What this blog is about:
The transformation of a traditional equipment sales and after-sales services business into a connected, networked, outcome-based business model is often seen as a commercial evolution. However, manufacturers must also transform business processes, skills, and systems across product development, sales, and services to capture the benefits of closer customer intimacy and engagement, faster innovation cycles, and predictable high-margin revenue. Equipment-as-a-service business models increase the risk profile and require extended corporate governance. Buyers will equally need to become more sophisticated in selecting and implementing outcome-based offerings. Integration of master and live equipment data requires a network approach of business partners and digital twins.
The past decade has provided manufacturers of industrial equipment with a broad range of IT technologies which can be integrated into traditional machinery. The number of embedded sensors has exploded due to lower cost and power consumption. Mobile, wireless, and fixed IP-based connectivity modules can deliver a real-time, always-on view on machine conditions. Storage of machine data both on the machine and in the cloud is available at very low cost while advanced data science and machine learning tools allow to derive deep insights into performance, anomalies, and potential failures. These technologies are often described as pertaining to Industry 4.0 or the Internet of Things.
At the same time, industrial equipment vendors have evolved their after-sales services business to become more competitive in a marketplace with decreasing product differentiation, to capture higher margin services profit pools, and to increase customer loyalty and market reputation. Moving from repair and spare parts services, vendors have added maintenance and remote support offerings, created value-adding optimization, and – in industries like construction equipment – proposed time-based rental services.
The evolution of technology is often used to drive further efficiencies of service models. Monitoring of equipment conditions combined with predictive models allows to reduce warranty cost and unplanned customer downtime while accelerating service response. Detailed insights into issues support service technicians to be better prepared and equipped with the right work instructions, tools, and parts. Manufacturers use additional information to refine their SLAs, create customer-specific service plans and offerings, and optimize spare parts and service technician planning.
The combination of hardware, embedded software, and services also allows to further customize solution offerings at a late stage of production or even during installation. This not only creates additional customizing services opportunities, but may also reduce the hardware complexity through modularization.
Innovative equipment manufacturers have started to think beyond optimizing their current business model. Known as product servitization or equipment-as-a-service, manufacturers no longer sell products and deliver after-sales services, but provide equipment to their customers priced by a performance- or outcome-based metric. This business model has become the dominant approach in the IT industry (software-as-a-service), replacing traditional hardware and license sales with maintenance services.
The introduction of innovative (yet unproven) products into mature markets or to address latent demand often requires manufacturers to reduce uncertainty and risk among leading customers. Servitized business models can help to more effectively compete and create new markets.
Data gathered to profitably operate an equipment-as-a-service approach opens a growth opportunity into a data-driven business. Combining statistical and predictive insights from a large installed base with environmental and business context data allows to create data-driven offerings, starting from benchmarking and consulting services to data aggregation services. Attracting partners to extend the vendor’s offering leads into a product-centric platform business. Figure 1 depicts the evolution of services for product-centric companies.
In the following sections, we will explore the benefits, challenges, and change requirements when transforming to an outcome-based business model for equipment vendors. Separate sections will assess the impact on the buyer and show the need to establish a networked business model on multiple levels.
Equipment manufacturer perspective
Product-centric innovation has been the dominant growth for industrial and commercial equipment manufacturer in developed markets. Given a strong focus on sophisticated solution buyers, vendors have complemented their products with after-sales services ranging from maintenance and support to spare parts and repair, optimization and benchmarking, education and training. Services have been used to increase competitive differentiation (against low-cost leaders in the market), to drive high margin growth, and to increase customer loyalty along the product lifecycle.
While expanding the services business, equipment manufacturers have established service organizations and corresponding business processes to optimize services margins, service technician resource utilization, spare parts and service vehicle management, service level agreement and contract management. Services are increasingly delivered through external providers, requiring subcontractor management. Typically, these services are fixed in scope with agreed-upon response times and additional material invoiced. Business systems for customer service management support service organizations with remote support, on-site technician dispatching, or contracting and billing.
In parallel, some industries have established rental models to increase utilization. Construction or farming equipment is billed based on duration of use while the airplane engine industry led by Rolls-Royce (see here) has evolved its fixed cost per flying hour business model. In both cases, the service provider assumes responsibility for availability and sufficient operations performance of equipment as well as all necessary maintenance operations.
