مدیریت تولید و عملیات
سال:1398 | دوره:10 | شماره:2 (پیاپی 19) |تعداد مقالات:16

The upward growth of steel industry has led to an increase in demand for raw materials and the release of about 7% of global greenhouse gases (An et al., 2018; Griffin and Hammond, 2019). Blast furnace (BF) is the most essential section of a steel company (Liu et al., 2016). Costs of production in steel companies are contributive to the competitiveness of such plants (Zhang et al., 2011). Due to the shortage of domestic lump and concerns about CO2 emission, Iranian steel industry has encountered serious challenges of supplying ferrous raw materials and coke for blast furnaces, while the overproduced direct reduced iron (DRI) and the vast sources of domestic natural gas and pulverized coal have made it possible to replace coke with these sources of energy and using DRI as ferrous raw material in the blast furnaces. High differences in the price of coke with natural gas and pulverized coal along with big price gap between DRI and lump, the influence of replacing complexity on the cost of ferrous raw materials, coke, and energy consumption, BF productivity, technical constraints, and carbon dioxide emissions level are the main reasons for conducting this research. Design/methodology/approach: A non-linear optimization model, extracted from thermodynamic equations, process relations, and mass and energy balances, has been applied in this study. This model can be applied as a decision support system for purchasing and supplying coke-energy, ferrous burden materials, and examining the effect of consuming different raw materials on the CO2 emission and evaluating the production profit. Findings: Results indicated that this model can decrease CO2 emission and is highly effective in gaining company benefits. Based on the research sensitivity analysis it was found that despite the advantages of the model, as long as there are no tough restrictions on CO2 emission like in Japan and in the developed European countries, and there is subsidized domestic lump charging DRI as BF burden materials, it is not economic. As a result, it was concluded that available ferrous raw materials options for Iranian blast furnaces are only lump, sinter and pellet. Research limitations/implications: BF thermal reserve zone is assumed 1200k, which may vary from 1100 K up to 1300 K in practice; hot metal and slag temperatures are assumed fixed; chemical elements distribution is assumed fixed; and the state of gas rising from the bottom segment into the up segment of BF is ignored. Practical implications: The proposed model was implemented in MATLAB and validated using the data of Esfahan Steel Company. A comparison between the model results and the experience-based results for supplying ferrous materials blending indicated a good compromise between the model and real situation, and it leads to an increase in production benefit around 16% for ferrous raw material and 19% for energy when using the model to purchase them. Another advantage of this model is the ability of prediction of raw materials which affects production parameters. In this regard, the effect of DRI on the CO2 emission, energy consumption and the benefit were studied and validated. Originality/value: Some of the innovation aspects of this study include: i) compared to available studies, optimal decision making on the supply and replacement of raw materials and energy, together with new constraints, were analyzed; ii) applying scrap and direct reduction iron (DRI) as environmental friendly ferrous raw materials for Iranian blast furnaces became possible, which contributed to a decrease in energy consumption; iii) the coke consumption rate in a BF as a function of the blending of ferrous burden materials and other production variables was assumed to change; and iv) for the first time in this study, the simultaneous consumption of carbon-bearing materials such as pulverized coal, natural gas, oil and coke were modeled.

Purpose: One of the most important risks of organizations is technology risk. In gas companies, due to the expansion of activities, increase in the number of subscribers, and increase share of gas in energy basket, technology has a very important role in delivering appropriate service. Risk assessment in gas technology development projects is very vital. In fact, the existence of numerous risks in the gas industry is one of the main obstacles to the technology development in the country's gas industry. In other words, the implementation of plans and projects of the gas industry are highly risky due to the uncertainty of the specific elements of this industry. The purpose of this study is to provide a suitable framework for identifying and ranking the risks of gas companies using the integrative technique of FMEA and TOPSIS. The distinguished aspect of this paper compared to previous studies is the new method developed based on failure modes and effects analysis (FMEA), Shannon Entropy approach, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking technological risks of the gas company. Design/methodology/approach: In this paper, technology risks in gas distribution have been determined based on literature and expert’ s viewpoints. Then, the identified risks were sent to 33 of the company's experts via the FMEA worksheet. After rating the risks by the experts in the FMEA worksheet, instead of obtaining the Risk Priority Number (RPN) number for each risk, the risks were prioritized using the TOPSIS technique. The FMEA method considers three kinds of attributes, namely, occurrence, detection rate, and severity. Occurrence is the probability of the risk, detection rate is the ability of detecting risk, and severity is applied as severity of the effect of risk. The judgment about determination of indicators has been proposed by experts. In this paper, TOPSIS has been used instead of applying an RPN to assess potential failure modes by multiplying indicators of occurrence, detection rate, and severity. TOPSIS is a ranking method with the aim of selecting alternatives that simultaneously have the shortest and farthest distances from the positive and negative ideal solutions, respectively. Findings: Findings indicated that the most important technology risks in gas distribution are i) variation in macroeconomics index (exchange and inflation rate) in country; ii) inability to access required equipment and machinery; iii) inability to access manufacturing technologies; and iv) limited financing for technology development. Research limitations/implications: One of the limitations of this study was separate access to the projects of this industry. In this study, the gas distribution project was defined generally and included all projects in the gas distribution industry. In fact, it was not possible to individually access the gas distribution projects. Analyzing and presenting solutions for each risk separately was another limitation of this study. In other words, considering each risk separately according to the structure of the industry was another limitation of this study. Practical implications: The results were valid based on the reasonable method and experts’ confirmation and could be suitable for this industry. The technique presented in this study was based on information obtained from the Chaharmahal and Bakhtiari Province Gas Company, while due to the similar structure of provincial gas companies in gas technology and distribution, the method and results obtained in this study can be applied in all gas companies in the field of gas distribution. Social implications: The results of this study could decrease the cost of gas distribution industry by determining the most important technological risks of the gas company. Originality/value: The aim of this study was to propose a new method of FMEA for ranking technological risks of the gas company by integrating Shannon Entropy approach and TOPSIS. The contribution of this study was the investigation of the technological risks of the gas company. In addition, in this paper, a new method was applied by the integration of FMEA and TOPSIS.

