3 Ways to Improve Service Business Profitability

Improve Service Business Profitability

by Teppo Salmia • 10 min read

Are you still selling low margin spare parts by the piece and protecting your revenue against original component manufacturers by masking part numbers? If so, odds are that you are not alone. Despite all the hype around value-based economy, spare part sales still is the largest source of aftermarket revenue for most capital equipment manufacturers. In this article I will discuss how you can improve service business profitability and

  1. Get a healthier margin from selling spare parts
  2. Get higher margins from performing service & maintenance operations
  3. Increase customer loyalty and get higher margins through optimized product performance

Improve spare part Sales Profitability

A few years back I discussed with a customer about their spare part sales process. Frustrated, the customer told me that it isn’t that uncommon for one or two persons to spend anywhere between half a day to a day investigating which spare part should be delivered to a customer. Even if the price of the part was less than 100 €.

Do you know the true cost of selling spare parts?

Let’s assume the part costs 50€ for the company and they sold it for 100 €. Healthy margin, right? – Well, not necessarily. If two persons spend even an hour investigating which spare part is the correct one, the margin has melted away. Granted, if you sell a lot of high value, high margin spare parts, you can carry the cost of occasional investigations. But if having to spend hours on investigating which low value low margin part to deliver is the rule rather than exception, you need to do something – and quickly. You could be losing money all the time.

Improving profitability in this situation starts with having to spend less time with value eroding investigations. You can decrease the time by having a better understanding of what you have originally sold to the customers, and by having an understanding of how those products have evolved over time.

Knowledge is a key to improving spare part sales profitability

When you know the up-to-date configurations, and have them computerized, you can cut down the time to find the correct part from hours to minutes. In the best case down to seconds. Accurate information is a necessity to have. Especially if you want to move spare part sales to an online portal. This is where a modern PLM solution with integrated as-built and as-maintained management capabilities excels and can give you the edge over your competition.

Start improving your profitability by capturing accurate as-built configurations of delivered products. And continue by building a mechanism to maintain the configurations up-to-date even when parts are replaced during service, or when the individual units are retrofitted with new features. Staying up to date with the changes could be tricky if distributors or 3rd parties service the products. You shouldn’t accept this as an excuse not to change the status quo. Try incentivizing your distributors by e.g. providing them with discount on spare part purchases or paying them a bonus if they agree to feed back the updated configurations. Just make sure you tie the incentive to savings directly attributable to your improved configuration knowledge.

Higher margins from maintenance and service operations

Do you know what your first-time fix rate is? And how do you compare with the competition? A study performed by Aberdeen some years ago found that in B2B setting the best-in-class companies reach nearly 90% first-time fix rates, while the laggards average just above 60%.

What impact does failed service have?

The impact of not being able to resolve the issues on the first visit, is twofold. Having to make more than one service visits significantly increases the cost of service. If you can’t bill the customer for successive service visits your bottom line will be negatively impacted. And even if you can bill the customer, customer satisfaction will take a hit. A hit that is the bigger the longer it takes to fix the issue.

How do you secure no follow-up visits are required?

If you skipped over the part discussing spare parts sales, I suggest you re-visit it. Anyway, what I discussed in the last two paragraphs of the chapter is a key component for improving your first-time fix rate as well. Having up-to-date granular digital twins of the fielded products is the foundation for knowing what to expect on a service call, and which spares to carry with you. But that’s by no means enough.

For all your field service technicians to be able to carry out required service operations to the satisfaction of your customers, they need clear and concise service instructions. The same is true if you have distributors or partners that perform the service operations. Maybe even more so, since they wouldn’t typically have direct access to your engineers for clarifying information. Without the instructions you are relying too much on individual competence.

For the instructions to be useful to field service technicians they need to preferably address just the procedures at hand. And preferably adapt to the level of experience of the technician. In addition to providing straight forward instructions for fixing issues, the instructions should include troubleshooting. The hardest issues even for an experienced field service technician to fix are those that do not directly manifest themselves. Sometimes the root cause can be identified only by connecting the dots based on multiple symptoms.

Product digital twin provides the information needed to plan service procedures

This is where interactive electronic technical manuals, also known as IETMs, excel. IETMs have been around for a long time in aerospace and defense industries, but are only gradually making their way into e.g. automotive and heavy equipment industries. Accurate, granular digital twin of a product is a great source of information for IETMs. Having an accurate 3D mockup of a product coupled with computer interpretable behavior model is hard to top when planning how to troubleshoot an issue. Or when defining the procedures and writing instructions to follow for fixing an issue.

So, if you don’t already have comprehensive digital twins of your products, start working on building them. Then start capturing accurate as-maintained configurations. And finally, put in place integrated service engineering capabilities and start providing your service organization with dynamic 21st century instructions.

The Ultimate Goal – Optimized Product Performance

Predictive maintenance has become commonplace across different industries. This development has been driven by efforts to minimize downtime of critical equipment. It’s easy to understand that disruptions caused by failed production equipment cost a lot. Depending on the industry we could easily be talking about tens of thousands, even hundreds of thousands of euros per hour in lost revenue. Not to mention the negative impact on corporate image if deliveries cannot be made as promised.

