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The Internet of Things—Five Key Takeaways for Supply Chain Innovation

Posted by Jeff Bodenstab on Oct 28, 2014 4:30:00 PM

Our previous post on the Internet of Things cited the approaching wave of Big Data—and the demand analytics that make sense of all the bits washing ashore. Here are five essential takeaways:

1. More data and advanced analytics are changing the game in the supply chainpromising an increase in forecast accuracy over traditional statistical algorithms.

Companies want insights they can act upon confidently, knowing they are based on data that has been properly analyzed. This data can come from anywhere and everywhere, deepening demand intelligence by gathering more signals for the analytics engine.
Lalit Panda, in a Wall Street Journal CIO Journal article titled, Companies Fuel Supply Chains with Big Data, Sensors for Competitive Advantage, writes, “The capability to combine (external) unstructured information … with transactional information within an organization, promises an increase in forecasting accuracy over the purely statistical algorithms currently in vogue. This capability takes pattern recognition to a higher level.”
2.  It’s no longer about forecasting “one version of the truth.” Big Data let you consider a range of possible outcomes.
“One version of the truth” is an aging mantra. Lora Cecere, in her Supply Chain Shaman blog, advises that it is more critical “to learn to use demand data than to make the demand number perfect. The discussion needs to be less about the “demand forecast number” and more about the probability of demand. Companies need to try to reduce demand error to the extent possible, but realize that demand error is a reality of managing a supply chain. ….. This requires using new forms of analytics for inventory optimization and network design and doing less on spreadsheets.”

Panda agrees, suggesting it’s no longer about bringing divergent viewpoints together into one version of the truth to drive Sales and Operations Planning (S&OP). He says that big data technologies, sensors and telemetry offer a rich source of potential insights.

Costa Express, highlighted in a previous blog, is a good example. At Costa, machine telemetry feeds real-time data for demand sensing and replenishment from 3000 self-serve coffee locations. By the way, Chris Clowes, supply chain manager at Costa, will be a featured presenter at Lora Cecere's upcoming Race for Supply Chain 2020 web seminar.

3. Sensing data yields a more responsive supply chain, particularly with volatile demand.

Three_Steps_to_Market-driven_ForecastingPanda says, “RFID chips, point-of-sale systems and beacon technologies generate volumes of information that were once difficult to analyze.” Demand analytics can now do that heavy lifting. He adds that, the two-way flow of information via the Internet of Things is particularly crucial in “enterprise operations where production may need to match highly variable demand patterns.”

4. Sensing data also reduces the bullwhip effect.

h and larger swings in inventory in response to changes in customer demand—is caused by belated response to demand. Analyzing Big Data “extends the ability to respond, to predict and, in some cases, even recommend subsequent action,” Panda says. Supply-chain strategy is evolving from “‘supply to forecast’ to ‘supply to prediction based on dynamic pattern analysis.'"

5. Machine learning is key to adaptive supply chain planning.

Cognitive computing is “learning” computing, well-suited to the complexities and ambiguities of adaptive planning. Combined with an expert planner’s “insights of how to respond to specific situations, these technological solutions could enhance the overall responsiveness of the business,” Panda says. Cognitive computing is key “where there are numerous variables to be considered simultaneously.”

Next week we’ll explore a new approach to demand forecasting called “Demand Modeling”.

To better understand how to take advantage of the explosion of already available data in your forecasting environment, check out the infographic and whitepaper below.



Topics: Forecasting Demand and Analytics

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