Future of software engineering Essay

The term â€Å"Software Engineering† implies a literal meaning of the mechanics and the engineering aspects of building and deploying a software or program. However, the topic of this paper is to justify and explain the future support that organizational goals can get in the organization’s workings. The paper will be presenting the aspects of software engineering as a tool for helping organization’s fulfilling their goals. The area selected for this paper is â€Å"Decision Support Systems† i. e. the importance and suitability of such systems will be discussed as the future of software engineering. Decision support systems (DSS) will be defined in greater detail in the accompanying sections and their advantages and disadvantages will be highlighted in the final chapter. It is important to note that DSS’s are under-used in the world in terms of quantity as well as efficiency. There are very few organizations in the world that house a fully up-and-working DSS and use it extensively for the purpose of analyzing and summarizing data. The logical details of a DSS are also laid down in this paper that can lead to the relation of such systems with our topic and justify their future uses in achieving organizational goals and objectives. The level where DSS operate is also well-differentiated and the general misconceptions held about these systems are also explained to avoid confusions and expose their real job and workings. CHAPTER 2 Decision Support Systems are those systems that give an organization the edge in making decisions and understanding data by making it meaningful in a presentable and summarized output. These systems assist in the general decision making; they do not make or propose the decisions, as is the general misconception about them. Their job is to gather data, process it in pre-defined formats, accompany related information and present in an easy-to-read and user-friendly format. These systems basically cater to the needs of the executives who do not possess enough time to read all source data and need the top view of figures or data in order to shape up decisions. Thus, DSS organize data and fulfill management needs by using modeling software and/or simulation to produce reports and ad hoc queries consuming up data and raw facts and churning out meaningful information and figures. There is no restriction on the organization level on which a DSS can be installed and similarly a DSS can cater to a variety of organizational needs and objectives from all levels within the organization (Marakas, 2002). DSS are systems that take in raw facts and figures, process them, analyze and summarize those figures providing the top view or the analysis of that entire data set. Now it should be understood that the data taken by a DSS for analyzing purpose is not the basic data contained in organizational tables and files. In fact, this data is partially processed: it is the output from a Management Reporting System (MRS). An MRS is used to generate grouped reports at the Operational level (Marakas, 1998). An example would include the hours worked by each employee during a particular month. It should be understood that there is no bias or conditional filter used in disseminating the data produced by an MRS. Hence, the DSS is fortunate enough to lay its hands on data that is predominantly raw and organized. This leads to significant time saves in terms of organizing data by understanding its relevance and scope. An MRS produced report has a narrow scope (Marakas, 1998). However, DSS reports and documents are more long-lasting and can be used for future referencing. In fact, a DSS is used to produce summaries of work over time periods defined by the management or automatically set. These reports are then archived and are useful in personal analysis of trends and performance. DSS assist management in doing Trend-Analysis, forecasting and taking decisions based on the analyzed results (Holsapple and Whinston, 1996). It should be stressed again that the primary purpose of these systems is to gather data, organize it and produce analytical views that management can use in fuelling their decisional justifications. The main idea of a DSS being installed is that it can get its data from a variety of sources and still produce one summary useful for the decision. This means that managers no longer need to look at three or four different reports and spend hours trying to make sense out of them. A single composite report created by an intelligent system such as a DSS can help save time, productivity and make decision making timely and effective. This is the main aim and function of a DSS: to provide summarized and timely data for analytical purposes grouped into well-defined areas for inference (Marakas, 2002). The reporting format of a DSS is highly flexible. It can be structured or unstructured depending on the scenario, the person being reported to and the situation where the reporting is to be carried out. Although the question about the type of report is a secondary issue, the primary concern is the type of decision that can be taken using a DSS. Here again, no restrictions or barricades on the of decision, which can be anything between structured and unstructured, including a hybrid (semi-structured). DSS possess the capability to analyze data in four distinctive ways (Marakas, 2002): 1. What-If Analysis: Changing a variable and analyzing its effects on other dependent variables in the same time or work domain. 2. Sensitivity Analysis: Keeping all variables constant, except one, and noting down its individualistic effects on the output. 