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How to Calculate Your Targets

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How to Calculate Your Targets

Calculating and setting targets is key to improving efficiencies and meeting demand for any manufacturer. Though, at Mingo, I’ve seen first-hand that calculating targets isn’t always easy. Most of the time, it feels like numbers are just being pulled out of thin air, and while that’s not necessarily a bad approach, there are ways to calculate targets that will provide more insight and value. 

Using experience gained from working with manufacturers of all types, I’ve pulled together a list of the most common manufacturing targets – and how to calculate them. 

This is the guide to calculating and setting targets.

Calculating Cycle Time 

It’s not uncommon to track cycle time via stopwatch and paper, but this is a manual, antiquated process that often falls on the shoulders of an engineer, taking valuable brain power and time away from more important tasks that speak to that person’s skill set. 

The answer? Automatic cycle time calculations. Using a production monitoring system, cycle time is automatically calculated with data from the machines on the floor. This system collects the data over time and populates an average cycle time, with no manual data collection required. Cycle time can then be used for scheduling, purchasing, and production costing, resulting in improved productivity and efficiency.

And, the engineer who was tasked with manually tracking cycle time via stopwatch and paper? He or she now has the resources to focus on more important tasks. 

Calculating OEE

Most manufacturers use Overall Equipment Effectiveness (OEE) to measure the baseline of plant performance, and if they have no idea where they are currently or what they should be targeting, the rule of thumb is to set a goal of 75-80% for performance, availability, and quality. When the baseline targets are met, it will result in a 60% overall OEE target. These numbers are achievable goals for most companies. 

As I mentioned, I don’t always recommend pulling a number out of thin air, but these metrics follow best practices. In a pinch, they will provide a baseline measurement to be used while efforts are made to collect data and determine the goals that are tailored to the company.

To set targets tailored to the company, manufacturers can track what the actual OEE is, in addition to the individual performance, availability, and quality metrics, for 2-4 weeks. After that period of time, data is reviewed, and based on the average and peak, without uncontrollable variables, a mid-point metric can be identified for goal setting. For example, if the average availability is 80% and the peak is 100%, the target goal is 90%. 

It’s important to note that goals change and evolve over time. Performance, availability, quality, and OEE should be constantly assessed via production monitoring software to determine how targets change and evolve to continue improving overall performance and efficiency. Then, iterate, iterate, iterate, and iterate more. The improvement process never truly ends.

Alternatively, There’s Another Way

The methods above are tried and true, but there is an alternative approach to calculating key targets that I’d be amiss not to mention. 

If cycle times were known, and the manufacturer had a relatively good handle on the rate at which machines ran, it’s possible to back into availability, performance, and quality, and subsequently OEE, metrics to identify targets specific to the company.

For example, if a manufacturer has the schedule planned accordingly, a machine should run throughout the entire shift, with the exception of breaks and lunch. A certain number of minutes are also allocated for small issues that could arise. If this all rings true, it’s possible to back into the targets without starting with a baseline best practice or requiring a learning period.

The availability goal is probably the easiest to do this way because most manufacturers know how often a machine should be running and what amount of downtime is acceptable. Calculating availability is done by estimating the expected machine run time divided by the run time needed to hit demand. Think of it this way, if there isn’t a certain amount of machine availability, there’s no way takt time can be met, resulting in missed demand. Availability, in this case, is based on what’s needed.

Quality is also a relatively easy metric to back into because it’s self-explanatory – 100% is always the goal. Quality products equal happy customers.

It’s also possible to back into performance. By taking what was needed to produce every day to meet demand, manufacturers can look at historical data to figure out what is actually being produced. Then, determine how many parts can be produced based on the average, identify the gap, and figure out the targets.

The main problem with this method is that any of these calculations can be customized and based on demand, but that in lies the problem – processes, calculations, and metrics become very custom. 

While customization isn’t necessarily a bad thing, it becomes hard to explain every possible situation. Processes, calculations, and metrics aren’t as predictable. In any situation, whether it’s building processes or determining metrics, simplicity is always preferable. The simpler a process, the more sustainable it will be to maintain and control over the long term. If it’s possible to avoid becoming convoluted in metrics, the easier it’s going to be over the years.

What’s the Right Approach?

In summary, to calculate targets, collect data over a 2-4 week period. Look at the average and the peaks to calculate the midpoint. That’s the goal to be tracked again. 

Do you have a different approach? A tried and true way of calculating and setting targets? Drop me a note – I’d love to learn more. 

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Bryan Sapot
Bryan Sapot
Bryan Sapot is a lifelong entrepreneur, speaker, CEO, and founder of Mingo. With more than 24 years of experience in manufacturing technology, Bryan is known for his deep manufacturing industry insights. Throughout his career, he’s built products and started companies that leveraged technology to solve problems to make the lives of manufacturers easier. Follow Bryan on LinkedIn here.