Table of Contents
A decision support system (DSS) is a computerized information arrangement that bolsters decision making in a company. It is critical for informed decision making, enhanced effectiveness in dealing with challenges, and timely solution to problems with fast-changing variables (Bhargava, Power & Sun 2007, p. 1081).
Ideally, several factors often pose decision problems while considering supply chain design and location decisions for the facility. For instance, environmental factors can lead to conditions over which an organization may not have control over. Besides, technological factors, which include the nature and accessibility of the production technologies are also crucial (Bhargava, Power& Sun 2007, p. 1082). In essence, vital factors such as the economies of scale, set cost related to production technologies, flexibility, and products demands at different locations determine the choice of the need to locate facilities, whether a few large facilities or numerous small facilities (Shibl, Lawley & Debuse 2013, p. 958). Moreover, Shibl, Lawley and Debuse (2013) postulate that the accessibility of natural resources, capital, information resources, and skilled labor equally play a significant role in the location decisions (p. 959). In the present case, the plastic manufacturing company should only consider moving to locations with ideal conditions and with availability of raw materials. Another key factor in the decision process is the commerce restrictions and prevailing conditions such as taxes, quotas, and tariffs (Ursavas 2014, p. 318). For instance, before deciding to relocate to different parts, the company should be influenced by these commerce restrictions. They should consider the tax incentives offered and favorable labor laws.
Likewise, Ursavas (2014) attributes that political factors such as availability of sound legal systems, rules of business, political stability, the transfer of earnings, and the corporate ownership are crucial to the supply chain decisions (p. 320). Also, key in the supply chain design involves decision making in terms of infrastructure, facility cost, logistics, customer preference, target market, and competition in different regions that the company is planning to locate their facilities. For the plastic manufacturing company to provide easy access to customers in the target market, they should opt to the regions with a range of service businesses such as logistics companies and banks. In essence, the location should be strategically placed to connect with customers from different regions and territories globally. Ideally, the number of locations that the company requires and the size of the facilities should depend on the target markets.
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The scope of the problem considered in the present case is the transfer of the facility to different regions, where the management should be scattered in different regions to reduce the cost of transportation and eliminate the imposition of duty charged by different sections when goods are imported from another region. However, the main decision challenge in the case at hand is how to exploit economies of scale since the proposed approach would only meet the local demands at the expense of the former (Hussain, Nazir & Baghdadi 2013, p. 28). As such, the company is also determined to decide on the appropriate size of the facility to build in each region by deciding between facilities with a capacity of 5 or 10 million units. Therefore, at every discharge point, at least one out numerous possible technologies can be implemented to the desired facility transfer goal in the region.
Decision Support Tool
The decision support tool that will be adopted in this case is the liner integer programming. A web-designed decision support systems will provide the tools to managers in retrieving, displaying and analyzing data from the company’s large databases and also, to provide access to models in establishing communication as well as decision making in the distributed teams. Intrinsically, linear programming algorithms will be useful for the selection of the best decision. Consequently, every decision alternative is evaluated against each criterion and allocated score (Hussain, Nazir & Baghdadi 2013, p. 27). Revised Simplex Algorithm and Exterior primal Simplex Algorithm are the types of linear programming algorithms that would be effectively incorporated in making decisions. In the present case, the decision makers should assess the trade-offs among the numerous alternatives based on proposed facilities, quality standards and goals; which reflect on the principles of equity, uniformity and efficiency, and the operating costs in the long term.
The conventional approach as adopted in many countries depends on the selection of virtually uniform facility standards. Unfortunately, such uniform policy may not necessarily be a reasonable option for companies with tight financial resources, especially in regions with high competition for various industrial needs. In such cases, numerous trade-offs between investment in new facilities and operating cost need to be examined using the liner integer programming that provides different alternatives and consequently, opt for the ideal one.
Once all decision alternatives are scored against all criteria using the liner integer programming, the results will then be tabulated and alternatives can then be assessed in entirety. The liner integer programming method is useful for making decisions, where several alternatives need to be weighed against one another using array of criteria as is the case in the case at hand. The management should, therefore, evaluate the benefit of setting up a facility in each region to lower the transportation and duty charges against the demerits of designing the facility to specifically meet the local demand, which can potentially fail to harness economies of scale. As such, the technique will be vital in evaluating complex system and decision-making process to provide the ideal solution.
There is a risk of limitations in the subjectivity of the liner integer programming scoring system. This extends even to a situation where hard numbers are opted, where the relevance of decision making can be subjective. Besides, all criteria cannot be similar in such decisions and therefore, despite the essence of their valuation, they can also be subjective. These criteria can be improved by efficient working models, experience, and desired results. Furthermore, there is a possibility that some criteria are related and so, the scoring system may show the undue evaluation of some features in the decision that can be significantly represented in defined criteria. However, the technique is still a valuable tool for intricate decision making.
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The facility location is designated regarding the decision variables as discussed in this paper. Decisions have to be made about the place where the plant and distributions centers need to be situated, the manufacturing process to be conducted at each facility, the appropriate capacity of the facility, the market base for each facility, and the sources of supply to avail raw material at each facility (Sorenson & Drummond 2013, p. 39). Virtually, all these supply chain design decisions are interconnected. Each decision has an impact on the entire performance of the enterprise regarding cost and responsiveness in meeting the demands of the customers on time and the changes in the market base.
- Bhargava, H., Power, D., and Sun, D. (2007). Special issue of Decision Support Systems on web-based decision support. Decision Support Systems, 43(4), pp.1081-1082.
- Hussain, N., Nazir, T. and Baghdadi, H. (2013). A matrix decision support tool for the development of viable paediatric dosage forms. Archives of Disease in Childhood, 98(6), pp.e1-e1.
- Shibl, R., Lawley, M., and Debuse, J. (2013). Factors influencing decision support system acceptance. Decision Support Systems, 54(2), pp.953-961.
- Sorenson, C. and Drummond, M. (2013). Decision Making Under Uncertainty: Coverage with Evidence Development in the Context of Medical Devices. The value in Health, 16(7), p.A328.
- Ursavas, E. (2014). A decision support system for quayside operations in a container terminal. Decision Support Systems, 59, pp.312-324.