Profitability, customer satisfaction, and safety of these business models depend on insights into the usage and wear of the equipment for efficient operations and maintenance. Data gathered from sensors built into the device (known as the Internet of Things) and ingested into (mostly cloud-based) data stores is analyzed using advanced analytics, machine learning, and physics-based modeling tools. In the past, these technologies and skills were only attractive for high-end use case. Now, they have become available for a large variety of industrial and commercial equipment via platform-as-a-service, cloud-based IoT, and big data platforms. The insights generated allow both to improve operations, lifetime, and maintenance planning for equipment, and to provide enhanced service level agreements to customers with performance metrics, outcome-based pricing components, and risk and reward schemes.
Using live data from machines, the servitization of products has started to move into consumption- or outcome-based models. Kaeser Kompressoren (see here) offers compressed air at a pre-agreed price per cubic meter with no investment cost on the customer side. The offering is flexible and can be adjusted in both directions depending on customer demand. Optionally, Kaeser also takes on responsibility for monitoring and operations.
From the CFO’s perspective, equipment managed under a customer service contract should not remain on the balance sheet. Hence, manufacturers will need to collaborate with financial institutions, leasing and rental providers, and insurance providers on finding suitable partnership arrangements. However, outcome-based customer contracts pose higher revenue volatility risks than traditional time-based rentals.
Business transformation of product-centric services into outcome-based services requires not only the right digital platform, but also adapted business processes and business systems across all lines of business.
At the very basic, products need to be enabled to collect and send relevant data to measure agreed upon outcomes and to detect any issue or failure. Internet of Things (IoT) technologies using embedded sensors, IoT gateways, mobile and fixed IP-based connectivity options with IoT security, data ingestion pipelines, and cloud-based storage have matured and become cost attractive. However, product development also needs to integrate operational data with the commercial metering, billing, and invoicing infrastructure run by the finance team as well as the customer contracting and services systems. Onboarding, upgrading, relocating, and decommissioning of products (and related services) become core enterprise processes, not only to meet SLAs and customer satisfaction requirements, but also to keep control over infrastructure cost for big data and mobile connectivity.
Availability of services depends on operations of products. Hence, product development needs to ensure not only development, but also enable operations of the entire service delivery value chain. Product firmware and (edge) application lifecycle management become equally important to allow for operations at scale in the field to reduce complexity cost. For industrial equipment with an average life expectancy of 5-10 years, several product generations with different software releases will be managed and maintained. DevOps (unified development and operations) and agile practices are essential to transition to outcome-based business models. Design goals will include ease-of-installation, ease-of-maintenance, or ease-of-repair. Reuse and recycling of equipment will equally become part of the product development under the circular economy charter.
The IoT data gathered from productive customers provides a lot of opportunity for further analysis:
- Detect and predict anomaly and failure patterns for product series
- Propose preventive actions during the warranty phase in case of early product issues
- Prescribe maintenance and repair work instructions and spare parts based on actual usage and issues
- Optimize product usage at customer and recommend higher performant or more cost-efficient operations
- Recommend replacement and reuse at the end of customer usage
- Review and adapt product design and create software-driven configuration options
Product development needs to create a (distributed) data management foundation as a common infrastructure for all LoBs across the enterprise to analyze behavior, to develop algorithms, and to optimize equipment operations and maintenance. Moreover, new product design will rely on high quality data to ensure the long-term profitability in a maturing market and enable a simulation-driven virtual design approach. This also allows higher customer intimacy for faster product cycles as well as opportunities for customer co-innovation projects.
In many cases, algorithms created during product design become an essential component of the services business, encoding intellectual property beyond embedded software shipped with the hardware.
A critical success factor for the product development organization is to evolve the design-to-cost methodology. While traditional business models have only loose interactions between product and service margin calculations, equipment-as-a-service requires an end-to-end product and services delivery costing approach. The uncertainty and volatility of future usage, excessive wear in unknown scenarios or environments, and other risks from operating the product must be included into the calculation. A proper customer segmentation and product portfolio approach needs to guide the equipment-as-a-service design. Pricing and commercialization often become corporate functions as the business risk exposure extends beyond development or sales cycles.
Innovative manufacturers who implement equipment-as-a-service product development will envision to also embed component-as-a-service offerings, e.g. pumps, motors, or filters. Neither suppliers nor internal sourcing and procurement processes will be prepared initially to create servitized supply chain.
The transition into an outcome-based business models affects sales in a profound fashion as revenue and profit are generated over the course of the contract (while product and after-sales services are more evenly split between initial contract closing and during the product lifecycle). Moreover, in a consumption-based approach, revenue generation depends directly on customer adoption. Sales incentives need to be based on forward-looking or planned revenue, driving up initial sales cost ratios. In most cases, manufacturers will augment their traditional business model with outcome-based models. Clear sales guidance becomes essential to address the right customer segments most suited for profitable servitized product business and to avoid margin cannibalization.
