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Operations and Information Management

Executive Summary of Lean Production for Competitive Advantage

The operational process plays an important role in every business. The operational process includes the process from the manufacturing of the products to that of delivery of goods and services to the customers. Forecasting approaches namely quantitative and qualitative are discussed in the report to determine the forecasting methods used by Vinmar International business. The 5 performance objective is used to evaluate the linear regression method and exponential smoothing forecasting approach. The company uses the jury of execution opinion to support the capacity and demand of the company’s operation. It is because the forecasting is based on the decisions and inputs of high-level management or experts. The application of the company is based on the industries which include packaging, agriculture, building and construction, hygiene and healthcare, rigid and automotive.

Table of Contents

Introduction.

Operation Process.

Forecasting Approaches.

Evaluation using 5 Performance Model

Evaluation.

Conclusion.

References.

Introduction to Lean Production for Competitive Advantage

This report aims to carry out the operational management of Axia plastic which is a sister branch of Vinmar Internation business. Axia plastic has a diverse range of markets which includes the UK, Poland, Switzerland, Austria, and Germany. The company has a product portfolio is three main groups namely polymer modifier, polypropylene, and polyethylene (Vinmar International 2019). The main purpose of operational management is to ensure that these business processes and activities are carried out efficiently and effectively. Operation management is therefore concerned with the internal processes of a business. However, the nature of how operations management works in an organization depends on the product and services that the organization deals with. Operation management is the center of all the changes that occur in the world of business. Some of the changes are changes in customer preference, changes in supply network caused by technologies, and also changes in the quality of the product to suit the customer. Quality control is one of the very crucial activities of operations management. It should be very effective so that continuous improvement in the quality is observed (Jia et al. 2019). This report will explain the operational process of the company by using a flow chart. It will identify how the company is using different forecasting approaches to support the capacity and demand of the company’s operation. It will also provide alternative forecasting methods to improve supporting capacity and demand alignment by using 5 performance models.

Operation Process

Axia is a sister branch of Vinmar International where products are manufactures. Vinmar is responsible for delivering the petrochemical products to different markets. It is one of the world's leading producer, known for its global marketing and largest plastic and chemical distribution company. The company carries out sales in around 110 countries and has 50 offices in 35 countries around the world (Vinmar International 2019). The operational process of the company involves various process which is demonstrated below:

Plastic Processing: The company first converts the bulk polymeric material into the finished article. This process includes first, the raw material is received which is converted in power then it is fed into machinery for processing (Vartanova and Kolomytseva 2019).

Compounding: It is the process in which the compound is manufactured by missing the polymer along with additives. First, the material is brought to a plastic state by heating it under mechanical constriction. It is then cooled and consolidated. Further, the end product of this process goes through the various process for the final product. The processes are discussed below.

Compression molding: First the power is converted into a plastic state and then the pressure force is applied to confirm the mold shape. This process is carried out to make a sheet from subsequent forming so that it can be converted into building large containers and tanks(Jia et.al. 2019).

Transfer Moulding: This process is the modified process of compression molding. First, the material is heated within the cavity and then the heated material is forced into the mold with the use of a plunger. The plunger is kept separate from the heating cavity (Jia et.al. 2019).

Injection molding: The heating cylinder is kept separate from the mold and the powder is heated in the cylinder. The powder is heated until it becomes fluid. After that, the fluid is conveyed through the barrel by a helical screw into the mold (Vartanova and Kolomytseva 2019). When the fluid gets hardened and cooled within the mold, then the mold is opened and the formed article is removed through the mechanical process with the use of tools and equipment. It is one of the important processes that take place in the plastic industry.

Extrusion: It is the process in which the plastic is softened through a machine and then the softened plastic is forced into the mold with the help of the die to take the shape of the mold. The end product of this process includes tubes or rods, which can be of any configuration. Tubes formed in this process can be used for both industrial as well as domestic purposes. This process has two major types; one is that a flat sheet is produced and is converted into useful goods with the help of different processes like vacuum forming (Yang et.al. 2020). Moreover, the second process is that the extruded tube is formed and is subjected to pressurized air so that tube with a very thin wall and big diameter can be formed. This formed tube is used for wrapping in the packaging industry.

After the formation of different types of products, the company proceeds with the marketing and distribution of products and services. The process of marketing and distribution, logistics, and financing are discussed below.

