Why Aftermarket Players Pivot Toward Digital Twins

Digital twin technology officially entered the limelight decades ago, but it has attracted substantial investments in more recent times. In the past two years alone, the use of digital twin technology has skyrocketed to support pandemic recovery. The global digital twin market, valued at USD 10.27 billion in 2021, is now on track to continue this upward trend. According to a new report, the digital twin market will grow at a robust pace through 2027—when it is anticipated to “reach a value of USD 61.45 billion”.

It is growing clear that the digital twin boom will last. Yet what exactly is a digital twin, and how is it used in the aftermarket today?

Experts in the field define the digital twin as “a living model” of a physical entity. The virtual replica is “continually updated” with data, accurately reproducing “the conditions and attributes of the real-world counterpart” and forecasting its future behavior. Those taking advantage of digital twin technology can study the performance of given machinery or part thereof in real-world conditions, from production to the aftermarket phase. Not only that, but they may also virtually monitor asset health, predict potential faults, and forecast spare parts demand to optimize aftermarket services and deliver well-timed maintenance or repair work.

The advent of digital twins has remarkable implications for today’s aftermarket world. Here is a more in-depth look at how virtualizing the physical world can open the door for aftermarket players to increase the efficiency of their spare parts and service management.

Digital Twin Technology Is Changing Spare Parts Management for the Better

The digital twin has what it takes to become a more pervasive technology for spare parts management. Through a digital twin system, aftermarket players are able to monitor wear on parts and correctly estimate when a replaceable component needs to be purchased. The virtual equivalent of machinery is capable of capturing the service history details of its physical sibling. As a result, firms have precise knowledge of which and when spare parts were installed to operate strategically and more efficiently.

If a spare part shows signs of deterioration, the digital twin can notify industry players of this in real time. The same happens when a required part is not in stock. Along with real-time alerts, advanced platforms also provide instant access to spare parts listings and allow for direct ordering of parts.

Already, some firms regard digital twins as indispensable in streamlining parts identification and ordering. Plenty of industry players push ahead to invest in technology that emulates the physical world to be more effective at training new workers on how to select spare parts for work orders. But this technology is also key in forecasting spare parts demand.

How much of a difference could a digital twin make in demand forecasting?

A study published by World Scientific Publishing finds that the digital twin makes it possible to forecast the demand for spare parts more accurately than traditional methods. As the authors of this study point out, accurate forecasting is needed to “formulate reasonable ordering strategies for spare parts” and, thus, prevent “problems of overstock and insufficient inventory”. Having a proper amount of spare parts in stock will lead to improvements in “efficiency of equipment use”, “maintenance support”, and “market competitiveness”.

Firms in Pursuit of Service Efficiency Will Benefit from Digital Twin Adoption

Efforts to recondition a piece of machinery or conduct repairs after it breaks down are nothing out of the ordinary for firms operating in the aftermarket. But, as an expert analysis discovered, taking corrective action is inefficient and very expensive in the long term. Over-relying on corrective plans may lead to higher failure rates and repair costs. A far better way to service machinery is to go beyond reacting after failure detection to initiate predictive action using digital twins.

Thanks to digital twin technology, anyone can become adept at predicting when maintenance work is required—which helps firms plan for, not react to, machinery malfunctions and facilitate better management of service provision.

Here is how that works.

Employing a digital twin for the real-time monitoring of machinery provides insight into the system’s health condition and behavior. The real-time data, which is continuously collected from sensors by the digital twin and ran against historical information, is analyzed for early detection of faults and wear. As reported in a study, “fault diagnosis and prognosis reach a higher accuracy and the reliability of equipment is enhanced” when advanced algorithms and high-fidelity digital models are combined.

Aside from delivering warnings of potential breakages before they cause any costly damage, this technology is capable of making helpful optimization recommendations. The digital twin predicts future asset performance so that industry players may precisely anticipate when a piece of machinery requires maintenance and service. This way, firms can take steps to optimize their maintenance and service schedules, adjusting routines to the machinery’s actual needs and increasing first-time fix rates.

Ultimately, as firms augment digital twins with predictive capabilities to foresee the likelihood of future machinery failures, they may increase customer satisfaction through enhanced aftermarket service offerings.

What to Remember Before Embracing a Digital Twin

The digital twin, lauded as a revolutionary technology, will fundamentally reshape aftermarket activities. But it is a significant investment, so firms interested in a digital twin solution have to know with certainty that they are making the right technology choice.

For this reason, industry players need to assess their technological readiness before any investment is made. It is similarly vital to calculate both the risks and rewards of each digital twin solution and, not lastly, to select a system tailored to support the firm’s business objectives.