From e-commerce to sprayable sensors to smart factories, technology – and its applications in distribution – continues its rapid expansion of possibility. But at the same time, this accelerating pace makes it even more difficult for even the best companies to keep up.
The key: Make sure you're trying to solve a problem – and that that problem is the right one to solve with the technology, said Michael Steep, executive director of the Stanford Center for Digital Cities and Emerging Technologies, at the 2017 NAW Executive Summit in Washington, DC, earlier this month.
"There are several transformative technologies that are on the verge of greater impact to distribution," Steep said. Predictive analytics, for example, sounds great to distributors right now, but most still struggle with developing a strategy for implementation.
"Data is no good without actionable insights," he said. Distributors have the data – mountains of it from all different sources – but they don't know how to effectively and efficiently mine it for those insights. A critical step is hiring people with an analytics mind set to start sifting through the data.
Advances in machine learning and artificial intelligence may help kick-start that process, as well. Machine learning allows computers to learn from the tasks it has been programmed to do. The program uses the data inputs to extrapolate and predict outcomes without requiring direct human input. As a result, the machine can provide warnings of potential problems or determine more efficient ways to produce, for example.
But you have to provide direction. If you want answers, you have to know the questions, said Kathy Mazzarella, president and CEO, Graybar, St. Louis, MO. Mazzarella also spoke at the NAW Executive Summit. "Effective use of data analytics doesn't happen by throwing people at a pile of data and saying, 'What do you see?'" she says. Don't waste time looking for a solution without a problem.
And remember, the data may reveal a problem/solution that doesn't fit your preconceived notions; be open to information that challenges your beliefs.
"Traditionally, executives have managed based on what they know," Mazzarella said. "Data analytics requires a shift to what they can learn." Knowing has an absolute set and end point; learning is an ongoing process. Even if a new process fails to return the desired results, there's opportunity to extract information that can be used in the next attempt.
Ultimately, effective implementation of technology requires a cultural shift that embraces managed risk to try new things and identify new solutions. And the rewards can be tremendous.
How you implement technology can have a huge impact on its long-term success. Join us for a free MDM Webcast on Thurs., Feb. 23 at 1 p.m. ET to hear how fastener distributor Specialty Bolt & Screw's strategic approach laid the groundwork for sustained profitability.
