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Engineering, Technology, and the Future of Work
Autonomous Vehicles Implications for Employment Demand
Creating New Value with Performance-Based Industrial Systems Design and Operations Management An Engineering Opportunity
Education Transformation to Support Lifelong Learning
Beyond Schooling Educating for the Unknowable Future
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Fall Bridge Issue on Engineering, Technology, and the Future of Work
September 15, 2015 Volume 45 Issue 3

Creating New Value with Performance-Based Industrial Systems Design and Operations Management An Engineering Opportunity

Tuesday, September 15, 2015

Author: Chris Johnson

In today’s slower-growth developed world many firms are experiencing reduced demand, in part because of the emergence of new providers of goods and services entering the market with lower pricing and margins. “Old school” original equipment manufacturers (OEMs) and their engineering professionals are faced with a vitality challenge: How can they energize customers, build excitement about their company, and enhance their ability to create value-driven growth?

There are internal and external systemic opportunities to enhance productivity-driven growth with a number of methods that engineers and their organizations can use to harness deep domain experience, hardware design, performance-based services, software, and financial management to unlock value in complex industrial ecosystems, improve the firm’s competitive position, and trump “cheaper” in the market (NAE 2015, pp. 68–70).

This article presents a framework for industrial solutions that create economic value with new investments, growth, and the ability to overcome margin pressure. The discussion is biased toward customer-facing constructs, but the same approaches work internally.

The framework builds on the concept of bundling as an approach to growth. With knowledge of a customer’s operation and the provision of new physical, digital, financial, and operating means to improve industrial infrastructure along with guaranteed key performance indicators or outcomes, service providers can manage certain customer responsibilities better than the cus-tomers can themselves with respect to cost and risk. This can be achieved by bundling insight, hardware, operations decision support software, finance, and a performance guarantee that transfers risk to the provider. Bundling allows organizations to improve operating metrics; develop clear methods to identify and create measurable new productivity and price the risk of achieving it; create service contracts that specify how performance optimization will be achieved, measured, and managed; create shared long-term incentives to co-innovate; and plan for comprehensive solutions that provide “one-stop shopping” for customers, including financing.

Assuming Operational Risk to Cause Growth: Traditional and Equity-Based Transactions

Customers are seldom foolish with respect to acquiring the capital goods and related services that OEMs offer. For fungible goods from OEMs with well-understood and achievable performance (i.e., low risk), a traditional commercial transaction is differentiated by price, integrity, relationships, and reliable services. Where there is acknowledged risk in production, service, or operations, customers usually seek a ratio of financial risk and return from a new investment. If what an OEM offers is not known to a customer, or is not core to the customer’s expertise, a performance-based partnership can be shaped to transfer operational and financial risks to the OEM.

Figure 1

As shown in figure 1, a traditional industrial infrastructure transaction is characterized by an asset offered to and purchased by a customer with capital funds (secured internally or financed) and then operated in their value chain. The provider (typically an OEM) and customer deploy experts to separately focus on engineering design, process design (for use of the asset), operational decision making, financing, and general management. Once transacted, the provider is out of the customer operation (until service or parts are required) and is generally not aware of the extensive interactions and financial incentives needed for operational productivity.

An equity orientation is typically assumed by the customer who is exposed to market demand, competitive substitutions, price pressure, and operational risks arising from the use of the firm’s resources. The financial reward for managing these can be significant—as can the losses. As the (OEM) service provider chooses to shift to an equity orientation, assuming responsibility for infrastructure lifecycle design, operations, and finance, it can include co-optimized economic performance and flexibility in its commercial offering because the risk and value management incentives are available to do so, as explained below.

In a slow-growth commercial setting, a forcing function that a provider can choose is to cause an operational performance risk shift. Co-optimized engineering design and operations, coupled with financing, align the incentives of the provider, the consumer in mature industrial system verticals (e.g., in rail transportation, power generation, health care, aviation), and their manufacturing ecosystems. At the intersection of asset design and operations, productivity gains can reduce a high percentage of operating costs and thus cause an incremental customer impact at a lower cost than through traditional new product introduction (NPI).

An example is a rail network customer operations optimization solution (Vantuono 2015) that required more than a hundred million dollars to develop but saves the customer several hundred million dollars per year in operations expense (Norfolk Southern 2010). This can contrast with investment in a hardware NPI design feature that also costs several hundred million dollars to effect and produces several hundred thousand dollars of customer productivity. Both operations optimization and hardware design investments meet financial hurdle rates, yet if there is a need for new growth and margins are under pressure, a shift to the right of the transaction-equity scale of figure 1 is a means for a provider to differentiate faster with a higher return on equity and enhance customer productivity at lower risk to that customer.

