The Power of Analytics: Harnessing Data for Process Improvement

Despite what you may think, data is pretty rad, and nobody can tell me anything different. The ability to quantify, deduce, and extrapolate information from even a few seemingly simple data points is incredible and I’m going to explain just how much of an impact they can make in your world and your manufacturing processes.

Data analytics involves examining data sets to uncover patterns, trends and insights. These insights can support data-driven decision-making. In process improvement some of the obvious things you’re looking for are workflow inefficiencies, opportunities to optimize resources and even down to things like maintenance needs on equipment or machinery. Some of these may be obvious but others aren’t so visible to the naked eye until you start digging into the actual data.

Collecting data can come from a WIDE range of sources. It can be as simple as reviewing historical job costing reports that outline hours required to manufacture common parts, but it can get as complicated as IoT (Internet of Things) sensors and tracking equipment. Most data is readily available if you just know where to look. The historical data is the simplest place to start as most companies keep extensive records of labor resources and job costs. It’s always important to confirm the quality of the data you’re reviewing but with enough data it’s easy to fact-check and pinpoint any irregularities.

Tools to analyze data come in a broad range of flavors from advanced data visualization tools and machine learning algorithms to everyone’s best friend, Excel. To be honest, if you’re just starting in the process all you need are a few formulas and some free time. The first step is to identify the key metrics you want to focus on. They can include manufacturing time, defect rates or damage caused by improper sequences or excessive material handling. In a previous article ‘Leveraging BIM for Process Improvement’ I discussed challenges we faced with damage and excessive material handling in one of our facilities and how BIM and 3D modeling helped us eliminate these risks.

Once the data has been collected and you’re ready to formulate a plan it’s relatively straightforward.

1. Prioritize issues - Rank issues based on impact and resources required to address them
2. Set goals - Establish specific, measurable, achievable, and time-bound goals for each prioritized issue.
3. Define action plans - Create detailed action plans outlining steps required to achieve the goals with realistic timelines.
4. Implement changes - Carry out the action steps as planned, maintaining focus on the objectives and timelines.
5. Review and adjust - Assess the results to determine effectiveness and use the insights gained to review and refine the process to make ongoing improvements.

Managing changes like this is key to their success and continued monitoring will help you either react quicker if things go wrong or help you improve your processes for the next time.

Data analytics has been around forever and will continue to shape businesses and the world. With recent advancements in machine learning and AI integration fields like real-time data processing and predictive analytics are going to grow exponentially. These capabilities becoming more accessible should lead to a significant boom in efficiency and productivity, reducing redundancy in the workforce and increasing overall improvement globally.

In conclusion, data analytics is fascinating and can significantly guide your decision-making process in a wide variety of ways. If you haven’t already, I encourage you to start digging into some data in your department or field and see what kind of insights you find. I’m curious to see what you come up with. Are there any unique metrics you follow specifically?