Today, all kinds of organization are constantly exposed to changes in a dynamic and moving environment. In order to survive they have to predict these changes and be prepared to respond quickly and adequately. When it comes to commercial enterprises, the following major driving forces seem to be the most influential today:
- Development of new production technology.
- A shifting competitive balance, from domestic to global competition.
- A slower expanding market.
- Changing market preferences with a demand for high quality at a low cost.
With this background it is natural that market share must be captured from competitors in a global marketplace. Success is possible if the enterprise gets the information about the changes as they occur and possesses the qualifications to process it adequately. The competitive edge belongs to those enterprises which can rationalize and use the new technology in planning, operating, directing and control of available resources. Use of decision support systems of various kinds has thus been one of the critical success factors in modern enterprises. It is here defined as ‘a computer-based information system designed to support decision makers at any level, working with semi-structured or unstructured problems’.
With respect to this definition it is important to understand that it is not a question of how to man a computer system. Instead it is a question of how to equip a small group of decision makers with individually adapted computer tools. The benefits of implementing such a system have been investigated by several researchers (e.g. Kroenke 1989). These are:
- Increase in the number of alternatives examined
- Better understanding of the business
- Fast response to unexpected situations
- Ability to carry out ad hoc analysis
- New insights and learning
- Improved communication
- Improved control
- Cost savings
- Better decisions
- More effective teamwork
- Time savings
- Making better use of data resources
Some typical tasks facilitated by a decision support system have been pointed out by Bidgoli (1989) and are:
- What if analysis: The effect of a change in one variable can easily be measured in relation to others. If labour cost increases by 4 percent, what is going to happen to the final cost of a unit? If the advertising budget increases by 2 percent, what is the impact on total sales?
- Goal-seeking: Goal-seeking is the reverse of what if How much must be sold of a particular unit in order to generate an increase in profit of 5 percent?
- Sensitivity analysis: Using sensitivity analysis will enable the detection of the most influential and critical variables of a calculation. What is the maximum price you can pay for raw material and still make a profit? How much over-time can you pay and still be cost- effective?
- Exception analysis: This calculation monitors the performance of the variables that are outside of a predefined range. It highlights the region that generated the highest total sales or the production centre that spent more than the predefined budget.
- Trend analysis: Before making a prognosis the past has to be studied. Useful building blocks of the past are time-series which are computer processed and current long-term development or trends. These, in turn, are extrapolated into predictions of the future.
- Revenue generation: Can be used to assess sales strategies, effectiveness of the sales force, allocation of sales personnel to territories, and appropriateness of the commission plan. As for marketing strategies, effectiveness of PR and advertising can be studied. Order-processing effectiveness can be assessed by examining the time required to fill orders, the number of backorders, and so on.
- Purchasing: Can be analyzed by comparing the cost and terms of goods purchased to industrial averages. Cash flow requirements for future periods based on past experience can be estimated as well as the payment policy (when to pay invoices, whether to take discounts and so on).
- Personnel and payroll: Plans can be developed, and changes in them can be carried out. Lead time for hiring and training employees on the basis of sales plans and manufacturing can be calculated and acceleration or retardation of the plans simulated.
- Asset control: The estimation and calculation of the market value of different types of assets can be facilitated. The impact of changes in asset depreciation schedules and the evaluation of asset control effectiveness can be performed by estimating losses due to theft, accidental destruction, or bureaucratic bungling.
- Product planning and budgeting: Can be performed. The cost and schedule of developing new products can be estimated and the financial result of planned product portfolios can be forecast. Trial budgets can be developed and analyzed in order to estimate their impact upon different types of divisions, etc.
- Manufacturing planning: For example, how large production facilities need to be in order to meet the manufacturing schedule, is conveniently calculated on the decision support system. Monitoring of the manufacturing process and assessing the costs and benefits of different alternatives is also possible. Quality measurement and subsequent detection of the causes of changed quality is another possibility. Finally, the effectiveness of production scheduling can be studied and better ways of organizing the production effort simulated.
Source: Skyttner Lars (2006), General Systems Theory: Problems, Perspectives, Practice, Wspc, 2nd Edition.