The ability to offer more equity-like performance-based solutions with scalable customization and ongoing interdependency presents an opportunity for traditional equipment-centric manufacturers, and the engineers who enable those designs, to find and make new value in the form of more free cash flow from customer operations.

Creating Real Option Value for Sustained and Reliable Cash Flow

Aircraft engines, power generation systems, locomotives, manufacturing plants and their supply chains, oil exploration, and healthcare systems—and the ecosystems that use them—all exist to perform a service that creates economic value, and their owners want a healthy ratio of risk and return.

Industrial value creation almost always involves the growth of free cash flow (FCF) (Stickney et al. 1991) from the use of assets over an economic life. The created FCF value is based on both present and future design and operation optimization. Future (uncertain) cash flows (of period n) are discounted to an approximate present value (PV) according to the ascribed risk (r) of achieving future cash flow (Brealey et al. 2013). The PV of future cash flow benefits minus the cost of required investments in hardware, services, and operational optimization decision support are a well-established economic metric called net present value (NPV). NPV is sensitive to the discount rate of reconciling future risk and so, just as it is important to find new productivity value, it is also important to reduce the risk in industrial systems operations of attaining that productivity in the future.

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Because forecasting is notoriously difficult, discount rates can be high. Value may thus also be ascribed to optionality, the flexibility designed into the hardware and operational decision support. Optionality is best understood in terms of improving the total probability of realizing value. There is nearly always extra upfront cost to add features now that enable industrial systems to have future operating flexibility. These features are real options (Mathews et al. 2007) because they make it possible to switch the design or operation of the asset(s) when conditions change.

Traditional FCF scenario–based accounting cannot be used to establish the value of the extra costs to build in flexibility. Multiple designs and operating points must be simulated and replicated for input variables (e.g., fuel cost, weather, interest rates) whose values vary independently. One option is to build in the capability to pursue future scenarios with the customer by including the ability to expand production, efficiently shut down or retire assets, or change what the industrial system does. These options will free up cash flow if the paths for which the optionality was designed come to pass.

There are tremendous risk reduction and value management benefits when flexibility is incorporated upfront into the design and operations optimization of an industrial system. The job of the service provider’s systems engineer and performance contract underwriters is to create both NPV and flexibility option value, especially when the provider is guaranteeing performance or outcomes over a long period of time.

The Business System Structure of Value Creation

A customer’s and an OEM’s industrial operations are likely to be complex and customized, having evolved to adapt to the technical, risk tolerances and competitive forces of their industry. To identify new economic value or reduce risk for a customer, OEM, or domain, a provider must first recognize and address the limiting factors in a business ecosystem (Johnson 2014).

It is very important to build trust with the customer in order to get to joint identification of value-reducing constraints and variance drivers. This trust will involve a deep understanding of the value creation structure and its constraints that either or both the customer and provider are working to solve. The constraints change over time and the relationship evolves to pursue new value, typically by a provider designing, configuring, and financing hardware and providing ongoing operations optimization and maintenance. In a slow-growth world with emerging substitutes this is a proactive path for a firm to create new value (GE 2012, p. 11).

Without an ability to understand and change the dynamics of the business ecosystem, there is limited prospect for growth and new value creation. Engineers do well with dynamic systems, and these business “physics” are an extension of the engineering mindset.

  Figure 2

Figure 2 illustrates a business system framework to build customer trust, identify value, and keep focus on addressing specific causal factors that may constrain value as the latter change through time. There is a central dynamic value creation structure composed of a “virtuous cycle” and a number of constraints or risks to that value. Ideally, in the value creation structure there is an economic surplus in the form of (1) ready capital that accrues from ongoing commercial activity. That capital is invested (2) in improving the firm’s capabilities as measured with key process indicators (KPIs) to (3) provide more attractive offerings. This, in turn, increases profitable business (4), which improves the rate of capital attainment. The virtuous cycle repeats and the rate of new value (1) is increased.

In some cases, however, there may not be novel ideas that are feasible for investment (5), or capital structure may limit the ability for investment (6), or some potential investments may fall below a payback hurdle rate. “Below the line” value creation possibilities may be opportunities for another party, such as (7) a service provider and its investors. Alternatively, a lack of capacity in people or skills leads to poor execution (8) in developing the great ideas a firm does have, or if there is ample capacity, its inefficient application of resources (9) limits value. Finally, a market (and the customers therein) may not understand the offered product or solution, or a significant number of customers may have an incumbent such as a lower-cost provider, or the value proposition may not be real and thus the potential for adoption is limited (10) for industrial investment. Alternatively, it may be that a market segment would adopt but the contact, knowledge building, and trust rate (11) are low.

