How manufacturers can adopt a trial-and-error mentality to catch up on the data-driven challenge.
Leveraging the power of fresh, granular data for business success is becoming a question of life or death – and not just for companies that operate primarily in technology- or internet-focused fields. In fact, CEOs in all industries, including traditional “old-line” businesses such as some manufacturers, are realizing the power of “data-driven transformation,” or embedding data throughout the company’s operations. And they would certainly like the 20% to 30% EBITDA gains that their peers are racking up by using data effectively in sales, marketing, supply chain, manufacturing and R&D.
Yet CEOs are right to wonder how their organizations – where managers and executives already complain about a lack of data skills and overburdened IT systems – can pull off a data-driven transformation. Several roadblocks stand in the way. For instance, firms in many industries – including manufacturing – tend to employ a methodical, process-oriented approach to adopting new technology, and the timeframe for a transformation effort can be years. That slow-simmering approach is generally at odds with the rapid development cycles of data technology, and puts firms at a disadvantage compared with nimbler competitors. It also risks wasting immense amounts of time and money.
In our experience, the most successful data-driven transformation efforts are cost-effective, incremental and sustainable. They follow three steps. They start with pilots that pay off in weeks or months, followed by a plan for tackling high-priority use cases, and finish with a program for building long-term capabilities.
Use Quick Wins to Learn and Fund the Data Journey
Rather than attempting sweeping, across-the-board change, successful companies start with pilot projects that address specific challenges—improving efficiency in vehicle scheduling, for example. These quick-win projects not only teach critical lessons, but also create new value to fund other transformation projects.
For example, a large industrial company built momentum for transformation through a series of pilots that demonstrated to the rest of the company how data-based operations can succeed and pay off. The first initiatives it selected were in inventory management and capacity optimization. The projects led to significant savings and more sales of high-profit items. In nine months, these quick wins generated $20 million in value.
Design the Companywide Transformation
Using lessons from these pilot projects, the company can then draw a roadmap for the companywide transformation. This starts with a high level vision, which the company translates into a portfolio of initiatives to be rolled out in a logical order on the basis of factors such as potential payoff, urgency and competitive pressure. Then the company must agree on some underpinnings of digital operations – analytics, data governance and data infrastructure. During this phase, companies also work on industrializing their data and analytics. Since digital systems are the new means of production, they need to have all the attributes of industrial machinery, including reliability and consistency.
Using this approach, a major logistics company rebounded from a major misstep. Its first attempt to become data-driven involved a top-down IT systems overhaul – one that cost time and resources, and that failed to deliver any operating improvements.
The second time around, the company took a new, agile tack. It created a detailed roadmap for transformation based on two primary considerations: (1) an examination of the data needed monthly, weekly and in real time to optimize functions and operations and (2) the assessment of systems and data already available to fill the newly identified business needs. On the basis of this roadmap the company began a series of pilot projects to optimize important cost drivers such as fuel consumption, maintenance, labor and pricing performance by accessing data such as customer P&L through new analytics. After dozens of projects in areas such as pricing, fuel consumption and network the company now leads its industry on EBIT performance.
Organize for Sustained Performance
As is the case with any change program, the success of a data transformation is measured by sustained results. To prepare their organization for a digitized future, companies need to move on four fronts: creating new roles and governance processes, instilling a data-centric culture, adopting new ways of working, and cultivating the necessary talent and skills. Many companies may be capable of managing this change on their own; but if a company faces competitive challenges that require a rapid transition, or if it is far behind in digitization or lacks the resources and capabilities to manage the transformation, it may benefit from adopting a build-operate-transfer model. This involves creating a dedicated organization—usually run with the guidance of an outside expert partner—that takes over the organizational change effort.
Many CEOs wonder how their organizations can pull off a data-driven transformation. The good news is that this three-step model can help a company move fast, get quick wins, and at the same time create a transformation roadmap that will make the gains from digitization stick.
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