In a world increasingly characterized by digital economies and disruption, every market disturbance exponentially widens the business agility gap between the less digitally evolved and those companies that have demonstrated innovation leadership through digital transformation. For those that are not transforming with urgency, the negative consequences will become compounded to the point of being untenable.
Analytics and the Industrial IoT (Internet of Things) are cornerstone competencies for digital transformation, and industrial innovators are harnessing them to accelerate their competitive differentiation. However, for many industrial companies, scalability challenges with these competencies are still limiting broader adoption, particularly by operational users. Most of the issues are organizational and cultural, though they are often viewed as technological challenges, put forward as limitations around data management, lack of skill sets and work cohesion, unexpected cost, and desire to avoid software-driven organizational upheaval.
Given that industrial digital transformation is so necessary to remain viable and competitive, why are mistakes, false starts, and dead-end investments with analytics and Industrial IoT all too common? How is it that innovation leaders avoid these challenges? This paper will outline where and why many companies go off track when thinking about digital transformation strategies, with a focus on analytics and Industrial IoT. Additionally, it will highlight the six key characteristics innovators embody that enable them to sidestep these challenges. Finally, the paper will present a starting point for how to begin to effectively implement digital transformation using analytics and Industrial IoT.
Mistaking Digital Transformation as a Technology Pursuit
Although monumental resources and investment have been applied to digital transformation, the effort has not equaled success. Data is still hard to access, organize, and use. Leaders struggle to understand how to connect strategy to execution. Workforce and organizational culture barriers remain. Return on investment is difficult to demonstrate, leading to an endless cycle of vendor testing. Where investments are made, use cases often don’t scale as anticipated. These issues are compounded by the realization that what works for one use case often doesn’t translate to others that seem similar on the surface.
As demonstrated in the visual above, digital transformation presents many possibilities and starting points – plant floor, supply chain, engineering, smart services and products, etc. The opportunities often require a range of disruption to traditional ways of operating, from the potential for massive revamping of business and work processes to completely new models for customer engagement.
Making sense of these myriad opportunities requires some means to filter decision making on which path(s) to take, why, and in what order. As organizations contemplate the breadth of these opportunities, a natural line of thinking arises: a technology or set of technologies (e.g., platform) can be identified and purchased to drive all or most aspects of change.
As a result, conversations become technology centric. Digital transformation turns into the pursuit of the silver-bullet solution or a proof-of-concept of the latest-and-greatest technology. Striving to get a potentially high-risk decision right, organizations look to compare solution techniques, tools, and technology architectures in “apples-to-apples” ways, even when that’s not possible.
ARC Advisory Group clients can view the complete report at ARC Client Portal
If you would like to buy this report or obtain information about how to become a client, please Contact Us
Keywords: Digital Transformation, Analytics, Machine Learning in Manufacturing, Artificial Intelligence in Manufacturing, Quality in Manufacturing, Industrial IoT (Internet of Things), Operational Excellence, Competitive Excellence, Digital Strategy, ARC Advisory Group.