When we are thinking about training, the topic of business analytics does not always spring to mind, but it really should. Mid-size manufacturing companies know how important training is. And we take great pride in ensuring we provide the right training for our folks.

But what happens when training appears to be dragging our results down, not up, and how can business analytics help? I started thinking about this recently when a colleague told me he feared his company was training themselves down the drain.

He described his situation as a Catch-22. They had an older workforce. They were adding new machines and new systems, and those would help make the company much more efficient. They obviously needed to train folks on their new processes. But the more they trained folks, the worse they did. What was happening? And how could they manage their way out of this dynamic.

They obviously needed to train folks on their new processes. But the more they trained folks, the worse they did.

Training

There are few things more important to the long-term health of a manufacturing organization than a well-thought-out program of employee training and development.

Shortlister recently published an amazing set of statistics about companies’ plans for training in 2024 and beyond.

Among their findings was that US training expenditures reached $101.6 billion in 2022. Those investments represent a tremendous commitment to employee development. They found the average company provides 62 hours of training per employee, and that 35% of organizations are reporting a 6% to 15% increase in their training budget. These investments underscore the critical role of training in closing skills gaps, enhancing productivity, and supporting organizational growth.

The statistics indicate a growing recognition of the value of training, with most companies spending between $501 and $3,000 per employee for training and development​​.

If training is not a topic of conversations at your company, it probably should be.

Why Business Analytics?

Manufacturing operations are complex, and it is difficult to see through all the noise and activity to what is truly important and has financial impact. Good business analytics are critical to piercing this fog and empowering us to optimally manage our manufacturing operations.

The design of your metrics is especially important in an environment where management has the team focused on kpi’s, because those numbers drive behavior. For this reason, your manufacturing metrics need to be designed right. If not, those metrics, the ones you love so much, will wind up driving bad behavior!

Let’s look at some of the typical ways companies measure success on the shop floor. Shop floor efficiency is often thought of as the primary measure of direct labor productivity. Most companies also look at utilization as well. Both metrics are discussed below.

High Efficiency Does Not Come Easy. It Takes Hard Work & Persistent Effort to Achieve & Maintain Peak Performance

Efficiency

The first important shop floor metric is efficiency which describes how well the folks on the shop floor are performing their tasks. Efficiency is typically measured against a standard. Those standards are typically set by the manufacturing and engineering teams. For companies with a standard cost accounting system, the determination of standards is probably already built into your processes.

Efficiency = Standard Time for a Job divided by Actual Time Spent on a Job

In these cases, efficiency is measured as percent performance against the standard. Tasks that are done as fast as the standard are said to be completed at 100% efficiency. If something takes twice as long as it should according to the standard, efficiency will be 50%. Some ERP systems make it relatively easy to obtain metrics on efficiency. In other systems, analyses like this are not readily available and can require a little work.

With good ERP reporting, month-end reviews can easily highlight those jobs where our efficiency was far below (or above) the expected standard. This visibility allows us to investigate and correct where necessary.

In a Manufacturing Environment, Good Understanding of Your ERP System and Solid Cost Accounting are Priceless

Utilization

Another metric used in most successful manufacturing plants is utilization. Utilization describes what percentage of an employee’s time is spent on direct labor. If one of the folks on the shop floor clocks into the company at 7 AM, clocks on a job at 7AM, works the next eight hours on that job, and then clocks out of the job, and the building, then that person was 100% utilized that day. It means every minute of the day was spent directly on a job. No break, no lunch, no rest room.

Utilization = Time Spent on Production divided by Time Clocked In

100% utilization is not a realistic goal. Lunch, breaks, meetings, and any types of labor that are not included in the standard, would be considered not utilized, and contribute to a utilization number of less than 100%. I hate rules of thumb, but I hate even more people that refuse to give them. The rule of thumb on utilization is that somewhere around 75% to 80% utilization is considered fair. That is an extremely general statement. Many companies are structured in ways that yield lower utilization or higher.

For companies with utilization numbers far different from the average, I highly recommend bridging your utilization to a typical 75 or 80% utilization, and then seek to truly understand why you deviate, and whether or not that is truly OK.

Again, your ERP system should facilitate month-end reviews that can highlight those resources that have below average utilization. That then allows us to investigate and make changes or improvements where necessary.

Danger Ahead

So, on the surface these sound like two wonderful metrics. The real truth is, if you don’t know what you’re doing with these metrics, they can be dangerous. Using either of these metrics in isolation, without truly understanding the numbers, can provide a very skewed picture of what is really happening on the shop floor.

