Procurement of Goods: Decision Support System Development Proposal

Project Rationale and Summary

This project is aimed at developing a decision support system (DSS) for a newly-opened retail chain that sells such consumer electronics goods as audio equipment, digital cameras, telephones, and so forth. It operates in the area where I am living. This DSS has to facilitate procurement of goods in this organization.

While making decisions about procurement, the management has to consider the following issues:

1. the quantity of goods that has to be purchased from suppliers;
2. the frequencies of such purchases; and
3. the storage of goods.

Those people, who are responsible for this task, take into account the following types of data:

1. the current supply of stocks;
2. the level of demand for a product; and
3. historical sales.

In this case, the decision-making process consists of such parts as

1. collection of data regarding sales and stocks;
2. analysis of this information;
3. prioritization of procurement tasks, and
4. action.

This retail chain has to resolve two types of problems. On they have to reduce stock-out time. Very often their customers want to buy a certain product, yet; it is not available. This problem is familiar to many retailers and it can lead to considerable revenue losses (Blauwens, De Baere, & Van de Voorde 2008, p. 217). The second type of problem that they have to address is oversupply of some products. Overall, failure to forecast the demand is one of the reasons why they have to cope with these difficulties.

In turn, the main objective of the DSS is to optimize the supply chain and minimize the likelihood of stock-outs and oversupply. This application will enable them to keep low level of inventory. It will also help this organization reduce their procurement costs. One can identify several stakeholders participating in the decision-making process, namely; supply chain managers, who guide and monitor procurement, and sales staff who provide information about demand for a product. These people will be the ultimate users of this DSS.

The Organization

This retail chain was opened approximately a year ago. It grew out of a small shop that retails consumer electronics. To some extent, the rapid growth of this organization can be explained by their efficient service, diversity of products, reasonable prices, and after-sales services.

However, now when its structure has expanded, they face several difficulties related to procurement. Previously, this company could be regarded as the economy of scope which means that their strategy was based on product diversification (Nooteboom 2009, p. 126). At the moment, they are gradually transforming into an economy of scope which also emphasizes price leadership and reduced operational costs. Such transformation is impossible without effective functioning of the supply chain.

On the whole, the decision-making related to supply chain management and procurement can be illustrated with the help of this chart:

This chart indicates that supply strongly depends on the accuracy of information provided by sales personnel. These people provide information about the demand for a product. On the basis of these data, supply chain managers take their decisions. Certainly, they also rely on historical sales records.

We should also discuss the culture of this company. It can be characterized as a flat organization which means that their workplace hierarchy has relatively few levels (Dubrin 2011, p. 274). This structure implies that the relations between the employees are egalitarian. Furthermore, in this company every member of frontline personnel can easily contact senior management. Overall, this egalitarian culture and flat structure speeds up the exchange of information and decision-making within the company.

Finally, it is important to speak about the influence of power and politics on the decision-making process. The supply chain management in this company is affected by trade quotas imposed on foreign importers, and taxation policies of the government. The most important issue is the trade tariffs which are paid by manufacturing companies. This factor shapes the price of products. Thus, prior to purchasing these goods, supply chain managers should determine whether they will be affordable to the buyers.

Decision support system proposal

This DSS can supplement various aspects of the decision-making process. It will facilitate the collection and dissemination of data. This application will enable sales personnel to enter sales records into a database. Thus, the supply chain manager will be able to see the changes in demand and supply. Secondly, this application will be particularly useful for the analysis of this information. This tool will help the management answer several important questions:

1. What products are most likely to be out of stock?
2. What kinds of goods tend to be oversupplied?
3. When or on what days does customer’s demand reaches its highest or lowest levels?
4. What is the minimum and maximum quantity of a product that the company should always have in store?

Thus, this DDS will provide tools analyzing the demand changes. The researchers working in this area believe that in such cases, decision-support systems can rely on several methods, such as time series, trend estimation, exponential smoothing, and moving average (Ruan, Montero, & Martinez 2008, p. 74). Yet, these methods can be successfully applied on condition that the company manages to collect historical sales data.

Finally, we need to speak about prioritization of tasks. This application will link the information about demand and supply, and the managers will be able immediately see what kind of products can be out of stock in the near future. So, they will make necessary purchases. Many scholars argue that effective supply chain management is possible when one can see real-time changes in demand (Cheng & Choi 2010, p. 96). This DSS will offer this opportunity to the company.

At this point, it is difficult to determine in which area this application will be most useful. At the beginning, it will assist the management in tracking the changes in demand and supply. Yet, at later stages when this organization collects historical data, this DSS will be able to perform analytical function.

Overall, a data-driven DSS will be most suitable for the needs of this organization. According to Daniel Power, such applications are intended for the analysis of structured data (2002, p. 13). These systems enable decision-makers to cluster data and track real-time changes (Schuff 2010, p. 27). These are the tasks that our DSS to do. On the whole, data-driven DSS can be of great use when an organization has to manage large amounts of data.

