Operation Management in the Hot Rolling Production Line - Report Example

Operation Management in the Hot Rolling Production Line Insert your Names Here Insert your Code here Date of Submission here Table of Contents i.Abstract 2 1.0.Introduction 3 2.0.Literature Review 8 2.1.The Scope of Operation Management and Hot Production Lines 8 2.2.Roles of Operation Managers in Hot Rolling Production 12 2.3.Multivariate Processes of Hot Rolling 13 References 16 i. Abstract Operation management can change or address various short and long term production problems in the hot rolling production line. However, there is need for evidence based researches that provide production setup times/costs so as to understand connectedness between hot rolling production and operation management. Hot rolling production line is both capital and energy intensive unit. It therefore calls for the transformation of a wide range of inputs into desired output. However, to have the most realistic level of production, there is need to integrate different levels of operation management that transforms and combines different used in the hot rolling sub-systems into value added services in a controlled way. Despite this position, there is paucity of information and evidence-based researches published that provide connectedness between operation management and hot rolling production line. The aim of this research is to critically carry literature review on operation management in the hot rolling production line. From the one hand, this research strategy reviews available literature on operation management in the hot rolling production line, taking case study from different simulation, experimental design, statistical analysis and thematic designs from previous researches. It extends the current operation management literature in hot rolling production line from a single stage focus to the role of operation managers in achieving multi-stage processes of production. 1.0. Introduction Operation management theorists holds that understanding operations of different tasks rest on how one is quick to make decisions that avert challenges inherent in the process of production. Specifically, Kaushik et al. (2017) while simulating on Automation Impact on Indian Steel Industry found that operation decision making processes rests upon organized principles of managers’ knowledge who can collect empirical data and analyse them in a way that repeatable results can be obtained. However, the scope of operation management, specifically on hot rolling production line stretches beyond research findings from Kaushik et al. (2017). Hot rolling production requires design of operation systems which again, entails planning for different inputs, conversion of multiple tasks and outputs of operations (Jayaraman and Jayaraman 2016). What Jayaraman and Jayaraman (2016) suggest is that operation management requires managers to have an understanding on the capacity of their operations and an understanding on the responsibility of operation management and scope of experimental design. This view has been supported by recent studies from scholars such as Kaushik et al. (2017) who defined hot rolling production operation management as a process of planning and controlling capacity so that planned processes and controlled process can match the level of operations. We stretch our understanding of the various conceptualization of operation management so that the whole process is not looked at in terms of planning and controlling capacity but roles of operation management in the hot rolling production line that keep in mind future expansion or growth of hot rolling production line. The review of literature as will be conducted in the subsequent section deals with hot rolling production line forecasting, simulation, experimental design, statistical analysis and assessment of market trends in the realization of hot rolling production line success. As steel industry has gone steps higher, so has been the need for a proper mechanism that will ensure that there is management of all operations at the industry. Previously, operations managers were concerned with mathematical programming approaches that could enhance production and task scheduling at the hot rolling production line (Tan et al. 2016). However, recent studies have investigated the connectedness between mathematical programming, task structure at the hot rolling production line and order batching policies. Conclusions made from these studies is that currently, hot rolling production line requires that operation managers see the hot rolling production line as a place where co-ordination of multi-stage operations take place. This research further observes that in as much as experimental design and statistical analysis have been developed to improve the approaches operation managers, reduction of cost production of single stage in the hot rolling production line is required. The aim of this study is to investigate literature that deals with emerging issues in operation management so that there is shift in focus that can help this research develop integrated production schedules that meet current challenges in the hot rolling production line. The marketplace remains complex with retaining structure that is always evolving with customers seeking greatest varieties becoming dynamic. The need for evaluation of the manner in which a business conducts its operations in such a dynamic and vibrant environment is thus essential for achieving profitable and sustainable development. For the delivery of long-term sustained business results, we assess how hot