Marketing and Distribution: It is the most important process of the company as it enables the company to reach a large number of customers. The company has its market in several countries around the world where it distributes its products and services, through attracting its customers through marketing strategies. The marketing plans are developed through analyzing the objectives of petrochemical suppliers to meet sells and demands of the company. The company has a local presence in several global marketplaces with the help of which it can maintain market positioning (Vinmar International 2019). The company uses the social media platform and its official website to inform the customers about its new products and services. Moreover, the online platform enables the company to attract new customers which results in higher brand recognition and higher sales and profit. The company has experience in the plastic market and it has a large distribution channel.

Logistics: As the company has a global presence, it has a large supply chain system and it possesses a good relationship with various international companies. The company has a strong logistics management as it comes under the top ten exporters of shipping containers. The company ships its products to several countries around the world. Besides, the local presence in the global market reduces the difficulties and problems that occur in logistics management (Jia et.al. 2019). Logistic management plays an important role in delivering products to consumers safely is very important for a company to maintain customer loyalty and trust. The company uses effective and efficient practices for logistics to provide better services to customers.

Financing: Vinmar International has a good and effective financial system and it also uses new and advances technology to maintain data regularly so that it will help in smooth functioning.

The company is a newly established firm and also has a strong financial history. Therefore, the company has a good experience to manage the country, credit, and currency risk. It also offers attractive financial benefits to petrochemical producers and users which helps to maintain long term relationships with them (Vinmar International 2019).

Forecasting Approaches

The concept of forecasting is quite important in an organization because every decision that is made now requires some prediction of their impact on the future. Some of the areas that companies usually focus on are the demand for the commodity, sales, and costs. Due to the increasing intricacy of the forecasting problem, there have been several techniques that have been developed. There exist three basic categories of forecasting techniques. The three techniques are qualitative, time series analysis and projections, and casual models. These three techniques use a different approach to predict future values (Vartanova and Kolomytseva 2019). These techniques have different uses, and it is necessary to correct the right method for a specific application. Firms employ various forecasting techniques to understand the nature of their customers' demands. Forecasting entails research and development to replace old trends with new trends. The various types of forecasting are in two broad categories that are qualitative and quantitative approaches.

Under the qualitative approach, there are the Delphi method, market research, product life-cycle analogy, and expert judgment. In this method, experts are presented with a scenario and are tasked to make initial predictions based on a questionnaire to write opinions. Afterward, response analysis is carried out, and another summary is given to the experts for further predictions. This process undergoes several repetitions until a consensus is reached. The customers can be contacted via a phone call or by using forms (Singh and Sharif 2019). This technique is based on judgments and views of experts and available information on special occasions. This may or may not take into account historical trends. The data can be obtained in several ways. The first approach is by informing the salespersons to gather the sales data for different categories and periods. Secondly, it is by getting the information from different employees at different levels. The third approach is by conducting customer surveys.

The final way of obtaining such data is monitoring the sales of other related products such as complements and substitutes. Market research involves the use of questionnaires and surveys in testing market trends and customers' behaviors. There is also the product life-cycle analogy that analyzes the performance of a given product in the market. This method studies the general life-cycle of all products that firms produce (Roriz, Nunes and Sousa 2017). Lastly, expert judgment is carried out by the entire management and other technocrats. The company uses the jury of execution opinion to support the capacity and demand of the company’s operation. It is because the forecasting is based on the decisions and inputs of high-level management or experts. The application of the company is based on the industries which include packaging, agriculture, building and construction, hygiene and healthcare, rigid and automotive. The company doesn’t deal with customers directly. Moreover, the company also uses the sales force composite for forecasting. In this, the management collects the estimated data from salespersons and this data is combined and analyzed for future needs (Pakdil, Harwood and Isin 2020).

In the quantitative methods, there are the time series forecasting methods; it analyzes historical information over consecutive periods. In this method, analysts assume that they can use varied precedent data patterns to foresee future data points. In time series, there are moving averages, Box-Jenkins methods of autocorrelation, exponential smoothing, and mathematical models. Time series analysis makes use of historical data to predict future values. The most common techniques that can be used under this category are moving average, linear regression analysis, trend estimation, growth curves, and exponential smoothing (Nicholas 2018). In this broad base quantitative forecasting method, firms plot to determine the characteristics of the plotted data. The components are the trend, cyclical, irregular, and seasonal components. Smoothing methods is an example of forecasting approaches, which show a fairly stable time series of no notable cyclical, seasonal effects, and trends.

