While a decision support system (DSS) may seem a relatively new phenomenon, it’s been here for years.
Actually, anything that helps with measurable, rational, and scientific data, and lets you make an informed decision for the existing and future growth of your business is a DSS.
However, you should also know it doesn’t give any decision itself; rather it helps decision-makers (board members, managers, executives) in an organization to make an informed decision, based on the analyzed data, records, and other details.
Some most commonly used decision support systems entail hybrid systems, manual systems, all forms of sophisticated as well as analytics decision support software.
But the matter of concern and confusion is how today’s DSSs differ from the early DSSs.
Let me tell you that today’s computerized data systems are much more advanced and capable than the earlier decision support systems. DSSs today can analyze a huge amount of data and suggest valuable recommendations to make the best decision.
So, it is the technological advancements, integration of additional features, and accelerated capabilities that separate today’s decision support systems from the prior ones.
How does a DSS help you to make an informed decision?
There are various examples of decision support systems used by organizations these days.
Where some like to use complicated computer software solutions, some take advantage of computer-created statistics when it comes to understanding the latest trends.
These comprise analytics like warranty rates, sales statistics, and cash flow trends that work as the most vital indicators when it comes to helping users in ascertaining the health of their businesses and encouraging the need for curative action.
The challenge is that data like this neither can ensure which of many will help in maximizing proceeds while gaining the expected outcome, nor it helps in anticipating external changes that could affect profitability, a crucial aspect as the majority of organizations work in an abstruse environment run and impacted by legal regulations, consumer sentiment, and extensive competition.
Besides, organizations are likely to be most affected by external impacts, for instance, uncertain weather events, major political uncertainty, and trade disputes.
At times these aspects help cause a storm where decision-making is hindered by a dearth of predictability and by an incapability to fast process the existing data to support decisions.
That said, decision support systems that have the capability to swiftly analyze data, ensure patterns, and measure multiple options have turned valueless to business leaders.
What are the principles behind DSS?
The basic principles of DSS began from theoretical work completed in the past century at the Carnegie Institute of Technology with regards to organizational decision-making.
This work ensured that though gut feel and human instinct generally brought about good decisions, there existed many examples where gut-based decisions weren’t right.
Alternatively, researchers created the model of employing executive information systems to do an analysis of organizational data and result out succinct executive data to support decision-making.
With time, and as the computer advanced, this initiative started including the utilization of advanced software that created the model of business processes, helping users to assess the results of several scenarios.
Hence, it wasn’t impossible to gauge which of many options provided the best business return.
Here are five best decision support systems for your business.
1. Decision support system that helps you in your day-to-day decision-making activities
The decision support systems function at multiple levels, and there exist several instances in common day-to-day use.
For instance, GPS route planning, by carrying out an analysis and comparison of many possible alternatives, helps you with the best and fastest route falling between two points.
A large number of GPS devices are enhanced with traffic avoidance capabilities.
Not only do they monitor traffic conditions in real-time but also keep you updated and informed of the traffic updates and thus help you avoid the congested route ahead.
Another example is the crop-planning systems that farmers use when they want to make sure which will be the perfect time to plant, fertilize and reap. On the other hand, there is a medical diagnosis program that medical personnel use to diagnose ailments.
Lots of systems like to share a common aspect in that decisions aren’t just repetitive but also based on identified data. But being infallible, they may also make irrational or incorrect decisions, something several previous GPS users found.
2. Decision support system that takes advantage of historical data
Historical data analysis, employed in every part of life and business, is mature and well-built.
While you cannot directly use such data, it is a vital part of a decision support system as it gives reports of prior performance and brings to light areas that need to be heeded.
A few instances are:
- Descriptive analytics: Metrics including inventory turnover, sales results, and revenue growth.
- Diagnostic analytics: Diagnostic data that dives a little deep to bring results and suggests reasons for previous performance as descriptive analytics measure.
- Business intelligence (BI): BI solutions help users in building and running queries that aid in guiding and supporting decision-making.
- ERP dashboards: These dashboards help managers in monitoring various performance indicators.
3. Manual and hybrid decision support system for all your decision-making activities
The majority of manual systems are immensely helpful when it comes to decision-making.
To name a few activities are the SWOT (strengths, weaknesses, opportunities, and threats) analysis, based on which teams need to ensure their organization’s strengths and weaknesses as well as discovering threats being encountered by the organization and future possibilities for further growth. The consequences of a SWOT analysis are important actions to be taken for furthering the organization ahead.
Hybrid DSS solutions mean using spreadsheet analyses that understand the ability of Excel for computation, analysis, comparison of options, and evaluation of what-if situations.
While hybrid DSS and manual solutions are rather unwieldy and slow, if used by the right team of people, they prove the strongest decision support systems, and a large number of small, medium and large-sized organizations are dependent upon them.
4. Decision support system that helps you predict future trends
Though it is vital to tap into the past and try to understand what took place in the past and why it occurred, you can hardly use this knowledge when predicting the future, but probably in very predictable and stable environments. But this hardly happens.
Luckily, some systems do help in predicting, with some certainty, potential trends, and modifications that actually affect a business or company. For instance, these systems help you with predication simply based on past performance, market feedback, external data, and facts for further product demand, product uselessness, and returns.
This is known as predictive analytics and helps in creating another form of a DSS system, one that aids in predicting what will take place in the long run. Predictive analytics take advantage of an amalgamation of statistical tools, data mining, and machine learning algorithms to make sure the possibility of specific events occurring.
What’s more? Financial sectors are using these systems to dig out fraud, while insurance companies leverage them for evaluating the possible risk and ride-hailing organizations employ them for the determination of ticket prices, as per the demand.
5. Decision support system that is free from bias and subjectivity
The ideal decision support system includes those that help with the best decision, as per some set criteria. Such programs eliminate bias and subjectivity from the decision-making activities.
That apart, they are capable of assessing many optional situations and finding out the best.
The best is to build a mathematical model of the business, find out how it’s making decisions, and leverage optimized software to ascertain the results of many scenarios.
This type of system hinges on prescriptive analytics and is very strong. Although a few do recommend that it is simply the decision-making process that needs to be built, creating a complete model of the company helps in scaling up versatility and improving accuracy in the form of financial results, and this is the goal of every company.
There exist two types of optimization approaches, optimization models and rules-based.
Rules-based models work great when it comes to predetermining the possible results, for example, evaluating insurance issues. Instead, optimized models aren’t easy to adapt, but they can also tackle complex risks, and handle multiple tradeoffs and constraints.
Looking for ideal decision support systems for your business needs?
The need for an appropriate decision support system relies on organizational maturity, complexity, and, to a certain extent, size. A hybrid system is enough for a small company.
And if your organization isn’t familiar with analytics, starting with a historical decision support system would be an ideal decision. If you are into activities such as trading and commodities, go with a predictive decision support system.
After reading the article about 5 best decision support systems you can use for your organization, we hope that you want to know more about the company that can help you in selecting and implementing the best decision support system for your organization.
If you have any questions about this software or need some experts’ advice to implement the same, we would be happy to answer them in the comment section below.