The Dirty Little Secret About Digitally Transforming Operations
Amazing dreams were being sold: A black box that could be installed in your plant and would improve your competitiveness — all by itself; big data servers and algorithms that would tell you how to improve your process — with no additional engineering investment; virtual-reality glasses that would make your workforce more productive — just by putting them on.
It was all reminiscent of 19th century advertisements for cure-all patent medicines.
There is a common misconception that technology alone can produce magical results. But the reality is the results depend on how people use it, particularly if they can use it to amplify longstanding skills and expertise. And that’s often when organizations run into problems. Thomas Froese, leader of data-analytics-and-modeling company atlan-tec Systems, has 25 years of experience in helping companies apply advanced analytics. He summarized the issue very simply: “We can automate mathematics, we can automate design decisions, but we cannot automate changes in human behavior.”
- Sponsored by AccentureThe technologies and processes that are transforming companies.
Even today, our own research confirms that only 26% of major organizational transformations succeed. Technology-based transformations involve a similar commitment: Froese confirms that helping people learn to use analytics effectively can take up to 75% of a project’s time — with the remainder going to the technical tasks of data cleaning and model design.
So what have we seen in companies that are successfully transforming themselves with new technologies?
In addition to the traditional transformation success factors noted above, we see four major differences between the success stories and the also-rans.
A focused perspective. Determining where to start among thousands of potential use cases is not easy, but a balance of expected impact, technological readiness, likelihood of success, and acceptance by the organization usually provides an effective initial screen. For industrial players, this set of criteria often turns attention to areas such as predictive maintenance, advanced planning, and sales optimization — all functions that cover large parts of the cost or margin base, are well measured, and show improvement potential beyond what can be reached with traditional improvement techniques.
A two-speed trajectory. Setting up an entire IT architecture or automation infrastructure is akin to installing a new ERP system: a multi-year journey that often costs hundreds of millions of dollars. To avoid this roadblock, successful companies use a more agile strategy that relies on local solutions to capture value quickly, while gradually building the long-term IT and automation architecture. Rapid impact generates excitement and buzz while providing lessons that can be codified and shared, ensuring a better outcome for the enterprise as a whole.
A translator for a bigger, broader team. Capturing the digital opportunity is becoming even more of a team sport. In addition to the IT and topic experts that major IT projects required in the past, today’s efforts in complex fields such as resource-productive manufacturing or predictive maintenance need deep expertise in production processes, data science, and change management.
A commitment to helping people change. Because these innovations can have a major impact on how people work, it’s essential to anticipate people’s concerns and build a persuasive case for the new approach. A major train operator, for example, designed an advanced analytics solution in order to better understand the failure behavior of a major battery pack in one of its train models. The technical solution was the easy part: Sensor data accurately predicted battery failure just weeks before it actually occurred. As a result, the company could abandon its rigid (and wasteful) cycle of changing batteries every two years.
The real challenge concerned the implications for employees. Instead of large crews disassembling the packs at centralized workshops, small teams would replace the batteries in the field as needed. That would free capacity for other types of maintenance tasks but would unwind longstanding work arrangements. Thinking through how to deploy employees under the new solution — and defining the benefits that employees would feel — therefore took substantially more effort than designing the technical solution did. But by emphasizing the greater flexibility, new learning opportunities, and advancement prospects that the new model generated, the company was able to increase employee satisfaction even as it achieved significant cost savings. And it was the combination of the analytics with people’s expertise that ultimately mattered — by allowing people to spend more time on tasks that were a better use of their skills.
So when leaders are thinking about their organization’s digital priorities — its digital strategy — they should obviously identify the technologies and ways to apply them that would have the greatest potential impact. But they should also make sure that they don’t give the possible barriers to adoption short shrift. They should have a plan for helping people use the new technologies and the related new methodologies more effectively. Remember that technology alone is not a cure-all. It’s the people applying the technology in their daily jobs who will create the additional value.