Latest Everest group research shows a preference for global in-house captive centers (GICs) for business process service delivery in industrial manufacturing. The research also indicates that while most GICs delivered on initial objectives of cost reduction and reliable service delivery, few have achieved the scale and maturity to deliver value beyond these base-case objectives. Two steps for transformation: a more robust business case, and a more contemporary yet practical approach to technology- and analytics-driven operating models.
A late starter in the adoption of shared services and outsourcing, the industrial manufacturing industry has traditionally gravitated toward global in-house captive centers for business process service delivery. Most industrial manufacturing companies, as they journey to transform operating models, focus on cost savings and reliable service delivery, and start by moving routine, transactional work to GICs in lower-cost locations. But research by the Everest Group (“How Can GICs Partner with Service Providers to Create More Value for their Enterprises?”) indicates that, while this strategy has largely delivered in terms of cost reduction andreliability, few in the manufacturing industry have evolved beyond these initial objectives. Arguably, assessing advanced operations solely through the lens of baseline-cost reduction may have created a bias toward suboptimal choices in operating models.
GICs are theoretically well placed to provide enhanced controllership, integration with corporate culture, and access to dedicated resources, but lack of scale, suboptimal talent management, and limited access to external best practices often restrain their ability to truly become business partners capable of driving innovation, expanding scope, reimagining processes, and investing in advanced technologies and analytics. As a result, a majority of the GICs continue to be leveraged solely for reliable service delivery and basic cost savings. With digital technologies and the Industrial Internet of Things (IIoT) opening up opportunities for new cost efficiencies and business models, manufacturers must evaluate whether their operating model is (a) designed and (b) being run, so as to tap into those opportunities by owning and driving business outcomes such as revenue growth and customer experience.
Strategic operating model choices start with a better business case
As the industry prepares for the next level of growth in an increasingly uncertain business environment, the expectations of target operating models shift toward driving effectiveness and standardization, addressing complexity, and creating revenue impact. Over 70% of GICs indicated this shift in the Everest Group research. In this environment, leaders must leverage accumulated learning from other industries, as well as early movers within the manufacturing industry. The advantages and disadvantages associated with in-house captives, outsourcing to service providers, and hybrid models are now well-understood. Analytics on historical performance of these alternatives can robustly estimate the full value1 of these operating model alternatives, while also taking into account the financial significance of value-added expectations. In our experience, any estimate driven purely by baseline cost risks underestimating the value potential by as much as 60% (Figure 1).
Alternative delivery models provide different benefits due to variation across compliance and control, ease of setup, integration into business, and intellectual property provisions – to name just a few. Making appropriate operating model choices requires that the decision be based on realistic expectations, taking into account mitigating factors, such as variance in executional capabilities, organizational culture and maturity, the scope and nature of work, and the level of stakeholder engagement, among others.
Making agility a cornerstone of operations with a Lean DigitalSM approach
Selecting the right operating model alternative is only the beginning. The (a) rapid rate of change in the business environment, combined with (b) the now mainstream adoption of advanced technology (including IIoT) and analytical capabilities, can ruthlessly test business cases assumptions on which operating model choices were based even six months ago.
The future priorities of GICs—driving effectiveness and standardization/efficiencies, delivering complex work, and creating revenue impact—require not only accelerated industrialization of back-office operations (F&A, S2P, HR, sales and marketing, master data management, aftermarket services, reverse logistics, asset optimization, engineering, and value redesign) but also meaningful assimilation of fast-evolving digital technologies and analytical capabilities.
In our experience, established manufacturing organizations are hamstrung by legacy processes, technologies, and ways of working. Firstly, many GICs lack sufficient understanding of end-to-end process flows or intersections between multi-function processes (e.g., order to cash, engineering, and aftermarket services). Secondly, a singular focus on cost and reliable day-to-day delivery restricts organizational capability for analytics-and innovation-led experimentation. Thirdly, the “waterfall” methods traditionally used to design, run, and transform operations in GICs are too rigid to quickly respond to changes in an operating environment, and require significant change management capacity and capabilities. These are just three of the “legacy practices” that GICs often suffer from.
Remarkably, Lean practices, a classic set of concepts derived from industrial manufacturing practices pioneered by Toyota and General Electric (GE)—is at the core of the agile startup practices that are better able to cope with, and even thrive in, hypercompetitive, dynamic markets characterized by fast-paced technological change. Lean could bring manufacturing inspired innovation back to industrial manufacturing environments – especially in the era of digital technologies.
Lean’s core principle is to maximize customer value while minimizing, but not necessarily eliminating, waste. Both tenets are particularly useful for large enterprises. On the one hand, customer value can illuminate the true north and allow for simpler operational architecture that transcends boundaries related to process and function. On the other, seeking to minimize waste provides large enterprises with a practical lens to reduce the displacement of existing legacy processes and systems.
This is what we call Lean DigitalSM. It provides large enterprises with a practical transformation and process re-design approach in the context of digital technologies. Instead of defaulting to the kind of incremental process improvement often pursued by GICs, or a wholesale rip-and-replace of numerous systems, the Lean DigitalSM approach tightly aligns the focus of intervention on the key end-to-end Data-to-Insight-to-Action steps that deliver on intended business outcomes. It allows organizations to complement existing systems of records with process-centric SaaS solutions like Systems of Engagement to realize rapid time-to-value in optimizing operational performance and business agility.
An example illustrates how Lean DigitalSM makes manufacturing operations intelligent—able to sense, act, and learn from the effect of their actions at scale.
Industrial equipment providers can leverage sensor data for constant monitoring of the most mission-critical machines (such as expensive rotating equipment like turbines), or for as-needed checks that would have previously required a visit to a service center (as in the case of heavy trucks). Additionally, the Data-to-Insight cycles of advanced analytics solutions like GE’s Predix help reimagine the servicing processes that engineers perform by providing them with guidance ahead of time on the procedures needed. This also offers an opportunity to better orchestrate the supply chain of specific parts needed in specific repair shops at specific times.
Lean DigitalSM, combined with the specialized expertise of third-party service providers who have access to specialized skills, global talent, and cross-industry best practices, helps manufacturing enterprises evolve their operating architecture to realize a much more advanced and agile operating model—Intelligent Operations–built on specially designed, smart processes that are able to sense, act, and learn from the outcome of their actions, at scale.
The pace of change in industrial manufacturing organizations’ business environment has undergone unprecedented acceleration with the advent of IIoT and digital. At the same time, the evolution of operating models has been far more gradual, with organizations mostly focused on moving transactional work to GICs in lower-cost locations.
In order to address the new opportunities and fend off today’s challenges, industrial manufacturing organizations need to reimagine their operations architecture. To do so, they must more holistically assess the value sources to determine the most suitable operating model for achieving their business objectives. They must also factor in the need to adapt to volatility, their own changing business needs, and the power of being able to capitalize on the opportunities offered by emerging technologies and analytics tools in an agile way.
While all the expertise necessary to architect and run such operating models may not exist in-house, potential partners have significant accumulated experience from the almost two decades since the first captives emerged. Experience in the widespread use of process redesign, technology, and analytics in operations has been distilled into practical methods that productively harness digital technologies, such as Systems of Engagement and analytics, to construct advanced organizational models and leverage them to deliver enterprise-wide impact. The resulting Intelligent Operations constitute a more rapidly attainable, yet scalable and cost-effective, business process platform, built to adapt.
For more information, contact, email@example.com and visit, genpact.com/what-we-do/industries/industrial-manufacturing