In addition to these factors, firms have biases that either result from an underlying structure or have contributed to its formation over time. These biases may have led to certain comparative strengths or weaknesses, and it is helpful to take account of them to understand and frame industrial solutions that reinforce strengths or address constraints.

A creative bias is associated with an abundance of valuable ideas for growth, and the system constraint may be a lack of capital or ability to execute these ideas or to reach customers who can and will act on them. A firm may be strong in the execution of projects but lack ideas or marketing. Trade-biased firms may be well branded but have weak order fulfillment. And a firm that views the commercial world as a fixed pie may be more likely to forgo valuable partnerships (e.g., with a service provider or a historical competitor).

Alignment of structural constraints and biases with proposed solutions for creating value and reducing risk can help a firm better focus its efforts and uncover new opportunity.

Following are examples of problems engineers can address through their understanding of dynamical and systemic structures (Sterman 2000):

  • New ideas, or the ability to integrate or exploit new ideas and technology, are lacking.
  • Improvement opportunities with favorable returns are hampered by lack of effective organization to get projects up and running (or keep them running).
  • Work with other firms that provide comparative value is limited by a lack of trust, or the “fixed pie paradigm,” limiting otherwise valuable partnerships to the role of commoditized vendor.
  • A quantitative operations measurement system for performance attribution and risk disposition is needed to lower performance risk and enable collaboration between firms that would not otherwise have the trust or relationship to do so.
  • The customer is unaware of constraints for which the provider can shape a solution.
  • Ongoing technical services require specialized knowledge and tooling (which may, for example, be more attainable in a focused and shared internal service or offered as a commercial service through contractual agreements for maintenance and operations).

Digital Simulation to Optimize Industrial System Design and Operations

Creating value requires a means to discover where risks and returns reside in a complex industrial ecosystem. Using an example from power generation, the modeled ecosystem includes the power market, its commercial structure, operating constraints (e.g., emissions, transmissions, water, capitalization), plant apparatus, operations decisions, control, service, and finance (figure 3). A digital twin simulation model of the industrial ecosystem’s physical and operational dynamics is needed to find and manage risk and value by increasing both NPV and option value. The model will simulate the physical assets, the processes that use and consume them, and the exogenous variables that affect both the asset performance and operational decision support. Optimality may be set as the design and operational set points that maximize NPC and minimize variance from plan.

  Figure 3

As a digitally orchestrated, simulated twin of the actual plant—with design, operating scenarios, and replications that account for exogenous variations in input assumptions—the system can be computed in an optimization loop to test new designs and operations policy. The aim is to identify trapped value, enable asset sales, and/or shift the performance risk per the terms of a service solution, including the provisions for investments in new capital assets or modifications to existing industrial systems.

Risk Attribution

As commercial relationships shift from transactional to equity-based,1 different risks—usually related to asset and operational performance—shift from the customer to the OEM.

Asset performance risk is best characterized as meeting the engineering specifications for efficiency and availability. Operational risk is best thought of as the business and physical system’s ability to achieve KPIs, service levels, and financial results. Other risks may involve credit and residual value when financing is involved.

If a customer is transferring responsibility to a service provider so that it may focus its people and available capital on other aspects of its business system, then a performance-based solution very likely has value. At the end of an agreed-upon period, perhaps quarterly, the net financial results of the transferred risk are accumulated for a net payment.

The most difficult risks to structure are performance-based solutions concerning combined design, financing, and operational risks, not only because these can be separately complex but also because of interaction effects. For example, a power plant may not achieve its financial plan for a variety of reasons: an inefficiency in a key physical system or a suboptimal lineup; a poor bid; high fuel costs relative to plan; unexpected maintenance resulting from operations choices that differentially consumed asset life; curtailment of operations because of an exceeded emissions limit; ambient conditions unfavorable to efficient production; lower than anticipated asset use; unavailability of the asset at a high revenue opportunity; or flawed underwriting models related to the performance solution.

A very robust and transparent performance attribution system is needed or a contractual calamity will almost certainly result.