rolling production line can be streamlined through operation management so that it can implement a new plan of action. Basing on Su et al. (2017) that investigated on integrated batch planning optimization based on fuzzy genetic and constraint satisfaction for steel production, operation management requires inputs that help hot rolling production to move from its functional model to a product design model, which enables alignments of operations to align with current demands in the market. These views help this study to narrow its objective in literature search. We realise that operation management needs to assess aspects such as product design, operations strategy, process selection, simulation, experimental design, statistical analysis, capacity planning and facility lay out. This is to mean that roles of operation management should be concerned with establishing a dominant route to the market system and an effective inclusion of the customers making sure that the firm’s operation in the marketplace is not paralleled. Meanwhile, the research is looking to evaluate hot rolling production line as a process that optimizes the utility of rethinking internal processes and systems (Adeyemi et al. 2010). On the basis of understanding the relationship between modern practices of operation management and hot rolling production we stretch our literature to cover different aspects of theories which include classic rolling-theory (Changqing et al. 2011). This research developed the question, ‘what are the research on rolling force model in hot-rolling process of aluminum alloys’ to show how the theory provides ground that helps understand roles of operation managers in a rolling force model and factors that affect rolling force. This study aims to make two major contributions to the general understanding of operations management within the realm of hot rolling production lines. First, the literature review in the next section seeks to provide general understanding between operation management and production scheduling for integrated hot rolling production process. To underscore this aim the data and literature search will be based on unbiased analysis that focuses on current operations to understand themes that these studies used to underscore their simulation, experimental design, and statistical analysis. Since operation management within the context of hot rolling production is still in need of further study (Changqing et al. 2011), our literature search include both primary and secondary data and researches so as to capture the dimensionality of hot rolling production line. Secondly, we realize that in the past few years, operation management in hot rolling production line has raised a number of concerns including the argument that small to medium size operations management problems could not be solved within a practical timeframe. As a result, effective solutions to this problem often required operation managers to develop heuristic methods or in some cases, high performance computing systems that detect problems with hot rolling production lines. As such, literatures to be reviewed will seek to understand the extent to which heuristic approaches of operation management can represent an essential and viable solution approach towards realization of effective process of hot rolling production lines. This research makes essential contribution to the general understanding of operation management in hot rolling production lines. First, the literature reviews will help in understanding how operation management in hot rolling production lines further provides efficiency on scheduling. As literatures have already shown, efficiency on scheduling is still in need of further research. 2.0. Literature Review 2.1. The Scope of Operation Management and Hot Production Lines Definition of operation management has been given multifaceted approaches. The multidisciplinary approaches in the definition of operation management have been necessitated by recent development in technologies that assist hot rolling production lines. Miltenburg (2009) while researching on ‘setting manufacturing strategy for a company's international manufacturing network’ noted that there have been development in software and technologies related to operation management that the role of operation managers as far as hot rolling production is concerned should be assessed in terms of their influence on the continuous caster and the hot rolling mill. The research used the question, ‘how to set manufacturing strategy for a company's international manufacturing network’?” to evaluate the connectedness between operation management and success in hot rolling production. Taking case studies from steel industries such as Mittal (India), Arcelor (Luxembourg), and Dofasco (Canada) the primary data used in Miltenburg (2009) defines role of operation managers differently. From the one hand, operation managers need to understand that manufacturing strategy in hot rolling line is a process the move the whole line it is currently to where it aligns with other functions of the industry. While studies such as Zhao et al. (2017) noted that determining the best operation management decisions in not easy, Miltenburg (2009) used a case study from Arcelor to indicate that in as much as operation management will likely face constraints, the limitation of the research is failure to recognizes that there is need for the development of a model or framework that will help operation managers identify the objects that entail manufacturing methods and plan. Unlike previous studies that defined operation