Putting “Intelligence” to the test: from analysis to action
We're going to hear a lot more about using software that takes an active role in our decision making process.
While software is often used as a tool to help us analyze, it's not frequently trusted to advise us as we take the step from thought to action. But for asset-intensive organizations, exploiting the decision making power of new software applications will separate the winners from the also-rans.
Note that I use the word “trusted” here. Whether we use computers or rely solely on human intuition, we have to “trust” the decision maker. No matter how sophisticated your information collection and analysis capabilities are, you inevitably have to take a leap of faith and actually make a decision based on the conditions around you (using data from the past, present and, to the extent possible, by including predictive information about future scenarios). This leap from analysis to action is where the most value can be gained or lost. This is the point where you put your “intelligence” to the test.
Using technology to go beyond analysis and actively assist decision making is not really that foreign to us. Let's look at a very common example. Driving is something many of us do every day. It involves a lot of complexity, and there are certain skills that we acquire, certain rules that we follow, and different metrics that we use to balance our performance (e.g., journey time, fuel performance) and mitigate risk (e.g., safety, maintenance/repair). We realize that the current systems have limitations. We know that we cannot drive our car looking only at the dashboard. Likewise, we know that we cannot predict an accident by looking in the rear-view mirror. Increasingly, to achieve better performance and mitigate risks more effectively, vehicle manufacturers are using technology to move beyond analysis to provide systems that assist us with complex decision making. ABS, traction control, night vision enhancement, and collision detection are all common examples. We have learned to trust these systems as they help us make complex decisions in changing conditions.
How can we apply the same thinking to management of highly complex production operations?
A January 2007 article in Industry Week focuses on part of this challenge by noting the following limitations:
- “Most business intelligence looks in the rearview mirror.” Using business intelligence software to analyze past transactions from Enterprise Resource Planning (ERP) software is analogous to driving in reverse along the highway at high speeds.
- “While real-time dashboards for plant operators [...] have been around for a long time, the data presented to operators have little meaning to supply chain managers, customer service representatives, or the CFO. Real-time manufacturing data need to be put into many different contexts for other roles in the organization that are contributing to the optimal performance of the real-time enterprise.”
The Information Week article addresses the need to use business intelligence discipline on operations data and the requirement to support cross-functional decision making requirements, but more is required. The next step is to go from analysis to active assistance with decision making. Just like the intelligent systems used in our cars, a similar evolution will occur with operations support software. We will learn to trust software that quickly identifies good decisions from literally millions of possible scenarios. Using software to determine “good” decisions will help us achieve business goals and can be measured against key performance indicators. These solutions will assist us to determine what actions to take given rapidly changing conditions and constraints. They will eliminate guesswork, improve performance and mitigate risk. Given the high stakes involved, asset-intensive industries will put more effort into using software that transcends analysis and actually enhances our ability to take effective action.