Because the paradigm is long term and depends on co-innovation for a shared gain, there must be a mechanism to calculate performance attribution. There must also be a means of disposition responsibility when operating performance is not achieved, whether because of a service provider’s inaccurate models; exogenous conditions that exceed the contractual range (e.g., temperature, airborne particulates, load); a customer’s operating decision that does not follow the operational decision support recommendation; and/or some aspect of the system that failed for unforeseen reasons. When outcomes fail to meet expectations, the presence of a robust and fair measuring system enables an honest relationship.

  Figure 4

In the attribution tree shown in figure 4, a first branch bifurcates for exogenous factors (e.g., temperature, humidity) that may differ from those specified in the contract. Taking the “in” (“yes”) path, the next causality of performance risk is the asset model’s accuracy to simulate the physical dynamics. Assuming it is accurate, the decision support is taken or not; if taken and the industrial system does not meet performance, it is the service provider’s liability.

Engineering’s contribution to making new value is particularly strong in the modelling of physical assets and their use in customer operations, and in the systemic management of risks and returns. The causal factors must be understood first, then a robust performance attribution measuring system must be conceived that is mutually agreeable, followed by rigorous mapping to financial results. These activities are more suited to the skills and tools of the engineering community than the accounting community.

Physical and Business Systems Dynamical Modelling: An Internal View That Flows Externally

For physical apparatus, engineers use first principles modelling to efficiently attain new design points for production and operations; a few obvious modelling examples are computational fluid dynamics, thermodynamics, materials, manufacturability, and control.

Emerging paradigms such as reduced-form and data-driven models (as in prognostics) build pattern recognition and provide the ability to characterize the health and performance of assets using data both observed and derived from simulations. Engineers also specialize in industrial simulation, operations research, and optimization of one or multiple aspects of the physical and business system.

Lifecycle models that discretize complex industrial assets into their key components and each asset’s use in customer operations are needed to find and calculate variable expenses. System of systems models orchestrate an asset’s design and operations models and reveal opportunities to challenge design points for enhanced manufacturability and lifecycle economic performance once customers use these assets.

Three initiatives illustrate GE’s research focus to address design-make-operate aspects of industrial systems that traverse internal and external operations: Brilliant Factory, Digital Twin, and Industrial Internet (also called the Internet of Things, IoT) (figure 5).

  Figure 5

Brilliant Factory aims to improve the design–product development cycle time, manufacturing, and supply chain throughput and to reduce inventory and expenses, improve service levels, and increase product quality. It integrates industrial apparatus design tools, supply chain and manufacturing-production capability, and factory operations optimization. It provides physical designs that can meet targets for operating specification, should-cost and manufacturing throughput, inventory, expense, and fulfillment.

  Figure 6

Digital Twin, illustrated in figure 6, integrates physical asset design(s) into the lifecycle customer operational design to optimize efficiency, reliability, control, operations, services, and financial performance, all of which then inform new product development, uprates to assets that are currently in service, and new services offerings such as performance-/outcome-based solutions. Combining OEM physical design knowledge with customer operations, Digital Twin seeks to enhance operating productivity and enable performance-based commercial optimization in part by augmenting monitoring and diagnostics to achieve maintenance productivity.

Predix is a platform for integrated data acquisition, security, data storage, analytical orchestration and microservices, user experience, mobile connectivity, and distributed computing that hosts data and computationally intense applications. When combined, these are solutions that can be marketed using business models (from purchased or licensed software) for outcome- and consumption-based performance services.

Investing to Enable Improvements

One aspect of internal investment in a “brilliant factory” or external investment to upgrade a customer’s operations is a need for capital today to change the system performance for higher cash flow tomorrow. Internal financing is well understood in business operations and financial services. Less understood are performance solutions that enable the implementation of an upgrade or service maintenance with financing and then realize income as use of the asset is recognized in customer operations.

Various risks in a performance-based contract are associated with investments such as those outlined above (under Risk Attribution). But with a system designed to enable asset monitoring and operations optimization, many risks are reduced and made attributable—and thus open the possibility to asset and operations financing by the OEM or a financial services entity. This embedded or bank financing, which activates new value, requires careful underwriting, monitoring, and management that would most likely be beyond the scope of scalable banking services but would be activated by the OEM/service provider’s participation and risk acceptance.

Uncovering an idea that leads to industrial system value creation is not the same as being able to invest in it. For power plants, wind farms, rail transportation, healthcare delivery, oil and gas extraction, and aviation, powerful engineering tools such as the Digital Twin identify specific opportunities for modifications, uprates, control, and operational decision support. Projects in these areas may have internal rates of return ranging from over 70 percent to less than 10 percent. Customers may have other investments with superior NPVs that preclude their direct investment, but in an investment climate where it is hard to find yield at various risk levels, there may be other investors whose preferences will make these industrial returns attractive.

