I read a good article in Modern Machine Shop today on committing to a data-driven approach to improving their production capacity and throughput, and doing that by monitoring not only the machines but also the flow of products through the plant.
This reminded me of two ideas. The first was the in the blog post from our partners and friends at SensrTrx on measuring the manufacturing process…not just the machine.
The second concept I was reminded of is the trend we’ve been seeing and that we’re trying to drive for maximizing the value from the goldmine of data buried inside the plants of manufacturing companies. I’m speaking of the progression from monitoring machines and operators for eliminating basic inefficiencies to changing how you manufacture products. It can have a massive impact on how the business runs, including production throughput, cashflow, revenue, and profit.
Manufacturers often start with Data Driven Manufacturing / IIoT solutions to monitor machines for downtime, moving to proactive maintenance, and automating production and quality data among other valuable uses. However, the value a company can get from data deep inside their operations doesn’t stop at monitoring machines and operators.
The progression goes a bit like this (with variations depending on the shop):
1) Monitor machines and operators, to get visibility to the factory floor, eliminate inefficiencies, and help operators produce more with the same effort and equipment. This helps a lot with Lean efforts and monitoring the gains from the efforts.
2) Expand and scale that solution to more machines and operators
3) Monitor the manufacturing process and use Lean to eliminate more wastes. Think about making the full process more efficient.
4) Integrate ERP and scheduling with the DDM/IIoT solution to eliminate manual data entry and data sooner.
5) Look at the larger manufacturing process and flow of products, and potentially modify how to schedule jobs. Think “8 wastes of Lean manufacturing” and working more than just improvements in cycle time at a machine. This includes looking beyond cycle time improvements on a machine and think about “load more, move faster, deliver to the next station quicker.” Add to that looking at the operations performed on the machines for the jobs or orders and start mapping routes by parts through the plant. Then modify the plant layout and flow based on the data to be more efficient.
6) Continue to monitor, measure, and improve.
Key approaches or perspectives to use now and over the long term are:
1) Strategic: start by defining the business challenge to solve and working towards what data and technology is needed, instead of trying to gather a bunch of data and then figure out what to do with it all.
2) Systems: Look at the larger picture, which includes the interactive nature of the processes, and the interdependencies.
2) Data-Driven: Use objective data from your systems to define metrics, measure progress, and share the data. Then make decisions, and most importantly take valuable action.