Why Use Data Driven Decision Making (DDDM)?

Safety. Production. Cost Reduction. Run Time. Efficiency. Innovation. Profitability.

Each of the potential outcomes above requires adjustments in operations, equipment and analysis. Automation is the foundation and the road to the destination, and data is the vehicle that gets us there. Perhaps the question that we should be asking ourselves is why should I use data driven decision making instead of intuitive decision making?

Companies go to great lengths to find and hire smart and intelligent people with significant relevant experience – one of the prevalent thoughts is that if companies spend considerable time and effort to find experienced workers that they should let them manage and make decisions. Certainly they should, but how do they also ensure transparency, consistency and continuous growth? While many people still use intuition or “gut feel” to make decisions¹, most organizations realize that data driven decision making helps eliminate biases and false assumptions and can lead to an accurate perspective on the status of the company, in addition to being proactive and realizing cost savings.  While many times intuition provides us with a direction in which to travel, data provides the justification and forms the story around which a business case can be developed. The benefits of DDDM can depend on who is looking at the data, what data are they looking at, and what questions are being asked. For example, a COO may wish to better understand the efficacy of the corporate safety program and may wish to look at incident information – severity and frequency, mean time between incidents, near misses.  All this data can be queried, presented and visualized using corporately collected information and enterprise level software. The time frames viewed can vary from months to years and from location to location as the COO investigates data from plants within the corporate fleet. Plant Managers or production managers may wish to investigate how to increase production levels to meet targets. The answer may not necessarily lie in faster operation or more feedstock but rather in more efficient operations – machine downtime, equipment availability or bottlenecks on the production line.  The data will tell them the story and point them to the likely causes of issues.

Often, recognizing patterns in data can lead to changing processes, doing things differently, innovations.  When implemented across plants or enterprise wide, these innovations often lead to significant top line enhancements and bottom-line improvements resulting in increases in corporate profitability. Knowing where to focus efforts and direct investment dollars can often be equally as important as the investment itself. Directed investments complemented with justification based on the data can improve safety, enhance throughput, inspire innovation and drive profitability.