3. Goal-seeking Analysis: Opposite of What-If. It is done by setting the goal and looking at what changes need to be done to reach that goal. 4. Optimization Analysis: Using constraints defined by the management, it seeks for a possible set of solutions or optimizations DSS are intelligent data processors, not data creators. Without input data, DSS cannot perform any inferential tasks. Just like a car is useless without fuel, irregardless of the model and functions, a DSS, however much efficient and strong, is useless without input data and raw facts that are impediment for the decision-making and analysis purposes. CHAPTER 3 A Decision Support System is primarily for the tactical level in an organization, nevertheless it can well adapt to the other levels. It can be even be used in a hybrid of levels gathering data from one level, analyzing it and reporting it to another level. In this way, a DSS can contribute towards organizational objectives very aptly (Thompson, 1999). The fully functional DSSs in The world are a strong reflection of the fact that a DSS can really help a company to overcome its Information Reporting problems and become a leading firm in is business on the basis of the jobs performed by a DSS enabling workforce efficiency and effectiveness. The DSS works on the principle of arranging data so that inferences can be made as quickly and as easily as possible. Imagine the future corporate world without a DSS. A weary manager leading a bored, monotonous workforce that is dilapidated with the over burdening of compiling data from every nook and corner and making it meaningful and presentable to their bosses (Thompson, 1999). A DSS allows for the generation of routine reports as easy as it is to click on the Print button. The fact that a DSS allows for repeatable, routine and scheduled reports to be produced without the interference of any person makes its usage and relevance even more pronounced. The application of a DSS transforms greatly the way in which an organization works to achieve its organizational goals. Take the example of 4 workers divided in a hierarchical manner striving for the collection of data and organizing it. After this organizing, this data is given to another 2 workers who then process it and present it to the management. With a DSS in place, the job definitions change: only 1 worker from the upper hierarchy is required to monitor formats and give commands timely. Another worker is needed to key in the data, as it is automatically organized. This reduces the job for 3 lower workers and 1 upper worker. You might say, bad. But looking on the brighter side, these 4 employees’ forces and skills can be polished on another branch: say, the marketing department (Marakas, 2002). Now that brings the organization more closely and quicker to fulfilling its short-term goals, which are just a break-down of the overall long-term goals. DSS allow for Business Process Re-engineering. This means that a DSS can be implemented for a key strategy or technical change in the methodologies and the system specifications f the current work methods and practices. This may sound too subjective to be understood in a practical corporate environment. How do several firms manage a turnaround in their sales and efficiency by keeping the same bunch of employees, the same size of plants, marketing strategies and same old buyers? The answer is Business Process Re-engineering (Marakas, 1998). This means changing the old ways or trading them with new ones that are according to the practices required by the DSS implementation. A classic example is the retailer who did not have any inventory control and alarm system and was often low in certain inventory when it was high and demand and had excess of another when its season was off. After the implementation of a DSS, it was able to act an alarm system that gave beeps when certain inventory levels receded; no this conception is false. It was actually a reporting system that could use sales data and produce individualistic item reports. It simply meant that the retailer could now generate reports on the sales of his individual items on his list and compare it with what he expected each item to spend in his store. After looking at a couple or more reports, he can, ideally, identify the general time each type of grocery took to be sold and the time periods when certain inventory was needed and what was the best time to hold up more inventories considering the future aspects. One might argue as to the effectiveness of such a DSS as described above and point out the costs involved in setting up a DSS. But, believe me, in the long run, there will be a point where the decisions made using the information churned out by the DSS will result in significant cost savings and greater sales for the retailer since the retailer will now be having a fairer idea of each type of inventory and the time it took for it to be converted into sales. It is worth noting, that there was no change of inventory, marketing, employees or shop; only the DSS was implemented and BPR was carried out that lead to the retailer creeping more steadily towards his personal goals. Competition is the key for survival in today’s world, be it any industry. Globalization has meted out a strong barrier to entry for smaller firms into the global market and the existing big fishes are also finding it hard to compete with global giants. Here, comes the need and advantage of a DSS. A DSS makes it possible for an organization to keep its maintain its grip on the market as well as blesses new entrants with the opportunity to seize the market share from big giants on the basis of the reporting system they use. What do all companies have in common? Reporting that leads to Decision making. And what is the basic job of a DSS? Information organization and Reporting. So why not combine something needy with something that can fulfill the formers needs. Common sense and simple logic make it more than evident that a DSS is best suited for the achieving of organizational goals and objectives. This logic can be derived from the fact that quicker and more effective decisions fuelled by organized information will lead to strategic edges in competition and success (Marakas, 2002). History has borne testimony to the fact that often big giants in the market look to buying up small ventures