As the era of Industry 4.0 approaches in earnest, production managers will soon have access to more data and information than ever before. Internet of things (IoT) sensors and RFID chips throughout the production chain will offer real-time monitoring for your planned production programs, just as robust software integration will help you to better understand what’s happening at various other touchpoints on the supply chain. This is exciting, but it can also be a bit daunting. After all, what exactly are you supposed to do with all of that data?
On some level, much of the information that you collect will be used in advanced analytics processes to power smarter forecasts and process improvements. Even then, your task as a planner will be to define the parameters for success and the metrics that those parameters will be judged on. This leaves us with an important question: what metrics should production planners in the Industry 4.0 era be tracking in order to improve efficiency and optimize their processes?
Let’s start with one of the more simple KPIs you can make use of: downtime. This can quantify either machine downtime or overall production downtime, depending on the scope of your operations. Obviously, a certain amount of downtime is a fact of life, and even to be desired when it comes to things like proactive machine maintenance—but by and large you want to minimize downtime to whatever extent possible, since it represents time when no value is being added. Obviously, this is easier said than done—especially in production plants that juggle multiple different products or offer some level of customization to customers. In environments like those, a daily or weekly production schedule produced with pen and ink is unlikely to able to able to thread the needle with regards to slotting orders in the most efficient way. Plans generated by advanced analytics, on the other hand, are often better able to determine the optimal schedules given complex constraints. In this way, downtime becomes a useful proxy for how well your planning flows are managing complexity.
2. On-time Orders
This one is also fairly straightforward, but its importance is considerable. How frequently are orders ready by the time they’re supposed to be? If you’re trying to keep your focus fairly narrowly on production planning, this number could refer specifically to the rate at which orders have made their way off of the production line and are ready to be moved to warehouses or trucks by their specified date and time. Not only does this give you a handle on how well you’re keeping costs down (since on-time orders will ostensibly reduce the need for premium freight and other expensive contingencies), it also gives you a sense of how effectively your production plans are actually being carried out. If there’s any sort of operational disconnect between planning and execution, this KPI will make it apparent. Crucially, it’s also a useful test of your information transparency. If you can easily attach planned production programs to finished products, then you can measure this fairly easily. If you can’t, you have your work cut out for you in terms of cleaning up your data.
3. Planned vs. Actual Production Time
Very broadly speaking, there are two higher-level concerns that your KPIs ought to shed light on: are your absolute numbers (e.g. downtime, throughput, makespan, etc.) moving in the right direction, and are your expectations coming into better alignment with reality? This KPI and the one below are directly tied to the latter concern. Why is it important to check that your expectations are aligned with reality? Because the more closely your expectations play out on the production floor, the fewer disruptions you’re likely to encounter. To wit, if your actual production time is much longer than your planned production time, tasks will pile up in a way that makes on time delivery impossible; while a planned production time that’s much longer than your actual production time will result in increased downtime, which signals a failure to maximize efficiency within your production flows. Here, it helps to have a clear and ongoing record of your plans as conceived and as executed.
4. Planned vs. Actual Production Costs
In the same vein as the KPI above, this will give you an account of whether you’re using resources as efficiently as you believe you are. The fact that this relies on a factor other than time makes it a useful complement to planned vs. actual production time. Why? Because it ensures that you’re not improving your time-bound KPIs by burning through resources less carefully. If, for instance, you were trying to improve your actual production time by speeding up processes in a way that would raise the rate of duds and products that have to be scrapped, this KPI would help you understand whether your strategy was proving cost effective or not.
5. Overall Operational Effectiveness
Finally, we get to the KPI that nicely sums up a lot of the previous considerations. You might already be familiar with overall equipment efficiency (OEE), which measures how well your machines are performing as a factor of availability, performance, and quality; overall operational effectiveness (OOE) applies the same idea to your entire production flow. Rather than looking at an individual piece of equipment, you can take your entire production plant and examine:
- Availability: How often is the entire workflow for the production of a particular product available relative to expectations (i.e. the factory’s overall uptime)?
- Performance: How many process flows are you completing relative to expectations (i.e. actual makespan over expected makespan for a particular product)?
- Quality: What percentage of started processes yield a good unit?
Multiply these three numbers together, and you have your OOE. Conversely, you could calculate the maximum number of completed production units given your optimal uptime, performance, and quality, and then divide your actual output by this optimal number to yield a comparable calculation. In both cases, understanding the optimal uptime and makespan is a matter of collecting usable data and examining it systematically over time. In this way, digital technology makes it easier than ever to understand where your production plans are going well—and where there’s room for improvement.