By Christian Green, Kepner-Tregoe
Starved for information but drowning in data?
Manufacturers have dramatically improved the way data is gathered, analyzed and processed. But too often, beyond standard monitoring and reporting, the abundance of operational and business data becomes overwhelming and fails to improve business results. Increasingly organizations are struggling to effectively identify and use system data to resolve issues and maximize profitability. This struggle is exacerbated by the increased complexity of problems created by the variety of systems, platforms, locations, suppliers and other factors that characterize modern manufacturing.
Identifying and using the right data is at the heart of effective problem solving and decision making. To stay afloat in the vast ocean of available data that can support complex problem solving, consider these three foundational actions.
1. Get the right people in the room. A diverse problem-solving team understands how to access and use information within their areas of expertise. For example, by including someone from quality, production and maintenance in resolving a quality issue vastly expands the expertise and knowledge in the room and can accelerate access to the information needed to solve a problem. By connecting to disparate sources of data, you gain access to information that has been siloed in various systems or departments. With everyone focused on resolving an issue, a diversity of perspectives and information can eliminate roadblocks, build consensus and drive problem solving ahead by focusing on relevant information.
2. Need more data? Don’t stop there. Eventually problem solvers reach a point when the information needed is not at hand and may or may not exist. Don’t stop there. Within all relevant sources of data, there may be raw data and analytics in need of compilation or there is someone outside the problem-solving team who has access to answers needed. You may be able to continue pursuing issue resolution, but before you do, delegate. Rather than accepting that the data is not available, assign a team member to follow up and gain access to the information and involve more resources. In one of our client organizations, the need to take action and not accept “need more data” as a dead end was identified as a significant barrier in their troubleshooting and was singled out as a key priority for improving problem solving.
3. Understand your problem before seeking a solution. A problem is a problem when the solution is not evident. It must be defined with specificity in order to consider solutions efficiently. A failure mode, such as “corrosion” is not enough to define a problem. The more specific the problem specification, the easier it is to find the data that leads to resolution. One simple method to help define a problem is to use the Five Whys (describe the situation and then ask and answer Why? until the answer is I don’t know.) For simple problems asking a few Whys? can quickly lead to cause. For complex problems the Five Whys helps define the real issue at hand by peeling away symptoms to get to the heart of the problem. When the answer to Why is I don’t know, you have identified the problem.
Developed by Toyota Motor Company, here is an example of Five Whys:
"The robot stopped"
- “Why did the robot stop?” The circuit has overloaded, causing a fuse to blow.
- “Why is the circuit overloaded?” There was insufficient lubrication on the bearings, so they locked up.
- “Why was there insufficient lubrication on the bearings?” The oil pump on the robot is not circulating sufficient oil.
- “Why is the pump not circulating sufficient oil?” The pump intake is clogged with metal shavings.
- “Why is the intake clogged with metal shavings?” Because there is no filter on the pump.
Notice the evolution of the problem definition. The five whys used data (in this case observed data, not digitally monitored or complex data analysis) to specify the problem from the potentially complex issue of “the million dollar robot is broken” to “the oil pump intake in the robot is clogged with metal shavings.” Rather than jumping to a fix—such as a software fix that might get the robot moving—the problem-solving team peels away symptoms to get to a specific part of the robot, in this case the missing oil filter. Specifying the problem accelerates access to the information needed to resolve an issue.
4. Build context. In Big Data, context is the key to revealing fully nuanced, complete information. By integrating “thick data” (huge amounts of data) rather than a sampling of information, data analytics can create more accurate context and a complete picture based on the data gathered. Similarly, in problem solving, context is key to revealing nuanced and complete information. One example is Kepner-Tregoe Problem Analysis, which builds context using an IS/IS NOT matrix.
While not on the scale of Big Data, collecting data that defines the what, where, when, and extent of the problem builds increasingly “thick” data by specifying not only IS but also IS NOT information (see figure 1) and then detailing the difference and considering the cause. The more diverse and abundant the data that is collected with both IS and IS NOT, creates a more complete, nuanced context for the situation, helping problem solvers eliminate issues that might be considered potential causes while zeroing in on the relevant data that best describes the problem and supports possible solutions.
These four foundational actions can help organizations use data more effectively and solve problems with greater speed and accuracy. As the amount of data that is available grows, the ability to use it cannot fall behind. For complex problem solving, these fundamental actions for navigating the sea of data can accelerate problem solving and help yield quality solutions.
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