In 2008 Hal Varian, the chief economist at Google, predicted that “the sexy job in the next 10 years will be statisticians.” It was an odd statement at the time, given the common perception of data scientists as computer nerds.
But Varian explained further, saying that, “The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades … Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.”
I couldn’t agree more. I call this vast availability of information in supply chain digital obesity. Inundated by terabytes of demand, supply, manufacturing and logistics information — from both inside and outside the organization — businesses are becoming overstuffed with data. How can they navigate across this ocean of information? How can they distinguish between so much useless data and the few nuggets that really matter?
Successful supply chains in today’s digital world are “sentient” — a word derived from Latin that describes things that are alive, able to feel and perceive, and to show awareness and responsiveness. Sentient supply chain means an ability to sense all available information, to intelligently turn data into insights, and to close the loop with adequate response to any changing condition.
The centerpiece of the sentient supply chain is the “decide and commit” capability, where companies make informed decisions, turning relevant insights into immediate actions. If digitalizing supply chains is the key to making sentient supply chains possible, then leveraging data-scientist skills is the key to making this new breed of supply chain actually work. A number of organizations are taking on the challenge:
- Walmart — The world’s largest retailer launched an initiative to develop a data-driven way to predict business outcomes, which it expects will drive competitive advantage. The company set up a team of data scientists that, using a machine learning tool called Quid, analyzes consumer behavior and discretionary spending to predict how the market will react to moves the company may make at the business level, or to events happening around the world.
- BASF — The Data Science and Statistics Team is BASF’s center-of-expertise for big data, providing corporate-wide quantitative decision support for multiple application areas. The team closely cooperates with business functions — such as strategic planning, controlling, sales and marketing, manufacturing and supply chain — communicating ideas and solutions based on predictive and prescriptive modeling. Topics range from statistical process and quality control to econometrics, risk assessment and market intelligence.
- Nestlè — With terabytes of demand sensing data gathered from the majority of its larger retail and e-commerce customers, the company started recruiting data scientists globally to perform demand data deep-dive analysis. The goal is to find insights and trends among big demand data that can support demand planners as they improve forecast accuracy and customer fulfillment.
- Amazon — The success of Amazon’s business model is based on extensive digitalization of its supply chain. The company continuously analyzes a huge amount of data, including millions of customer orders and billions of website clicks. It uses big data analytics to create predictive models to determine what customers want, to identify the most performing suppliers and to pinpoint the best location for its distribution centers. To make all of this happen Amazon deploys the latest predictive analytics and machine learning technologies along with an army of data scientists.
Our Future of Supply Chain survey indicates an existing trend of leveraging data scientists within the supply chain organization. In 2015 we asked nearly 1000 organizations whether they were using data scientists to manage and mitigate supply chain risk. Nearly 20% of organizations reported having data scientists in place, while roughly the same number were currently piloting the use of data scientists. And over 20% of respondents were planning to use data scientists in the supply chain organization in the next three years.
Whether you are a first mover or a laggards, you will need to take on the challenge of recruiting and retaining data scientists within your supply chain. Their availability is scarce, and there is fierce competition among large organizations to secure the best talent in the field. In an increasingly digital world, supply chains are facing a tidal wave of data. You can either crest that wave, and learn how to harness the momentum of all that information, or else risk being at its mercy.