management in terms of input managers have to the general success of steel aluminum making, in their book, Prediction and Optimal Scheduling of Byproduct Gases in Steel Mill Zhao et al. (2017) provide the link between different models thus bringing new challenges that operation managers face as far as planning and scheduling is concerned. For instance, Zhao et al. (2017) take case studies of different industries to compare and analyse the production processes as well as production management problems operation managers’ encounter in scheduling hot rolling production. While Zhao et al. (2017) methodological analyses has been supported in experimental designs, it fails to establish a framework that will help operation managers to separate when then can use traditional cold charge processes and technology oriented planning and scheduling of hot rolling mill. Other studies have shifted focus and now see operation management differently. Taking case study from Tang and Liu ( 2007), operation management need to be understood as a management tool that incorporates aspects of hot rolling production with generic algorithm and modeling solution when implementing steel or aluminum rolling schedules. Researching on ‘a mathematical programming model and solution for scheduling production orders in Shanghai Baoshan Iron and Steel Complex’, Tang and Liu (2007) developed operation management framework and statistical analysis to guide managers working with different departments including hot rolling production. Figure 1: Operations System for Decision Support System The figure above encloses operation management in hot rolling mill as a measurement of different functions. It therefore means that operation managers are tasked with the responsibility of planning, controlling, organising and coordinating different functions of hot rolling mill. Tang and Liu (2007) provides different perspective or definition of operation management since it looks at hot rolling mill line as a department that requires planned course of action that guide future decisions. The limitation of the research is that it only draws on the roles of operation managers to define the objectives for the operations and to improve general performance of other subsystems. Other studies have contradicted scopes of actions that define operation managements when dealing with hot rolling mill line. Papaefthymiou et al. (2016) researched on typical defects in plate and long steel. The study provided simulation and experimental design as a model that underlie procedures for achieving objectives in hot rolling mill. According to the authors, task structure as shown in figure 1 above does not include clarifications on the role as well as focus of operations in hot rolling mill line’s overall strategy. In as much as Tang and Liu (2007) definition of operation management provides detailed analyses of elements of hot rolling mill, the figure on operations system for decision support system fails to capture simulation and experimental design (Voß and Witt 2007; Papaefthymiou et al. 2016). Voß and Witt (2007) thesis concentrated on ‘scheduling as a multi-mode multi-project scheduling problem with batching requirements.’ The research noted that Tang and Liu (2007) adoption of task structure as captured in figure `1 above failed to incorporate the aggregate planning model that would be used by operation managers in the examination of hot rolling production. What Voß and Witt (2007) argue about is that hot rolling production requires a tool that assess how best operation managers can use existing situation in short term as well as long term basis. This view was supported by Nishi et al. (2007) on their research that concentrated on a distributed decision making system for integrated optimization of production scheduling and distribution for aluminum production line. These studies agree that the overall role of operation managers is to minimize the line’s weighted tardiness. Figure 2 below presents aggregate planning model analyses conducted by Voß and Witt (2007) on mechanisms operation managers can use to minimize the tardiness. According to the figure, essential factor to consider is to adopt a mathematical model which is based on well-known hot rolling production resource constrained task scheduling. Figure 2: Aggregate Planning Model 2.2. Roles of Operation Managers in Hot Rolling Production For early detection of quality issues or anomaly and expedition of processes of manufacturing, researches have sought to understand roles placed by operation managers in hot rolling production line. Vinodh et al. (2010) research sees the role of operation managers in term of lean tool. That is, they see operation managers as individuals with the responsibility of developing series of manufacturing processes that are in tandem with specific needs of hot rolling production line. Researching on ‘application of value stream mapping in an Indian camshaft manufacturing organisation’ the authors developed model of error detection that proposed the connectedness between cold rolling lines, hot rolling line and heat treatment. The research further provided a model that helped operation managers to develop high quality processes according to the demand in the market. There are two proposals that Vinodh et al. (2010) bring to the general understanding of the roles of operation managers in hot rolling production line. First, it combines framework analysed by Berezin et al. (2016) and Ibrahim and Elnady (2016) to conclude that operation managers have the responsibility of ascertaining the extent to which their processes are based on multivariate statistical process control systems. Secondly, the author based their research question on issues such as lean strategy on operation performance to show that operation managers have the responsibility of using different techniques and monitoring systems for continued processes of hot rolling. While this research proposes for the improved roles of operation managers, contemporary studies have noted that such roles can only be advanced if there are strategies put to manager operation conditions, especially on multivariate processes of hot rolling. 