Sales approaches for acquiring new customers (hunting) will be different than renewing (to avoid churn) and upselling to existing service contracts (farming). Digital startups have created the role of chief revenue officer to streamline service selling processes, introduce comprehensive configure-quote-price process and tools, and to manage service contracts and SLAs across different customers based on a data-driven approach from actual usage information.
An agile and flexible contracting and monetization platform needs to provide fast ways to create and automate new pricing structures with flexible contract duration and billing terms while giving holistic insights into customer entitlements and consumption. Sales tools should be tightly integrated to support account executives with estimating consumption, expected outcome or performance, and impact of contract duration. Proper commercial and technical risk assessment for individual customer contracts needs to become an integral part of the quotation and contracting process.
With a strong focus on efficiency, condition monitoring provides a real-time visibility into the state of the equipment during operations and allows operations staff to react immediately to any anomaly, continuously improve, and gain insight and predict potential issues. Predictive maintenance delivers asset health scores and remaining lifetime information to dynamically schedule maintenance windows in accordance with agreed upon SLAs. IoT data can also be used to optimize service scheduling and technician dispatching, work instructions, spare parts planning, service vehicle fleet management, and work execution (e.g. using augmented reality).
Due to the relatively higher fixed cost of equipment-as-a-service, product utilization will become a major concern during the customer contract duration to increase margin. Manufacturers will often need to realign their sales and services teams to address the ongoing services engagement and upsell opportunities. Moreover, the service engagement team is responsible for generating sufficient margin through higher utilization of equipment, value-added services, and long equipment lifetime.
Most vendors will complement their equipment-as-a-service offering on customer premises with a cloud-based customer engagement platform for consumption monitoring, bill and invoice presentment, ticket management and tracking, and remote support engagement.
Manufacturers will often rely on service partners to support service delivery on a global scale. Outcome-based business models will require different engagement models and access to critical IoT and customer data to efficiently deliver the service. The same applies to spare parts and tools suppliers as response and procurement lead times will affect the overall SLA compliance and potentially service margin.
With equipment under physical control on customers’ premises, manufacturers need to protect and remotely management configuration settings and other equipment parameters to ensure equipment integrity, safety, and performance. This includes all components used to measure and bill performance and outcome. For equipment operated by customer staff, the service contract needs to stipulate customer responsibilities like regular inspections, anomaly detection, or emergency shutdown procedures. In other scenarios, manufacturers will partially or completely operate the equipment, adding labor and skills management and costing to the scope of the service offering.
The implementation of an outcome-based business models for product-centric companies requires a corporate digital platform. Harmonizing and integrating formerly distributed product development, sales, and services data silos is essential to create a common platform for different LoBs. Digital twins are a helpful concept. Building on design artifacts and models created during the product development phase, each equipment is configured and serialized during production to create a digital twin of a specific equipment. During installation and onboarding, the digital twin is augmented with location and usage information. Live IoT data is collected and evaluated during operations and serves as basis for maintenance scheduling and product lifecycle management. Using rules, data science, and machine learning, events can be published to different consuming departments for further actions. As corporate-wide implementations, a digital twin requires a strong governance and authorization management.
What is a digital twin?
A digital twin is a live digital representation of a connected physical object. Representing the essential physical manifestation and the business context of the object (from inception during design or production to end of life), a digital twin provides real-time condition and state information as well as historic data which can be queries through APIs. Digital twins act on behalf of physical objects by sending alerts and notifications while being able to initiate control flows to act in the physical world. More complex digital twins feature hierarchies or relationships, contain derived data from statistical, machine learning, or physical simulations, and expose service capabilities. Connecting digital twins from multiple providers to different consumers creates a trusted network of digital twins.
Parts of the digital twin model will be extended to the customer (or even maintained by the customer). For equipment which is used as part of larger production lines, live or aggregated IoT data will be shared back with customers. Equally important, service partners will require access on a more granular level. Access for external consumptions require a role-based authorization model with cloud-based identity and API management. Contractual and legal obligations for data privacy and protection need further consideration. Manufacturers will be challenged by customers on the use of operational data beyond the narrow scope of delivering the service. Building a trusted relationship around data from customer operations is a prerequisite for an equipment-as-a-service business, failing on this dimension may put the entire manufacturer at risk.
As the role of a manufacturer changes, so does the role of the buyer. While the financial impact is certainly most visible shifting from capital expenditure to operating cost, equipment operators need to take additional steps to take advantage of an outcome-based business model.