One means to overcome an investment constraint is to couple risk and value management opportunities (e.g., from design and operational capitalized expense) with parties seeking alternatives with similar payoffs and durations in the financial markets. Engineers can identify such ideas with tools such as the Digital Twin and performance-based contracts that enable the origination of investment opportunities for others.

Engineering Skills Involved in Value Creation

The framework described above explains how engineers can translate the cash flow of customers and their ecosystems into what engineers design, sell, and service in industrial systems markets. Engineering education may not devote much attention to outcomes, performance, risk transfer, optionality, consumption-based operations with financing and integrated performance solutions, but these factors do ultimately affect the demand for engineering skills directly and indirectly.

Modelling skills needed for integrated systems engineering include model-based asset design and control, industrial engineering and operations research simulation and optimization, financial capital structure modelling, industrial risk management, computer science, statistics, signal processing, and data science.

The systems engineer causing new value will consider a customer’s use of assets over an economic lifecycle and optimize design and operations using real option, multi-criteria, and evolutionary approaches to ensure that customer and provider criteria achieve year-over-year performance improvement.

Beyond technical expertise, an engineer must empathize, listen, and persevere to engage the various levels of a customer’s organization as an “outsider” until trust is established. A mix of technical and interpersonal skill development is needed at the intersection of academic courses and mentorship.

The engineering profession, more than any other, can master the expanded portfolio of skills and manage the complex, embedded, and systemic operational/commercial relationship.

Conclusion

Consulting organizations have long embraced solutions that integrate technology and process. Complex government procurement contracts have shifted their bias to performance and outcomes. Industrial OEMs, however, engage a limited portion of their offerings in performance- and consumption-based service solutions because the contracts are riskier than traditional transactions and require extended balance sheet management, a lifecycle approach, and an acceptance of various risks (that may not be in their control).

The lifecycle, performance, and consumption-based solution orientation enables a symbiotic forcing function for OEM innovation. It also justifies the requisite investments that more closely address the customer’s actual value creation needs as compared to what is attained with an arm’s-length transactional relationship in which a technology provider is a vendor and value is measured in comparative cost to providers of equipment and/or repair services.

Internal and external business and physical systems can be designed, financed, and operated to create increased economic value and more assured outcomes. Two elements of lifecycle economic value creation are actual increased cash flow (from the use of assets) and probable cash flow (risk management). Engineers can help to both find value that is held back in the business system and maximize assurances that the forecasted productivity will be realized. Engineers in traditional OEMs can make new value in a slow-growth economy by working with customers to identify and move beyond constraints through dynamic systems.

References

Brealey R, Myers S, Allen F. 2013. Principles of Corporate Finance. New York: McGraw-Hill.

GE. 2012. GE Infrastructure Investor’s Meeting: Accelerating Services Growth, September 27. Available at www.ge.com/sites/default/files/ge_jri_infrastructure_ investo r_
09272012_1.pdf.

Johnson C. 2014. Thoughts on making value in America. GE Global Research 2014GRC856, August 2014, p. 12.

Mathews S, Datar V, Johnson B. 2007. A practical method for applying real options: The Boeing approach. Journal of Applied Corporate Finance 19(2):95–104.

NAE [National Academy of Engineering]. 2015. Making Value for America: Embracing the Future of Manufacturing, Technology, and Work. Washington: National Academies Press.

Norfolk Southern. 2010. Norfolk Southern and GE announce success of breakthrough technology to help railroads move freight faster and -smarter. News release, June 7. Available at www.nscorp.com/content/nscorp/en/news/norfolk- southernandgea nnouncesuccessofbreakthroughtechnologytohe. html.

Sterman J. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. New York: McGraw-Hill.

Stickney CP, Weil RL, Davidson S. 1991. Financial Accounting: An Introduction to Concepts, Methods, and Uses, 6th ed. San Diego: Harcourt Brace Jovanovich.

Vantuono WC. 2015. Dispatcher’s delight. RailwayAge, April 13. Available at www.railwayage.com/index.php/communications/dispatchers- delight.html.

 FOOTNOTES

 1 Such a shift may occur because there are limited OEM growth prospects from a customer base that is under duress or because of a devolution of pricing power in a market segment due to new entrants that offer assets at a lower cost.

About the Author:Chris Johnson leads the Management Sciences Lab at GE Global Research in Niskayuna, NY.