Problems with the Efficiency Metric

As discussed, the efficiency metric is used very commonly to express how efficiently folks on the floor are operating. In most organizations, it is measured as a percentage of standard. The measurement period begins when an operator clocks on to a job. The operator will perform a perform a series of operations and perhaps complete a quantity of units and then clock out.

In modern plants this is facilitated by bar-coded shop orders and scanners at operator stations. If the standard time for a build is 7 minutes, the system will take the total time for a job, divide it by the quantity completed, and calculate an actual time per unit. That time will then be compared to the standard and the level of efficiency is calculated

Business Analytics Close-Up

An Example

Let’s say we are building an Acme Medical Device, item #A27643
The standard build time is 7 minutes per unit.
The operator clocks on to the job at 1PM. The operator clocks off at 3:12.
At the end of the run, he has completed 16 units.

The Math

The actual build time was 1PM to 3:12PM = 2 hours and 12 minutes = 132 minutes.
The actual build time per unit was: 132 minutes divided by 16 units = 8.25 minutes per unit
This means the Efficiency was the standard time (7 minutes) divided by the actual time (8.25 minutes) or 85%.

Analysis

So, from an Efficiency standpoint, the operation is running at 85% efficiency, which is generally considered good.

That’s great. As we said, the efficiency metric does an excellent job of telling us how we are performing against our standard build times.

Things can start to get a little dicey though when we try to put this number to work.

So, now let’s imagine, like my colleague with the Catch-22 situation, that we have gotten some serious religion about continuous improvement and training. We have purchased new tools, we have adopted programs to ensure our folks know advanced production methods, we have brought material to the work cell. All of that.

And it seems to be working. Efficiency numbers are higher than ever. Somebody measured efficiency last year and it was averaging 75%, so, now that we are at 85%, things are great. The manufacturing team is proudly sharing their latest efficiency reports. Mission accomplished. Or is it?

What Happened?

Despite the improved efficiency, when financial results come out, they show a decrease in gross margin, not an improvement.

Impossible. Look at these efficiency numbers!

Well, what happened here is that the efficiency numbers just didn’t tell the full story. The extra hours spent on training and safety took a toll on overall performance.

In our example, when the original performance level of 7 minutes per unit was calculated, there were very few training sessions. The estimated percentage of time that folks spent on direct labor was around 85%.

This meant that folks were clocked in and on jobs 85% of the day. And we know that, when they were clocked in, they were operating at 75% efficiency.

Now our folks are in more meetings, so they spend less time clocked in on jobs, but are supposedly more efficient when they are clocked in. Are the benefits of the training worth the time spent on it? Let’s see.

In the old days, operator utilization or percentage of time clocked on to jobs was 85%, and they were operating at 75% efficiency. We can use these two numbers together to calculate a new measure called overall productivity.

In the period before our improvements, we had overall productivity = 75% x 85% = 64%.

Today, with our folks attending lots of training programs, our utilization is down to 70%, from 85%. Meaning we spent more time on non-production activities like training than we used to. But our efficiency is up to 85% because of that training.

After our improvements, we had overall productivity = 85% x 70% = 60%.

So based on a reading of all the metrics, our actual overall productivity went down. The reason for the overall productivity loss is that the benefits from the training were outweighed by the loss of productive time spent on training. This is not what we hoped to see. But it is reality. And it is much better to know reality than count on hope.

Monitoring Shop Floor Performance is a Key to Success

Modern Timeclocks Make it Easy to Track Utilization

Kill the Training!!

No! Don’t do that! Snap decisions based on metrics are never a good idea. We want to be thoughtful in the way we approach this kind of scenario. Instinctively, we know training is good, but we don’t want to be surprised by our financial results.

Additionally, we also know that training is an investment in our people. The expenses are incurred today, but the benefits should last for years to come. So, while there is no doubt that training is good, we want to make sure we figure out how and when we are going to pay for it.

How Do We Manage It All?

More than anything else, the executive team does not want surprises. So, we need to be able to improve our capabilities on the shop floor without causing chaos in the financial statements.

Fortunately, things like the impact of training can be modeled and budgeted.

Careful budgeting and planning should take training into account, along with the associated loss in utilization and the overall impact on productivity and margins. Companies that budget increased margins because they are implementing training may be setting themselves up for nasty surprises when the financials come in.

