Proving the Business Case for the Internet of Things

Ingram analytics engine helps supply-chain decision making

Steve Rogerson
May 12, 2015
Ingram Micro Mobility has introduced an analytics system that can take a user’s data and connected-device lifestyle trend and behaviour analysis to help decision making by the supply chain team.
The aim is to help solve supply chain problems, cut costs and increase the supply chain's total value by delivering insights into device lifecycle statistics and analysis.
For businesses in the mobility and hi-tech industries, the enormous cache of digital data available through point-of-sale encounters, insurance claims, returns data, failure codes, data usage, recycling revenue and repair cost has made traditional analytical tools ineffective. Additionally, by combining these data with market intelligence and customer profiles, Ingram Micro Mobility's system helps reveal gaps in processes and services, contradictive actions, and opportunities for cost savings.
"Each year, consumer returns cost carriers tens of millions of dollars," said Bashar Nejdawi, president of Indiana-based Ingram Micro Mobility. "We modelled the advanced analytics solution after the health IT field, recognising that we could use the same type of heuristics to predict future behaviour and make accommodations early – ultimately reducing cost, while increasing customer satisfaction."
Customer and retail integration connected within the Ingram Micro Mobility data ecosystem help fuel the advanced analytics system, allowing for real-time supply chain analytics at the point of delivery and distribution or return and repair to support operators' routing decisions. Upstream automation partners with analytics to drive returns avoidance.
The system can predict customer behaviour by analysing real-time data on device upgrades, trade-ins and buyers' remorse returns. It allows the development of a smarter supply chain by more accurately addressing the amount of product needed by store, region, country and so on. This helps to determine more accurately total device lifecycle cost from cradle to cradle.
Designing for better serviceability becomes possible with information on how much a device will cost to fix or maintain. It allows the collection of returns data, along with failure modes, to drive training and enhance repair capabilities. Unknown conditions can be identified in the supply chain within retailer, carrier or OEM populations to prevent conditions from developing, leading to greater customer satisfaction and cost savings.