In this paper, a new integrated mathematical model of the production planning and dynamic cellular manufacturing system (DCMS) wherein the product mix and/or volume is different from one period to another has been developed. So far, literature review indicates that the key role of Material Handling Equipment (MHE) has not been considered in the developed model, while ignoring such role will lead to wrong results. In other words, ignoring characteristics such as MHE capacity and inter and intra-cell movement times cannot be justifiable especially in shops in which, the movement times for parts are considerable compared to their processing times. The proposed model covers concepts such as inter/intra-cell movement, reconfiguration, subcontracting, inventory and backorder, lead time for subcontracted parts, optimal lot sizing in each period, number of inter/intracell MHE assigned to manufacturing system, number of MHE purchased and sold in each period and price of purchasing/selling for each inter/intra-cell MHE. A numerical example and sensitivity analysis have been used to verify the proposed mathematical model.

This research presents a mathematical model for information integrating manufacturers, distributors, and intermediate warehouses and transportation systems in the supply chain over multi period. Goods are spoilable and Capacity, production costs, warehousing costs and transportation of each transportation systems costs are limited. Rate of corruption of goods in the warehouse and transportation systems is known or predictable by experts. The amount of demand for goods is constant. The objective function of the proposed model is single-objective and the costs of production, transportation, warehousing, corruption, shortage and unpackaged goods are integrated minimally. The proposed model is non-linear and of a strict type and has been confirmed by solving a few small-scale problems. For validation, a case study was performed and genetic Meta heuristic algorithms were used for solving the problem. The results of the solved model showed that Decision making integrated is better than the case that sections are decided separately.

Purpose: The aim of this study is to develop a dynamic and flexible procedure for designing the spectrum of quality management maturity as well as measuring and determining the membership degree of each quality management characteristic to different levels of this spectrum. The model is essentially based on the evolution of the quality management systems and provides a basis for calculating the organizational maturity in quality management and determining its position in the maturity spectrum. Design/methodology/approach: The four levels of the spectrum of the quality management maturity have been defined and designed. Then, the fuzzy Kano questionnaire has been developed, followed by examining changes in the membership degree of each attribute to different levels of the maturity spectrum. Based on the analysis of quantitative results from the experts’ point of view, relative importance of the degree of quality management characteristics to various levels of the maturity spectrum was determined; in other words, for organizations at higher levels of the quality management maturity, tools/techniques have been considered as fundamental or functional, while for lower levels, they have been considered as motivational and attractive. Findings: The analysis of quantitative results indicated the relative membership degree of the quality management attributes to different levels of maturity and these differences varied from the motivational aspect to the questionable dimension depend on the competitive position of the organization. Practical implications: The results of this study was used to prioritize the capabilities associated with the characteristics of each maturity level in those organizations that intended to improve competitiveness while adhering to the prerequisite principle. This study also provided a basis for assessing the maturity of quality management by focusing on the deployment of such characteristics. This study also provided a basis for prioritizing and establishing the needed and relevant capabilities associated with such characteristics based on their interdependencies. In the case study, 28 well known characteristics of quality management were exploited in the competitive environment of Iran. Obviously, the proposed model was found to have the capability of applying different characteristics in higher levels of competitiveness. Social implications: According to the climatological features of quality management systems at the national level, the terms 'infancy', 'stripling', 'hobbledehoy' and 'adolescent' were also used to classify the levels in the maturity spectrum. Since deploying any of the characteristics and developing the associated capabilities is an improvement project, firms can refer to their maturity level of quality management to invest in and to deploy the quality management characteristics. Originality/value: Achieving excellence in quality is an incremental process that will often be achieved by adoption and deployment of a set of attributes the quality management (values, methodologies, and tools). Given the fact that achieving higher degrees of quality depends on increase in the adoption of products and services in response to the changing needs of customers, the maturity of quality management is also incremental and can be illustrated and visualized through a spectrum. The dynamics of the quality management systems and the complexity and ambiguity of their measurement have led to challenges in providing the scientific and executive methodologies for measuring quality management maturity, which in turn resulted in some limitations in the theoretical framework. To fill this theoretical gap, in this study, the focus on the indicators of quality improvement was changed for the purpose of investigating maturity. In other words, in order to determine the degree of organization maturity in the field of quality management, the basic focus was on applying and deploying quality management characteristics, i. e. values, techniques and tools, while the proposed methodology opened a new window for future studies.

Risk Adjusted Cumulative Sum (CUSUM) control charts are powerful statistical tools for early detection making of process changes. Unlike other industries, healthcare systems are of a wide range of variability and different levels of inputs. However, since variability in the output of healthcare process may result from different factors including environmental factors, doctor’ s performance, or patient specifications; therefore, considering multiple outcomes facilitates and increases precision of the process control. Accordingly, in this paper, risk-adjusted CUSUM control chart with multiple outcomes is applied to monitor kidney transplantation surgery. It is assumed that transplantation surgery might result in full recovery of the patient, rejection of the organ, or aftersurgery complications. Finally, the annual report of kidney transplant surgery in the U. K has been used to monitor 1779 surgeries between 2010 and 2011, and the associated CUSUM control charts have been presented.