Engineering and procurement
Planning and procuring outcome-based services around industrial equipment poses significant challenges for the buyer. Typically lacking historic usage information at sufficient granularity, vendor offerings are difficult to assess, both standalone in view of operational requirements and compared to traditional product plus after-sales service propositions. Assumptions about expected lifetime and future utilization significantly impact the business case calculation. Most offerings will propose trade-offs between flexibility, duration, and pricing. Consumption-based leasing contracts may be more advantageous than outcome-based service contracts.
Buyers should take a data-driven approach when preparing for adopting an equipment-as-a-service model and require manufacturers to provide sufficient access to usage and maintenance data. A win-win for buyer and manufacturer is typically not achievable on financial terms only. Outcome-based pricing with proper metrics and incentives allows to strive for higher performance levels delivered by the manufacturer working on the customer problem while limiting the risk of underperforming equipment. The alignment of objectives, outcome or performance criteria, and measurement processes will likely take more time than simply buying or replacing equipment.
Equipment has often been repurposed or recycled for other use after the initial usage phase. In an equipment-as-a-service model, the buyer does not own rights to the equipment beyond the contract duration. A holistic view of equipment lifecycle is mandatory to assess impact and economics of adopting new service contracts.
Maintenance and operations
Most importantly, equipment-as-a-service delegates availability to a third-party vendor, however, the buyer continues to assume overall responsibility for the facility. Typically, the manufacturer will not take on further business responsibility, leaving a business risk for not meeting SLAs, not delivering expected outcomes, or even ceasing service. Operations of the equipment may stay with the buyer, or even become a fully managed service from the manufacturer. In the latter case, buyer and supplier operating and risk management practices and potentially organization need to be aligned.
Streaming operational data from the buyer’s premises to the manufacturer’s cloud (and potentially back) typically supports analytical use cases, but not shop floor operations for reasons of latency and safety. However, local access (e.g. through gateways) to data and configuration settings may pose security risks for the manufacturer.
The integration of live data from the manufacturer (e.g. into operations or maintenance dashboards, planning schedules, alerting and event management, machine orchestration) poses additional technical and contractual challenges. Access rights, data sharing and archiving agreements, data usage after contract expiration, and third-party usage rights need to be negotiated to ensure asset operations and performance improvements.
As buyers set up digital twins for their operations, both master data and live equipment data needs to be integrated from the manufacturer. The granularity of data will depend on independent the equipment operates and how anomalies and performance variations affect other parts of the buyer’s operations. In general, buyers using equipment-as-a-service offerings from different suppliers will face heterogeneous integration needs with various vendor portals, master data and live data interfaces.
The network effect of outcome-based business
Both manufacturers and buyers are equally challenged by the collaboration and integration requirements of outcome-based business models for industrial equipment. Manufacturers need to set up a regional or global network of connected customers (often in different industries or usage contexts) while buyers will consume services from a variety of suppliers across different equipment types, different production sites on a regional or global scale. At the same time, both parties will leverage additional service providers to deliver parts or complete service offerings with physical and data access to equipment. Insurance providers, financing or leasing companies, and regulatory bodies may further expand the ecosystem of partners on both sides.
The business network for equipment
Traditional one-to-many collaboration will not scale with data exchange and authorization requirements when moving from service orders and contracts to master data and further to live equipment data. Equipment models with hierarchical component information, physical layouts and designs, hazardous materials and safety documentation, work and operations instructions for integration into production procedures are just a few examples. Service providers also need access to repair and maintenance instructions, spare parts information, firmware and configuration settings while updating actual maintenance and repair journal entries. Other parties will be interested in equipment-related incidents, usage or consumption information, location data (e.g. for moving or movable equipment).
Buyers will integrate the equipment data into their respective digital twin models, often at different levels of asset or component hierarchy, systems boundaries, and organizational responsibilities. While digital twin master data sharing is a differentiating convenience factor in traditional product sales with after-sales services, the fluent borders of outcome-based business models with numerous stakeholders involved for delivery require a network-centric approach to enable a seamless digital business.
The network of digital twins
Networking live digital representations will become a prerequisite for buyers of outcome-based services for industrial equipment. Both central approaches for sharing (mostly for analytical and planning purposes) and edge-based integration (for low latency alerting and operations) will coexist. Manufacturers need to invest into live discovery and onboarding capabilities as well as drive standards to ease operational go-live.
An established digital twin network also provides a foundation for further partner models. Manufacturers can include partners into their offering and delivery who propose value-added services based on data or software co-deployed or embedded into the equipment. Industrial equipment may evolve into a platform for delivering outcome-based services. With superior understanding of live data integration and semantics, manufacturers may also evolve into system integrators or data aggregators.