So, what is optimized product performance after all?

If you are an owner-operator, you expect the plant or system or product to perform at a satisfactory level and produce the expected outcome with minimum downtime. You would also expect to minimize the total cost of ownership. In a traditional after market business model spare parts and maintenance services provide an important source of revenue to OEMs. Thus, OEMs have not really been incentivized to minimize the maintenance cost.

The game will however change radically for any OEM that adopts a value based service business model. Since OEMs assume full responsibility for maintenance costs over the contract period, it is in their interest to to take a hard look at total cost of ownership. All of a sudden durability becomes a much more critical performance parameter. This could have a profound impact on products, all the way down to component and material selections, and manufacturing technologies. Accurately predicting when to replace components also becomes much more critical, when total cost of ownership is optimized.

Why predictive maintenance may not provide expected savings?

Predictive maintenance is likely to help you avoid the cost of production disruptions. However, there is another side to the coin – the cost of replacing parts unnecessarily. R. Keith Mobley cites an example of this in 8th edition of his book Maintenance Engineering Handbook.

Mobley states that over a six-year period, annual part replacement cost for for a steel mill grew from $2,7 million to $14,1 million. This happened after the company put in place a predictive maintenance program designed to eliminate unscheduled downtime resulting from failure of critical bearings.

Of the $14.1 million spent, only $705,000 was for bearings that had reached or exceeded their rated design life. The remaining $13.39 million were an unnecessary cost.

Case example of a steel mill predictive maintenance program impact. Source: Maintenance Engineering Handbook – 8th edition, by R. Keith Mobley (see Google Books for where to get it)

As soon as critical bearings showed any signs of abnormal performance, the company scheduled them to be replaced at the first possible maintenance window. This practice resulted in excessive, premature replacing of these critical parts. Root cause analysis showed that only 5% of the bearings failed as a result of reaching the end of their normal life. Overwhelming majority of the problems were caused by maintenance either choosing wrong types of replacement bearings, using improper lubricants or improper lubricant application methods, and by improper operating procedures.

So how can you avoid excessive replacement of parts?

In the steel mill example selecting wrong types of replacement bearings, or improper installation of bearings caused a whopping 49% of all bearing failures. This proves the point I made in the previous chapters. Knowledge is key to improved profitability. Think about it. The steel mill in question could have avoided roughly $7 million worth of unnecessary cost per year, had they been armed with information on correct replacement parts and proper maintenance instructions.

But let me get back to how you can more accurately estimate the true remaining life of various critical parts. And how you can use that information to optimize product performance for value based business models.

To begin with, you obviously need to understand how your products behave under different conditions. And how the products might fail under these conditions. But since you are in fact also interested in how the parts perform, you need to take your understanding to subsystem and part level. Thus you need accurate and granular model-based digital twins to build on.

You also need your products to be connected. That’s because you need them to feed actual performance data collected from sensors back into your simulation models for computing remaining life of various critical parts. It is however impractical, or even impossible to equip physical products with enough sensors to monitor all the necessary performance parameters. So what should you do?

Combine simulation models with real performance data to predict remaining life of critical parts

Use sophisticated 1D simulation environments to build virtual sensors. Virtual sensors enable you extend the real life measurement dataset to accurately measure phenomena you could not measure in real life. Thus, virtual sensors enable you to understand how real time load conditions, measured with a few critically placed sensors, impact product or part performance elsewhere. Then combine 1D system models with 3D multi-physics simulation to analyze e.g. strength, durability, vibrations or thermal behavior of parts and systems.

For those of you unfamiliar with 1D simulation, it is all about modeling and simulating multi-domain mechatronic systems, and predicting their behavior by connecting validated analytical modeling blocks of electrical, hydraulic, pneumatic and mechanical subsystems into a comprehensive and schematic full-system model.

It takes some effort to validate the accuracy of the simulation models with test data. But once you have validated and further correlated your models, you are ready to reap the benefits. Simulation models provide you with an understanding of how and under which conditions a part might fail. When you feed the models with real performance data, you can reliably compute the remaining life of critical parts.

What could you do with this knowledge?

How can this knowledge improve customer loyalty? Or how does this knowledge help you to get better margins from services provided?

Let’s look at customer loyalty first. A happy customer is likely to be loyal. And odds are that a customers who see their business performance improving because you can help them avoid unscheduled downtime, and in general optimize service and maintenance costs, is going to be happy. And because of measurable business benefits loyal customers are inclined to pay more for your services.

Parting Thoughts

I did mention the hype around value-based economy at the beginning of this blog post. High capital cost of industrial and heavy equipment is likely to slow the transition to value-based economy. Many industries are however slowly but surely moving in that direction. There are already rumors for e.g. Volvo Cars possibly making some of the new car models available only through private leasing contracts.

I don’t recommend moving into value based business models without a solid understanding of how your products fare in real-life situations. Or without a solid understanding of service business cost drivers. As the steel mill example above proves, your business could go belly up in no time at all if you don’t understand the true service business cost drivers.

But if you marry connected products with accurate and granular model-based digital twins, you have the means to make the transition without the business risks becoming unmanageable.