Manufacturers, There’s Way More Money to Be Made After the Sale—Here’s How to Get It
Tom Gill posted on September 26, 2019 |
OEM can get revenues from service, spare parts using PLM, BOI, digital twins, IoT.
There’s money in service, repair and maintenance. OEMs can use PLM, IoT, digital twins and bills of information to exploit revenue opportunities after an initial sale.
There’s money in service, repair and maintenance. OEMs can use PLM, IoT, digital twins and bills of information to exploit revenue opportunities after an initial sale.

Product lifecycle management, or PLM, is generally seen as a technologically sophisticated way for large enterprises to bring better new products to market quickly and at lower cost. PLM boosts revenue and improves margins by pulling together the people, processes and technology of product development.

But is this a limited view of lifecycle management that has led to an overly tight focus on developing new products with PLM? What about current products, those that are already in use or “out in the field,” that could potentially also generate more revenue and heftier margins?

Many credible analyses point out that after-sales margins from spare parts and services are commonly two and a half times larger—and far more predictable—than the revenues and margins that original equipment manufacturers (OEMs) get from the original sale, according to analysts at McKinsey. Over the life of the product, revenues generated by spares and service can be 80 percent more than the OEM’s initial sales revenue, according to PricewaterhouseCoopers (PwC).

Consider the following:

  • The scope and focus of OEM sales organizations can be broadened to embrace opportunities in field service, spare parts, upgrades, and so on. The sales forces of many are incentivized to  push new products and recover development costs as quickly as possible.
  • The after-sales revenue from service and parts needn’t default to resellers and distributors. While their customer relationships may be closer than those of the OEM, the OEM’s intimate product knowledge can more than compensate for this difference.
  • OEMs with service-and-spares businesses report that the after-sales data they glean can predict revenue opportunities in periodic service, replacement parts, upgrades, and their associated costs such as labor, and that it can be used to extract insights into the next-generation of products.
  • The data an OEM needs to prosper with field service, spares, and so on, is much different (and more elusive) than the information captured during product development for use by the sales force. A cost-benefit analysis can be invaluable in quantifying the many opportunities while the product is in use.

In April 2019, the missed after-sales opportunities of OEMs were addressed in a webinar titled, “As-Maintained BOM: the Foundation of Service Revenue.” The webinar focused on managing the product lifecycle after the sale and capturing that information in the product’s digital twin. I am coming back to this topic because of the gratifying response to that webinar in the intervening four months; CIMdata had just over 100 attendees and many have followed up with us. More broadly, the webinar addressed why it is essential to use PLM to support after-sales operations.

After-Sales Problem Solving with Lifecycle Management

In the lifecycle management of after-sales information, there are three main moving parts:

  1. Digital twins that result from their digital threads
  2. Configuration management
  3. Bill of information

A bill of information, or BOI, is an extension of a bill of material (BOM) that includes all the item-to-item relationships, such as parts, documents, and process definitions that fully define a product as well as the processes with which the product is designed, manufactured and serviced.

The BOI is at the core of a PLM environment. It includes all relationships among the product’s various components—that is, all the mechanical, electrical, software, documentation, formulae, physics-based, and process-related aspects.

Managing the BOI, which is a “structure of structures” that includes all the BOMs generated along with its related data and documents as a new product comes to market. The BOI serves to get the as-designed, as-manufactured, as-shipped, and as-maintained BOMs and their information under configuration control.

Configuration (or change) management implemented across the lifecycle can assure that what is done to a product in the field conforms to given requirements and regulations.

This assurance requires an end-to-end enterprise-based process. Maintaining relationships between data elements to support traceability as products evolve is complex and varies with specific company practices. Reliably determining product configurations across the lifecycle can be difficult.

Digital Twin

The digital twin is an emerging as a method to track what happens to the product over time in the field—including maintenance and service, repairs and modifications, replacement and substitute parts, and software upgrades. As the years roll by, formats, workflows and the cast of characters change again and again. Today’s PLM capabilities make gathering and managing this information much easier.

That post-sales revenue potential hasn’t been seized upon by most OEMs, perhaps because of inherent challenges. However, technology vendors deem these challenges as no longer insurmountable. They point to a current maturity and connectivity of digital twins within BOIs and how these technologies can help boost customer satisfaction.

The benefits of digital twins can include better utilization of the asset, improved field-service outcomes, reduced customer complaints, improved insights into customer requirements, identification of upgrade offerings, and closer control of spare parts inventories, leading to fewer inventory write-offs. After-sales business opportunities that formerly elicited groans of frustration can now be put through conventional cost-benefit analyses since data can support it.

Verifiable data from field service can be invaluable in addressing regulatory concerns in aerospace, defense, nuclear power, wind turbines, electrical grids, medical and health care, and fleet transportation.

Building an accurate as-maintained digital twin from any product in the field requires cooperating with multiple internal organizations and business partners. They know who generates BOMs for the fielded product and who is responsible for updating them.

But there are many issues in people and skills, choices of processes and technology implementations that factor into the incorporation of after-sales in a PLM strategy:

  • Legacy field service data—notoriously incomplete and dirty—this can be cleaned up. Service techs can be incentivized to capture the changes in the configurations data that is essential to any OEM’s after-sales success. Service data includes the use of repair/replacement parts, parts from unknown third parties, and parts in unknown configurations. And just because a part was shipped doesn’t mean it was installed.
  • Any viable OEM initiative into after-sales and service also requires that service techs track equipment use over time as well as its performance. OEMs often lack the tools to capture this data and, unfortunately, not all owners/operators will share it.
  • Vital configuration data must be gathered from any beta users and from the product’s first customers; service histories or spare-parts lists may not exist yet because the equipment is the first of its kind. Also unreliable are links between product performance data captured by the Internet of Things (IoT) and the customer’s own configuration data. These links are weak because IoT data is mostly about a product’s performance and not its configuration.
  • BOIs and their digital twin views present an additional OEM opportunity—building or strengthening relationships with customers. Because customers deal far more often with distributors and resellers, IoT connectivity and customer relationship management (CRM) solutions can be a big help in strengthening OEMs’ ties to customers. New contractual agreements may be required.
  • Security concerns must not be minimized. Information normally deemed proprietary may be closely held amid additional risks from the IoT. Likewise, legal issues about the ownership of data generated by customers’ assets must be addressed.
  • Better connectivity between OEMs and customers can also help resolve issues arising from software in complex, connected products. Embedded software is not always well coordinated with product reconfigurations and asset updates. This can worsen product performance and even cause shutdowns.
  • The architectures of data and solutions are crucial. Data needs to be connected and synchronized across the product lifecycle. Change-impact analyses and traceability inquiries need to be fast and accurate and include fielded products.

OEMs in capital and durable goods enterprises may use the above reasons to extend operations beyond sales into service, parts and IoT-connected support. This is particularly true for regulated companies. A modern PLM strategy, enabled by a product innovation platform, can enable these extended capabilities. And, don’t forget: revenue from service, support and spares is far more regular and predictable than revenue from the initial sale of a product.

Where to start? As in any broad-gauged technology implementation, success requires getting all the stakeholders to “see the light.” This must begin with a strategic change in OEM sales thinking.Short-term attitudes such as “sell it and forget it” must give way to the goal of maximizing revenues over the full product lifecycle by incorporating ongoing service, repairs, upgrades and spare parts.

OEMs must refocus their sales pitches away from “low” initial sales prices to total cost of ownership. In many cases, this means restructuring commissions and incentives. The key to it all is lifecycle management; the as-maintained BOM and its big brother, the bill of information; and (most important) the ability to define and manage a comprehensive and accurate digital twin.

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