Over the past several years, industrial and other leaders have become increasingly interested in digital transformation.
This is not surprising, given that many potentially disruptive digital technologies have emerged in recent years, carrying with them the implied promise of significant change. Vendors have started to incorporate these technologies into their products and solutions, and use cases, best practices, and solutions are becoming more widely known and available. This adds pressure for organizations to “do something” about digitization. Unfortunately, it also tends to add to the confusion.
Clearly, this widespread digital transformation will continue to accelerate and evolve. It’s equally clear that every organization will need to innovate, change, and adapt. The question is, how can organizations take best advantage of this disruptive transformation?
Let’s consider the four dimensions that any industrial organization must consider when developing a digital transformation strategy: targets and outcomes, technologies, change and impacts, and management issues.
A holistic plan will work on all four dimensions, and changes in each dimension will affect the others.
Targets and outcomes
Digital transformation involves a host of interrelated things that need to be considered. It involves disruptive/transformational technologies, but it also affects how products are designed, sourced, manufactured, sold, delivered, and serviced. New business processes, value chains, management practices, information systems, and customer relationships will have to be cultivated, implemented, and optimized (see Figure 1).
ARC has identified six focus areas that are likely to be transformed for the better:
- Operations (both operate and maintain)
- Design and construct
- Supply chain
- Business processes
Smart products, or smart, connected “things,” are often the first thing that come to mind. These products typically have onboard sensors and embedded computational and communications capabilities. They can run analytics and other applications. Designing the smart product demands knowledge of these new capabilities as well as an understanding of how the products, ecosystem, and surrounding business models will operate. Who will own the product? Who will have access to the machine health data generated? How will it be operated and serviced? How will parts and supplies be sourced? What’s the expected lifetime? All may affect the design, materials, process, and production equipment, but they also have organizational implications.
New business models, service organizations, monitoring teams, pricing models, and financing and warranty support may be required. It may be worthwhile to finalize some of these before design commitments are made.
Many organizations will target production operations rather than products for their digital transformation plans. These organizations can choose to focus on asset performance (improving uptime and reducing risk to optimize the asset value over its lifecycle), operations performance (improving responsiveness, changeovers, throughput, quality, safety, sustainability, etc.), or both.
Other organizations will focus primarily on transforming their supply chains or service offerings (see Table 1). Innovations in service offerings could be based on enhanced connectivity and monitoring machine health data or could represent a new class of service offering centered around the customers’ use of the products. These digitally enabled services bring the company much closer to the customer while (ideally) improving the customer’s results.
Because we live in an age in which so many emerging technologies have enormous disruptive potential, it would be easy to get too caught up in the technologies themselves. However, technology is an important dimension of digital transformation, so it’s worth highlighting some of the technologies that organizations should be considering.
Artificial intelligence, machine learning, cognitive computing
AI is the primary enabler and driving force behind digital transformation. With connectivity and execution, AI is already transforming industrial processes in operations, maintenance, engineering design, and supply chain. AI also powers a multitude of other transformative technologies that will drive industrial efficiency. Examples include augmented reality, autonomous machines, smart voice interface, and remote sensing.