By thinking through the kinds of training needed, then making plans on time spent in training, companies can phase in training needs in a way that doesn’t cause financial disruption or surprises. Like many things in business operations, cost and speed are flip sides of the same coin.

We can throttle our training up, meaning more and more hours per week, but there is a cost. Alternatively, careful planning and management can allow you to implement a training regime that you can handle and that does not lead to surprises.

By thinking through the kinds of training needed, then making plans on time spent in training, companies can phase in training needs in a way that doesn’t cause financial disruption or surprises.

Other Surprises

We have spent most of our time discussing training as the driver of time away from the production floor. It is often a common driver of non-productive time in manufacturing environments. But it is far from the only activity that takes folks off direct labor work. And companies often do not have a very good handle on just what their folks are doing when they are not clocked onto jobs. And that costs you margin.

The best way to address this is by setting up indirect labor reporting codes. This means collecting data on the time folks spend on activities other than direct labor. This can be done in categories and can sometimes leverage other systems like an HR system.

The general idea here is to identify the most common off-job requirements and then have folks report those when they happen. These usually include training, required meetings, town halls, and breaks. Individual companies often have tasks that are unique to them as well that land in this bucket.

Safety Meetings and Medical Visits are Common Non-Productive Activities. How Much Time Do You Spend on these Activities?

Set Up Visibility

Gaining an understanding as to what is happening during these down-times can be very valuable. For example, if an analysis shows your crew reported training of 20 hours, but you know you only offered 18 hours, something is wrong.

There are many other possible areas where we lose utilization as well. Indirect labor reporting will help identify where that is happening and gives you the chance to manage the issues.

In addition to setting up the codes to allow your system to capture indirect labor activities, you also need a series of visual tools so you can really appreciate and make the most of that data. Reports, dashboards, shop-floor whiteboards, etc. are all good methods of visualizing your direct labor workforce and what they are doing. There are many ways to skin a cat. Figure out what works best for your organization. The key element is that you can efficiently see what is happening with your direct labor resources.

Drill Down to Understand the Business

A good regime of indirect labor reporting can give you a greater understanding of your operations and where your labor resources are spending their time. The whole point of this is to allow you to take corrective actions when you see things going off course.

I suggested a scenario above where the company offered 18 hours of training, but, when it drilled-down into the down-time numbers, they saw 20 hours of training being reported. One usual suspect in that scenario is what I call meeting setup. Manufacturing folks are used to the concept of setup. It’s when you need to take time to warm up a machine, update its settings, adjust some tolerances, etc, before beginning the actual production run. The concept bleeds into administrative areas as well.

A 60 minute training session may take 90 minutes if we include setup, and the setup goes slowly. By setup, I mean the act of shuffling from the floor to the training room, the settling-in and small talk, and then the reverse at the end of training. That extra 30 minutes (or 20, 15, 10) multiplied by the number of bodies being trained multiplied by every session can easily cost you some margin.

And if you don’t know it’s happening, then you have no chance of fixing it.

Manufacturing Systems Generate Enormous Amounts of Data About Your Business… Do You Understand Your Data?

Final Words

There are great benefits to the collection, organization, and analysis of labor performance data and the associated metrics that become available as a result. In a nice bit of irony- I think it’s irony- we need to acknowledge the fact that the very existence of a labor reporting requirement entails some cost and loss of productivity.

Data collection itself is a non-productive activity. We instinctively know there is great value in knowing the performance numbers of our manufacturing operations. However, as we have seen, drawing incomplete or incorrect conclusions may do you more harm than good.

We must also make a very active effort to set up processes to facilitate and ease the burden of labor data collection. We want the data, but we want to collect it very efficiently so that work does not become a cost factor itself. We will examine methodologies for this in an upcoming post.

According to a study by Deloitte and the Manufacturing Institute, the skills gap in US manufacturing is expected to result in 2.1 million unfilled jobs by 2030. We will need to invest significant time and hard dollars in training to close this gap.

To properly support this demand for training, and all the other important non-productive activities we all perform, we want to give the right amount of time and attention to choosing and designing the manufacturing metrics we use to understand our business. We want to fully understand each of the metrics. We need to understand the dynamic relationships between different metrics and understand the trade-offs between related metrics like efficiency and utilization. And we need to understand how to apply corrective actions when things are not working according to plan.

With these capabilities in place, your manufacturing team will be in the best possible place to optimize their operational and financial performance.

The End