Evaluation using 5 Performance Model

The two forecasting methods that will be evaluated using 5 performance objectives are exponential something and linear regression. Before evaluating the above two forecasting methods, lets first see what these methods are.

Exponential smoothing is one of the common forecasting models used by many organizations today. The exponential model generates an accurate forecast that can be applied in automated systems. Consequently, the method can be used in large-scale forecasting. The exponential smoothing is a time series technique which is based on historical data of a forecast (Matinaro, Liu and Poesche 2019). It is mostly applicable in situations where there is reasonable continuity between the past and the future. Thus, the statistical technique is best suited for short term decision making that can easily be identified based on current and future trends. In the long term, these assumptions can potentially become difficult and unreliable because variables change significantly due to changes in technology and improved efficiency.

Linear regression is used to forecast future values of a company such as demand metrics and other variables that predict economic climate. Linear regression can be employed in a casual model with several explanatory variables (Kang et al. 2016). This model is best suited where there is no time component. For instance, it can be used to forecast when the metal will melt under different conditions by developing a line of best fit based on historical performance to predict the future outcome. The regression model can be used to forecast the business continuity and risk management program. Linear regression is a forecasting model that uses the assumption of the underlying factor when it is possible to identify some variable that influences a given variable. For instance, a regression can be used in business continuity management (BCM) to forecast future sales based on weather conditions which can enable supermarkets to predict umbrella sales in a given period. It is also a seasonality model that can allow hotels to predict how sales will increase or decrease during summer and winter (Guenther 2020). Several variables can affect the sales revenue of a company. Some of the factors are the price of the products of the company, competition, news about the company, and the amount spent on advertising, among other factors.

Evaluation

The 5 performance objectives are cost, quality, dependability, speed, and flexibility.

Cost: The company's main objective is to reduce operational costs so that it can earn more profit. The lower the costs for goods and services, the greater will be the profit. So the company needs to choose forecasting methods that are cost-effective and help the company to increase its production volume as well as the volume of profitability (Dombrowski Richter and Krenkel 2017). For example, cost performance will help in improving the quality of the products and will reduce the independence of the company on other companies if the company will have a strong financial capability. Exponential smoothing will be best suited for the company as it uses the historical data and doesn't require any initial investment in for a casting. Moreover, the exponential smoothing will be resulting in reducing the level of the inventory process. Whereas linear regression is to reduce the cost by finding the demand for the future. It will help in creating a plan for the future according to the needs of the customers, which will reduce the cost of products and services as they will be predetermined by the company.

Quality: For maintaining loyal customers and gaining the trust of new customers, the company needs to maintain the quality of the products. All operation related to quality within the company is important factors to consider as it the major reason that customer chooses the company. Quality directly relates to customer satisfaction and dissatisfaction. The quality of products results in various benefits for the company internally as well as externally. Firstly, it improves the operational processes as well as the work of redoing things. Secondly, it reduces the cost and saves expenditure costs (Chung and Mutis 2020). Exponential smoothing will improve the quality of the products as it analyzes the previous data for forecasting. Knowing the drawbacks of history will help the company to improve its operational process to deliver higher quality products and services to the customers. Whereas linear regression will be able to improve the quality of products according to the needs but if there will be new demand of the customers, this forecasting method will fail to provide the appropriate quality of a product.

Speed: It is the delay in the time between the customer's request for goods and services to that of when they receive the goods and services. From the aspect of customers, speed is very important as the customer will prefer to acquire the goods and services of the company which will provide them the required demand in the fastest time (Claypool et.al. 2019). Moreover, the speed will reduce the speed of inventories by decreasing internal throughput time. Besides, it also reduces the risks of delaying the resource commitment to consumers. Exponential smoothing will require much time than linear regression as linear regression forecasting methods plans the strategies according to the current needs of the customers whereas the exponential smoothing will use historical data.

Dependability: It is also an important aspect of the customer’s point of view. It has lots of advantages which include, firstly, it increases operational reliability resulting in time and money-saving. Secondly, increases the efficiency of the operational process (Azadeh et.al. 2017). Exponential smoothing depends on the historical data for strategizing forecasting for the company and it takes time for this process. Whereas, linear regression uses market researches for forecasting the future demands of the customers. Linear regression will be best suited for the company as per the current scenario as the behavior of the customer changes day by day.

