Analytics 50: How big data innovators reap results

Five winners of the 2016 and Drexel University Analytics 50 awards share details of their projects, lessons learned and advice.

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Mani Janakiram, Intel’s director of supply chain strategy and analytics. Intel

Mani Janakiram, Intel’s director of supply chain strategy and analytics.

Intel: Mastering supply chain analytics

Getting Intel’s chips and other products to market is a highly complex affair. The company’s supply chain is a capital-intensive global network that requires many specialized materials and complex manufacturing processes with long lead times and short product life cycles. The semiconductor giant has developed advanced supply chain analytics and saved millions in the process, says Mani Janakiram, Intel’s director of supply chain strategy and analytics.

“Intel, by nature of being the leading semiconductor-producing firm, is a capital-intensive and high fixed-asset-based company, and our capital expenditures attain a level of approximately $10 billion per year,” Janakiram says. “Critical capital equipment used in our factories may cost anywhere from $30 million to $100 million or more per tool. And a new semiconductor plant can cost upwards of $4 billion.”

Developing and mastering the analytical techniques for forecasting, planning and aligning cross-functional supply chain metrics enabled the company to save millions of dollars by, for example, avoiding purchases of capital equipment, reducing inventory levels and identifying opportunities for systemwide optimization, Janakiram adds.

Financial upside

Advanced analytics tools also helped Intel capture millions (and potentially billions) of dollars of revenue through improved customer satisfaction, increased agility and faster time-to-market, Janakiram says.

In many cases, capital planning and contracting has to happen more than two years before Intel starts producing products — well before those products are finalized. Manufacturing lead time is measured in months, Janakiram says, while customers expect changes in their orders to be accommodated in a matter of days.


Intel is a data-driven decision-making company, and analytics play a role in everything it does, Janakiram says. When it realized that its supply chain metrics weren’t well-aligned with the APICS Supply Chain Council’s Supply Chain Operations Reference (SCOR) model, Janakiram and his team turned to advanced analytics and modeling to solve the problem. They tracked, aligned and improved the key “Tier 1” metrics that steered operational excellence in the core business and provided insight into future lines of business.

“We nurtured highly skilled data scientists with an appropriate blend of business and analytics skills,” Janakiram says. “Our data scientists have expertise in operations research, computer science, mathematics, statistics, data mining, finance and business,” and they drew on their combination of business and technical analytical acumen to identify, solve and align the key metrics.

Through those efforts, he adds, the analytics team showed how it gives Intel a competitive advantage “by providing advanced data models to help our supply chain to make better and more effective decisions.”

“We regularly evaluated and employed advances in technology such as big data, cognitive computing, text mining, agent-based modeling and simulation,” he says. “We also partnered with leading universities to apply advanced analytical techniques to our metrics, as well as other complementary supply chain needs, including advanced production planning, supply chain gaming, inventory strategies, procurement and simulation modeling.”

Janakiram says it wasn’t too hard to convince Intel’s executive leadership team that the project was necessary, but that’s not always the case.

“Sometimes it’s not an easy sell,” he says. “In some cases, where the solution is new or evolving, we have to define what it means for the business. We have to show a future value add. We do a proof of concept. We go through that process to get management buy-in.”

And having that buy-in in place is important, he says, because it helps get end users to overcome their resistance to change. With stakeholders and management engaged, key decision-makers and users can participate in the process and get other users on board.

Janakiram has three tips for other executives planning analytics projects:

  1. Engage the right people.
  2. Ask the right questions. You need to learn about users’ pain points and priorities to understand the problem.
  3. Don’t get seduced by the elegance of an analytics system. Instead, focus on how you can improve the experience of your customers and stakeholders with the right analytics and applications.

Do your homework

What Janakiram and his team learned from the project was, first, to “do your homework” so you can understand the problem and, second, to learn from what others have done.

“Look at similar, like-minded companies or groups,” he says, then ask, “What are the things they had to learn that we can fast-track?”

Also, make sure you can build your system piecemeal and earn credibility along the way, he says, adding, “You need to keep feeding the beast to have the feast.”

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