2.3. Multivariate Processes of Hot Rolling Researches on multivariate process of hot rolling link operation managers with the principal component analysis (PAC) and how PAC can help them interpret and analyse multidimensional data regarding hot rolling processes. Modelling hot rolling manufacturing process using soft computing techniques is one area that studies such as Faris et al. (2013) has focused on in understanding multivariate process of hot rolling. According to the research operation managers have the responsibility of carrying analysis of the effects of different variables on the final properties of strips in hot rolling mill. While Faris et al. (2013) used unscrambler software to identify and analyse hidden variables, the study failed to consider the fact that not all operation managers working with hot rolling mill may knowledgeable with the software. Different research findings have since developed that have improved proposal made by Faris et al. (2013). According to Ibrahim and Elnady (2016) multivariate processes of hot rolling is part of operation managers’ role as it seeks to improve processes of selecting materials so that the entire processes will be able to reduce the occurrence of defects in the hot rolling. Researching on diameter control of copper rod in hot rolling processes Ibrahim and Elnady (2016) argue that multivariate processes of hot rolling help operation managers to improve the adjustment of different process including the set points that are performed in every section or pass of the hot rolling processes. Analyzing this research, we understand that hot rolled strips that can enter the cold rolling mill are often made of different strips manufactured by different companies. Therefore, it is not clear how multivariate processes of hot rolling suggest that operation managers will be able to use multivariate processes of hot rolling to achieve current practices in operations including automation of the manufacturing process and specifically, hot rolling. This analysis is supported in the research that investigated ‘application of parabolic velocity field for the deformation analysis in hot tandem rolling’ (Peng et al. 2016; Zhang 2011). For over years, studies have acknowledged that multivariate processes of hot rolling is about performing symbolic regression models to the known empirical models so that operation managers will be well placed to know the best way they can handle different needs (Peng et al. 2016; Faris et al. 2013; Wu et al. 2016). However, Peng et al. (2016) took a different perspective arguing that there is need for improved mathematical functions that will help operation managers to represent the dynamical link between the input and output of models studies such as Peng et al. (2016) have put across. A comparison with experimental data collected from different operations in steel and iron industry in Canada, United States and Turkey indicated that success in hot rolling is easily attained when operation managers use multivariate processes to verify model performances. This finding was supported by industry based case study on hot rolling which showed that genetic programming based multivariate processes had better performance metrics when they are compared to other computing methodologies like fuzzy logic and neural networks. Lin et al. (2016) improved on Peng et al. (2016) application of parabolic velocity field for the deformation analysis in hot tandem rolling by suggesting schematic diagram of production process that integrated multivariate processes of hot rolling. According to the figure below, multivariate processes is essential in continuous casting of hot rolling production of multi-plants and therefore the function of operation managers in such cases is to check for instances of tardiness. Figure 3: Schematic Diagram of Production Process One key issue that Lin et al. (2016) mention is that operation managers have been facing challenging tasks especially in integrated order planning (IOP) especially with multiple plants. Therefore, introduction of the figure above details how operation managers will integrate multivariate processes in understanding integrated order planning. While Utsunomiya et al. (2014) discussed significance of integrated order planning when they researched on formation mechanism of surface scale defects in hot rolling process; Lin et al. (2016) research succinctly provides solution to project managers because it suggest among other issues, the rolling temperature and the scale thickness before rolling. References Berezin, A.A., Leonova, S.I. and Vakula, I.A., 2016. STRUCTURAL AND EXTREMAL PROPERTIES OF THE HOT ROLLING BATCHES PRECEDENCE GRAPH. Ural Mathematical Journal, 2(1), pp.9-16. Changqing, H., Hua, D., Jie, C., Xinghua, H.U. and Shuangcheng, Y., 2011. Research on rolling force model in hot-rolling process of aluminum alloys. Procedia Engineering, 16, pp.745-754. Faris, H., Sheta, A. and Öznergiz, E., 2013. 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