Flexibility: Linear regression is much more flexible than the exponential smoothing as it changes according to the demand and needs of the customers. It plans and strategies forecasting in such a way that the customer can get their required services in time and of high quality. Whereas exponential smoothing lacks flexibility as its only source of review the customer needs is historical data which is not appropriate for determining future strategies (Biton et.al. 2019).

 5 Performance Objective

Linear regression

Exponential forecasting

Cost

High

Low

Quality

Low

High

Speed

Low

High

Dependability

Low

High

Flexibility

High

Low

Conclusion on Lean Production for Competitive Advantage

It can be concluded that the report has carried out the operational management of Axia plastic which has its product portfolio in three main groups namely polymer modifier, polypropylene, and polyethylene. The report has provided a detailed operational process of the company with the use of a flow chart which describes how the company delivers its goods and services to consumers, starting from manufacturing to that of delivering the product. It has also identified different forecasting approaches that are currently used to support the capacity and demand of the company’s operation. There are two types of forecasting approaches namely quantitative and qualitative, in which the company uses time series, the jury of execution operation, and sales force composite furcating methods. Besides, two alternative forecasting methods are also evaluated by using 5 performance models. The two alternative forecasting methods are exponential smoothing and linear regression. These both help the company by improving support capacity and demand alignment.

References for Lean Production for Competitive Advantage

Azadeh, A., Yazdanparast, R., Zadeh, S.A. and Zadeh, A.E. 2017. Performance optimization of integrated resilience engineering and lean production principles. Expert Systems with Applications, 84, pp.155-170.

Biton, A., Reovan, A., Bressette, B. and Systematics, C. 2019. Asset Management Guide Supplement: Asset Category Overviews & Lifecycle Management, Update [2019] (No. FTA Report No. 0138). The United States. Federal Transit Administration. Office of Research, Demonstration, and Innovation.

Chung, A. and Mutis, I. 2020. Quality Assurance and Quality Control of High-Rise Enclosure Design Using Lean Principles. Practice Periodical on Structural Design and Construction, 25(1). p.05019008.

Claypool, K., Woodfill, C., Martin, K., Johnson, C., Hoff, E., Storm, J. and Kalas, A. 2019. One system to rule them all: The story of merging two inventory management systems. Journal of Digital Media Management, 8(1), pp.6-14.

Dombrowski, U., Richter, T. and Krenkel, P. 2017. Interdependencies of Industrie 4.0 & Lean production systems: A use case analysis. Procedia Manufacturing, 11. pp.1061-1068.

Guenther, P. 2020. A Case Study of Perceptions of Asset Tracking and Inventory Management Technology in a Small Construction Company (Doctoral dissertation, Wilmington University (Delaware).

Jia, X., Zhang, Z.C., Yuan, L., Qi, S. and Tian, Y. 2019. A logistics company transportation solution research. In IOP Conference Series: Earth and Environmental Science (Vol. 295, No. 4, p. 042122). IOP Publishing.

Kang, N. et.al. 2016. A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems. International Journal of Production Research. 54(21). pp. 6333-6350.

Matinaro, V., Liu, Y. and Poesche, J. 2019. Extracting key factors for sustainable development of enterprises: A case study of SMEs in Taiwan. Journal of cleaner production, 209, pp. 1152-1169.

Nicholas, J. 2018. Lean production for competitive advantage: a comprehensive guide to lean methodologies and management practices. United States: Productivity Press.

Pakdil, F., Harwood, T. N. and Isin, F. B. 2020. Implementing Lean Principles in the Healthcare Industry: A Theoretical and Practical Overview. In Delivering Superior Health and Wellness Management with IoT and Analytics (pp. 383-413). Springer, Cham.

Roriz, C., Nunes, E. and Sousa, S. 2017. Application of lean production principles and tools for quality improvement of production processes in a carton company. Procedia Manufacturing, 11, pp.1069-1076.

Singh, A. D. and Sharif, M. 2019. Bi-directional Storage Capacity and Elevation Level Calculator for Reservoir Operation Management. American Journal of Water Resources, 7(3). pp. 121-127.

Vartanova, O. and Kolomytseva, О. 2019. Measurement of the key capabilities of the company: approaches and methods. Manufacturing and Service Operations Management, 1. pp.1-11.

Vinmar International 2019. About Us. Available at: http://www.vinmar.com/services/.

Yang, T., Wen, Y.F., Hsieh, Z.R. and Zhang, J. 2020. A lean production system design for semiconductor crystal-ingot pulling manufacturing using hybrid Taguchi method and simulation optimization. Assembly Automation.

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