the use of this approach easier by providing a method for using linguistic variables and the fuzzy inference system in the systems dynamics approach. To evaluate the ease of use and efficiency of the presented method, this method would be used to define the relationship among variables in the purchasing department of a distribution company.

To carry out this research, a literature review was first conducted in the field of fuzzy logic and system dynamics. Next, with the cooperation of an expert from the purchasing department of the distribution company under study, some fuzzy linguistic variables as well as their rules were determined. Finally, the SD model was obtained by using the fuzzy inference system.

The proposed approach can reflect the business dynamics of the distribution company in accordance with what is happening in practice. According to the feedback model feedback and based on the modified linguistic variables, appropriate values were obtained for decision making. In order to evaluate the hybrid approach, a fuzzy inference system was used to calculate the purchase rate according to the two factors of inventory and base sales. These two factors were expressed through linguistic variables by the words "low", "medium", and "high", while the purchase price, as the output of the inference system, was expressed through the five words "very low", "low", "medium", "much", and "too much", according to the expert. After implementing the model, the presented approach (by modifying the fuzzy linguistic variables) was found capable of changing the output to achieve the desired results, as the expert confirmed.

The combined approach can be used in simulating similar cases (where human factor perception and decision-making play a significant role) and can easily reduce the complexity of the required formulas in the system dynamics approach. An important function of the hybrid approach used in this study was to model and simulate the real world in accordance with what is happening in practice.

Considering the large volume of mineral resources in Iran and the geographical extent of mines and related industries in the country, its transportation is of particular importance. Although this product is known as a rail-friendly product in terms of transportation mode, but the 39% share of the rail transportation industry is far from the capabilities and expectations of this field. Therefore, in order to increase the share of rail transportation, it is necessary to identify the factors in the attractiveness of rail transport compared to road one. Identifying the factors in mode choice of transportation is one of the most important issues that has always been of interest to researchers in this field. Several studies have been conducted on understanding the behavior of customers to choose the mode of transport. In this paper, the most important factors that affect the mode choice have been identified and the demand function for minerals rail transport is estimated.

For this purpose, at first step, the affective factors in choosing the mode of transportation have been identified with the revealed preference method through literature review and background studies. Using the multiple regression model and the SPSS software, the correlation between the dependent variable (share or demand) and independent variables have been measured and a list of variables that effect on attracting demand and have a significant presence has been identified. In the end, using the double logit model and NLOGIT software, the rail utility function has been estimated to absorb the demand for minerals. The logit model is a discrete choice model, which in this research includes a utility function with different choice options and parameters affecting them. The structure of these models is of the probability type and in it the behavior of the decision maker and his efforts to maximize the utility resulting from the choice are modeled through mathematical relationships. The "forward selection" method has been used to build the model. In this method, the independent variables are entered into the model one by one in the order of their influence on the dependent variable and are evaluated, and at each stage, according to their t test statistic sign, chi-square test statistic and model fit. The next step is to obtain the final model of the utility function. Finally, the validity of the model was measured by the in-sample method, which shows a 3% error in demand forecasting

The results show that the most important factors affecting the choice of mineral transportation method are the ratio of rail transportation tariff to road tariff per ton, the bulk of the mine load, and the accessibility of the origin and destination to the rail network. This means that with the increase in the price of the rail tariff compared to the road, the desirability of rail transportation decreases and the probability of transferring cargo to the road increases. Also, having origin and destination access to the rail network was identified as one of the favorable factors for the rail transportation. This can be included in network development policies for investing in connecting major cargo centers to the network. On the other hand, the lack of access to the network increases the cost of combined transportation, which, in addition to wasting time, causes additional time and unloading and loading operations, which will lead to a decrease in the attractiveness of rail. Load accumulation with a threshold of 200 million ton-km increases the possibility of attracting demand to the rail side. Also, by increasing the ratio of rail to road distance on a fixed route, the possibility of rail and road competition decreases.

The results of this model help the transportation service providers to focus on improving the identified factors that lead to the growth of their performance in the transportation market of this product.

Purchasing portfolio models have received a great deal of attention in both academic and practice fields as suitable purchasing strategies. Purchasing portfolio applies as a diagnostic and prescriptive purchasing tool. The core purpose of this study is to introduce a quantified portfolio for developing purchasing strategies that are aligned with competitive priorities. The quantitative method of this study relies on data mining (ordered clustering) and MADM (Best Worst method) to classify purchased items with the aim of creating a strategic fit in the supply chain and developing purchasing strategies in accordance with the competitive priorities of organizations.

In portfolio models, the determination of the dimensions and the manner in which they are measured is important. In this study, firstly, the proper dimensions for commodity classification were introduced. These dimensions were competitive priority (as introduced in the literature review, including the costs, quality, speed, flexibility, and innovation), supply market analysis (an important dimension that should be considered in commodity classification analysis), and product features (that describe the characteristics of the commodities). Next, the proper criteria for each dimension were determined using the Delphi method. After that, the selected criteria using the Delphi method were weighted using the Best Worst method. In the following, purchasing items were classified using ordered clustering based on the PROMETHEE method. In this study, the clusters determined by PSO-K-means were ranked using the total unicriterion net flow of clusters in each dimension introduced in this study. Then, the preference profile was used to measure the preferential quality of each cluster on the different criteria in each dimension. The profile helps the purchasing managers with selecting the best proposed working methods for purchasing in each class of commodity by the preference profile. Finally, the proper working method and purchasing strategy were proposed for highly strategic commodities.

The approach and method presented in this study were implemented for 100 purchased items in a steel company. In this study, the company selected cost and quality as competitive priorities. The method construct bases on these competitive priorities and proper criteria for each dimension. The most appropriate purchasing methods for each class of high-strategic purchased items were presented taking into account the proposed methods and the opinion of experts. The strategies and working methods that were introduced in purchasing Chessboard were applied in this study.

The approach of this research helps purchasing and supply managers to have more and more accurate choices of purchasing methods for each category of purchasing items. By considering the profile of preferences in the ordered clustering method based on the PROMETHEE principles it helps them to improve supply management and supply chain performance. Also, the alignment of purchasing strategy with business strategy could improve competitiveness.

Since the obstacles to executing directional contracts have not been identified by previous research, the present study seeks to examine executive obstacles to carrying them out in supply chains. The purpose of this research is to investigate, identify and prioritize implementation obstacles of bi-directional contracts in the supply chain both in production and distribution contexts.

At the end of this study, barriers to the implementation of bi-directional contracts in the supply chain were examined. Ten criteria were identified including lack of employee training (with a weight of 57.64 percent), lack of motivation and employee involvement (48.82 percent), unwillingness to change (81.3 percent), lack of corporate social responsibility (63.09 percent),  management skills and knowledge (63.18 percent), lack of less perceived benefits (22.6 percent), fear of failure (61.5 percent), unclear organizational objective responsibility (32.36 percent), lack of integration and coordination benefits (20.73 percent), and political instability (22.3 percent).

As a case study of five car companies in Iran, the current study identified barriers to the implementation of bi-directional contracts in the supply chain. Examining the criteria, the study could identify which of the companies was ready to implement such types of contracts.

In this study, the effective factors and criteria in bi-directional contracts, as well as their prioritization and degree of importance in automotive companies, were identified and analyzed in a fuzzy environment. Finally, the results were analyzed by applying other decision-making methods. It was shown that the results are not much different from each other. This means that more experienced and up-to-date companies are always in the first ranks and are always better prepared to implement contracts of various types in the supply chain.