in the market owing to them posing serious threats to their future goals and survival. Now the question that lies here is: what makes these small ventures so important in the eyes of big companies in that they regard them as threats, given the difference in their sizes and market shares? It must be the technology: specifically DSS and Expert Systems. While discussing the latter is beyond the scope and requirement of this paper, I would like to reinstate the use of DSS in the meeting of organizational goals and objectives. As a final bow, I would like to re-emphasize the fact that the tried-and-tested formula of the implementation of a DSS to enhance efficiency and effectiveness in company goal achieving capabilities has never been proved wrong in any major investment and changeover. Thus, we can safely assert that a DSS is essentially a valuable contributor and facilitator towards the achievement of organizational goals and objectives in a timely and successful manner (Marakas, 1998). CHAPTER 4 In compendium, I would like to end my discussion with the futuristic advantages as well as the disadvantages a DSS holds. Generally speaking, there are more visible advantages of a DSS than disadvantages owing to their easy-to-use nature and the variety of jobs they can perform. The most important advantage of a DSS is the use of data and producing a timely report that can be used to justify and influence organizational decisions. On a futuristic outlook, time will become more and more scarcer and decisions will have to me made more quickly if they are to have any impact. If managers are left doodling over 300 files to understand a trend and then make a decision then it is highly likely that at the end of the day, the company will be losing out to businesses using DSS’s (Holsapple and Whinston, 1996). This is due to the high level of automatic dissemination and organization of data done by a DSS that enables it to cater to the format and the needs of specific informational roles and managerial positions. The flexibility of a DSS will allow its extensive future use for organizational goals. A DSS does not mean a system that only produces analytical reports and stops. There is more to it. The DSS also records the decisions made and stores results of decisions and retrieves such data for future decision making purposes. An example would be when a manager was in a problem to decide on price cuts in order to remain competitive. The manager did not cut the price, and soon enough, there was a 65% sales cut. Instantly, the management decided to cut the prices but were still only able to recover just 60% of the lost sales. Slowly, they progressed, lucky enough not to go out of business. In the future, when a similar situation persists, the DSS will show the past decision along with the outcome. It is important to note here also, that in line with our past definitions of a DSS being a decision facilitator, not a decision maker, the DSS will just provide the course of action taken previously, and will not propose the manager to take the step of cutting prices as it had lead to a worsening period for the company. The decision still lies at the hands of the manager who can again decide to retain prices owing to a difference of situation or other factors. The variety of data that a DSS can handle is commendable (Holsapple and Whinston, 1996). It can be configured to use several data sources easing down managerial work. Time, efficiency and ease of work all lead directly to a guarantee of achieving organizational goals, since if decisions are made on time, with good hindsight and information, they are bound to be successful and contribute towards standards set to be met by the organization. Futuristic advantages of a DSS include giving one company a strategic edge over another through the effective use of a DSS which enables them to gather information from wide sources and work with them quickly in order to produce meaningful results that can be used to trigger well-timed decisions. DSS makes Business Process Re-engineering (BPR) a possibility, a process where the core activities and components of an organizational work flow or department are re-designed to improve their effectiveness towards organizational goal achieving. A possible disadvantage of a DSS might be their stagnancy with newer data types and the need to define reporting formats and the types of reports it can produce. A coffee maker knows how to make coffee. Similarly, a DSS cannot be programmed to work with data types as they come. It has to be informed, which is done in the designing phase, and once its made, there is no automatic way in which it can align itself to a data type without it having been configured earlier. So there is the need for redefinitions. On the positive outlook, a DSS is a well-oiled machine that is a very important part in running the organizational motors nonchalantly and stopping errors and inefficiency becoming an impediment to organizational goals and objectives (Thompson, 1999). The future is not happening without the use of a DSS, for sure. It is imperative that DSS be taken on into the future since it is an efficient part required to keep the wheels of efficiency and effective time management ticking on. REFERENCE: 1. Brooks Jr. , F. P. (1987). No silver bullet: essence and accidents of software engineering, IEEE Computer, 20(4), pp.10-19. 2. Marakas, George M. (2002). Decision Support Systems(2nd Edition) 3. Marakas, George M. (1998). Decision Support Systems in the 21st Century. 4. Holsapple, Clyde W. and Whinston, Andrew B. (1996). Decision Support Systems: A Knowledge Based Approach. 5. Thompson, J. Barrie (1999). Here, There and Everywhere: The Future of Software Engineering Education. Twenty-Third Annual International Computer Software and Applications Conference, from http://csdl2. computer. org/persagen/DLAbsToc. jsp? resourcePath=/dl/proceedings/&toc=comp/proceedings/compsac/1999/0368/00/0368toc. xml&DOI=10. 1109/CMPSAC. 1999. 812708