Nonetheless, one should not forget that this system also has to perform some other functions. It will have to trace the relations between such variables as time and demand for the product. Therefore, this DSS will have to adopt different analytical and statistical models, and this feature is more typical of model-driven decision support systems (Power 2002, p. 13).

Moreover, this type of DSS is of great use when a person has to find the most optimal solution to a certain question (Schuff 2010, p 27). One of such tasks is to estimate the minimum quantity of a product that the company should purchase in order to avoid stock-outs. This example shows that our application will be a combination of data and model-driven DSS.

Supply chain management At this point, we need to show how various actors or entities will interact with this decision support system.

Thus, in this context diagram, we have identified the main users of this DSS. It is also important to show how data will be used by this system.

In this case, special attention should be given to the inference engine. This component of the DSS will analyze and identify the patterns of demand and supply. It will also examine historical sales records. This element will enable the management to identify the changes in customers’ demand and optimize procurement.

At this stage, we need to describe hardware and software requirements for this system. It will be based on Intranet connection. The main role will be played by DSS server that will have at least 6 gigabyte of RAM in order to operate in a real-time regime and organize data. The hard disc drive ought to have at least 500 gigabytes in order to host enclose various data structures.

Furthermore, this system can function effectively only if it is supported by a software application that allows exchange and processing of data. Moreover, it must be able to use supply chain optimization algorithms. These are the key functions that this software has to perform.

This DSS system will incorporate database management system. The data dictionary will contain the names of lists and spreadsheets stored in the server. With the help of data dictionary, the management will be able to categorize data into clusters such as: 1) names of manufacturers; or 2) types of products.

Moreover, this dictionary will help categorize sales data, for example, daily, weekly, monthly, and annual sales. The goal of data dictionary is to describe and categorize information used by the DSS (Coronel, Morris, & Rob 2009, p. 75). The data dictionary that we have proposed will improve the storage and retrieval of information.

Secondly, this system will achieve better results if it will adapt object-oriented data model. The main advantage of this approach is that it allows to describe the data in terms of attributes, classes, and associations between them (Singh 2009, p. 82). For example, such class as products will have such characteristics as quantity, serial number, or name of the manufacture. Moreover, this data class will include information about historical sales. The main advantage of object-oriented data model is its ability to draw connection between various types of data (Singh 2009, p. 82).

Successful implementation depends on the use of various analytical models. The most important role will be played by optimization model. The thing is that the main task of this DSS will to determine the optimal amount of product that has to be acquired in order to avoid oversupply or stock-outs.

This analytical model can of great use to retail chains and supermarkets (Zarate, Belaud, & Camillieri 2008, p. 81). Provided that this system makes an effective use of different optimization models, this organization will be able to minimize its costs related to the procurement of goods.

Conclusions and Recommendations

Therefore, the main purpose of this project is to improve the functioning of supply management in a growing retail chain that offers consumer electronics to the clients. We have identified two types of problems, namely, stock-outs and oversupply. This can be explained by lack of analytical tools that identify the trends in sales.

The proposed decision-support system will be able to link the information about supply and demand. This application can be characterized as a data-driven DSS; however, it will make use of optimization models and methods. The database management will be based on object-oriented data model. These are the key features of this decision support system.

This tool can bring various benefits to this company. First of all, it will speed up the exchange of information within the organization. This DSS will allow the management to better track changes in demand and procure necessary products when it is necessary. This application can enable them to avoid losses which are often associated with stock-outs and oversupply. These are the main benefits that the management can expect from it.

Reference List

Blauwens, G., De Baere, P., & Van de Voorde E. (2008). Transport Economics. Amsterdam: De Boeck Hoger.

Cheng, T.C. & Choi, T. (2010). Innovative Quick Response Programs in Logistics and Supply Chain Management. Munich: Springer.

Coronel, C., Morris, S., & Rob, P. (2009). Database systems: design, implementation, and management. NY: Cengage Learning.

Dubrin, A. (2011). Essentials of Management. NY: Cengage Learning.

Nooteboom, B. (2009). A cognitive theory of the firm: learning, governance and dynamic capabilities. London: Edward Elgar Publishing.

Power, D. (2002). Decision support systems: concepts and resources for managers. NY: Greenwood Publishing Group.

Ruan, D., Montero, J., & Martinez, L. (2008). Computational intelligence in decision and control: proceedings of the 8th International FLINS Conference, Madrid, Spain, 21-24 September 2008. NY: World Scientific.

Schuff, D. (2010). Decision Support: An Examination of the DSS Discipline. London: Springer.

Singh, S. (2009). Database Systems: Concepts, Design and Applications. Delhi: Pearson Education India.

Zarate, P., Belaud, J. & Camillieri, G. (2008). Collaborative decision making: perspectives and challenges. London: IOS Press.