Collecting data — not just any data, but the right data — can be crucial to manufacturing firms during this time of uncertainty and unpredictability.

Today's work environment is as unpredictable as ever, especially in the manufacturing world. There are just so many factors to deal with: Manufacturers not only need to manage the new workplace health precautions from COVID-19 but also need to cope with physical workplace changes, PPE, staffing uncertainty, capacity limitations and supply chain disruptions.

Thus, manufacturing data analysis becomes even more important as you try to identify new production goals and measure productivity in your new environment.

The Importance of Data

As you adjust your manufacturing process, staffing and other areas, you need a way to measure whether your assumptions were accurate and the changes were appropriate. That way, you can identify areas that can be improved through further retooling, retraining and redesigning.

In particular, focus on time data and performance data. With time data, you not only want to look at hours worked and labor costs, but you want to be looking at employee and machine time spent on direct work, indirect work and being idle. With performance data, you want to be measuring output, the scrap rate, the impact on labor standards, piece rate bonuses and the total cost of production.

With this information, you can start seeing how your changes are impacting your overall costs, profitability and timelines. That way you can set production targets, delivery timelines, employee expectations and customer commitments appropriately. You can also compare with your past performance to truly understand the financial and productivity impact of this new environment.

The New Environment: Many Changes All at Once

A recent automaker case study shows why it's so daunting for manufacturers to track all the changes in the COVID-19 environment.

This manufacturer normally houses tens of thousands of workers. Can you imagine what it took for them to reopen? Some of their major changes just for their employees included:

  • They restricted how workers should move around the plant, asking them to walk single file in designated areas with markings on the floor.
  • They installed additional hand washing stations, and imposed additional sanitization and PPE requirements.
  • They spaced cars farther apart on the assembly lines.
  • They only targeted roughly 10% capacity in Week 1 and 40% in Week 2.
  • They reduced the number of workers and hours scheduled, and also staggered shifts — having multiple shift start, break and end times — to reduce interaction and exposure.

Altogether, they made over 100 changes for employee safety. Every one of these changes could have had a major impact on production, downtime and available capacity. So how could they identify the impact of any given change so that they could potentially address issues and make improvements?

Employee Task Level Data Is Key

Tracking employee level data throughout the day is the key to finding and fixing possible inefficiencies in your manufacturing process from new adjustments. But capturing all this data manually is difficult and often impossible. Trying to do so manually would likely be very error prone. Employees would need to take time off the line to record this information, or try to remember or estimate the time and write it down after the fact.

This is where technology comes in handy, so you can track this employee data faster, automatically and more accurately. Barcode scanning of activity codes — even for activities such as idle time, hand washing and transit — makes it easier for people to switch tasks while the data is immediately input into their timecards to record stop/start times and durations. This not only helps employees track their time, but it helps you gather the detailed data needed to perform your analysis.

For analysis, the timekeeping system can upload activity and work level information into your MRP and ERP systems. Using this type of technology can help you keep up with all this crucial employee level data, even as the number of workplace changes skyrocket.

Data can be used for workplace productivity improvements, and also help you with job costing, work order status reporting, quality control and determining profitability of different project types. It could even help you determine the cost of supply chain disruptions.

Inform Your Strategy

Every business is a little different when it comes to their situation in the post-COVID-19 world. Regardless of your strategy, plans and expectations have been reset, often based on many assumptions. You may have predicted some things correctly, but may need to improve in other areas.

As you begin to work again in the new environment, maximizing manufacturing productivity and optimizing your operations may feel like starting over. History and pre-existing standards and results may not be very helpful as you move forward.

Success starts with capturing the right data in this new environment. Gather as much as you can, ideally in real time, and make it regularly available for analysis. By doing so, you can continually improve and make more informed decisions as you try to predict what productivity and good performance should look like while your ramp-up and return to work strategy evolves.

Learn how your organization can take advantage of manufacturing data analytics; launch this webcast: Innovating in manufacturing through people analytics


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