Working together in a happy, cooperative supply chain is more aspirational than reality for most companies. Although everyone pays lip service to working with vendors, shippers, and customers and sharing data, the reality is much less interactive than the rhetoric.
There’s just too much to do in the daily tasks of moving product in and out to spend time and effort sharing data with other companies beyond what’s absolutely necessary. There always seems to be good reasons to share data about location, condition, quality, and so on, but there’s always a more important reason to put off the cross-company integration work for another day.
This is where some smart groups will profit in the future from a little effort in the present.
Sensing the Future
So much is made of knowing when items are going to arrive at their destination that sometimes other factors aren’t communicated or considered useful. With the aid of the Internet of Things (IoT), data streams, and advanced analytics, there are many areas where sharing data between partners in the supply chain will benefit both parties and is easier than ever.
Today, companies are collecting more and more data about their products and production machines as well as about products from their vendors. Unfortunately, there’s almost no standardization of what format or which data elements need to be collected. Overcoming this hurdle is the first step.
Why Is It So Hard?
The hurdle involves attitudes and priorities. People are naturally inward looking and focused on their own tasks, and this translates to companies. Sharing data means giving up control and perhaps not getting the same payback others will. Combine this self-interest with a general lack of knowledge about the IoT and predictive analytics, and most managers easily put off data sharing and analytics.
Fixes and Upgrades
As the IoT and the data it generates become more pervasive, widespread, and detailed, companies will have a natural drive to do something with the data. Beyond the obvious task of providing real-time activity monitoring, aggregating and analyzing these data give companies and supply chain groups a platform from which to predict upcoming events and react to them before they even occur.
Companies need to partner with their vendors to coordinate the data being generated by items that ship between them. Sharing requires standards and standardization. With standardized data come the ability to collaborate. This process requires better sharing systems, both hardware and software. More importantly, it requires different skills—more analytical skills than operational.
Big Potential Benefits
Right now, companies share information focused on what’s being sent and when it arrives. In the near future, predictive analytics models will look at many other characteristics and data elements.
Here’s one example: In the future, predictive analytics models will look at the quality of the products coming in. Elements like purity, failure rates, or other applicable quality measures by batch could change how the upstream part of the supply chain conducts its processes.
The near–real-time data flow and sharing feeds predictive analytics models, which can change production scheduling with finer detail. With so many components in transit, being able to predict even minor delays based on history is useful. The best part is the automation and constant updating of artificial learning models. As products, components, and vendors change, predictive analytics models evolve and learn.
Power Up with Upping Labor
Sharing data and having predictive analytics make smart decisions lessen the labor burden and the level of experience the organization needs. Sharing data with partners gives those predictive analytics models more visibility and lets them make changes to operations with greater lead time and accuracy. Thus, working with supply chain partners today will yield smarter partnership tomorrow.
About the Author
David Gillman has worked in software and technology-related services for more than 20 years. He has been a user and administrator of CRM systems as well as a pioneer in analytical CRM. David currently works on hands-on analytical CRM projects in several industries, publishes on the subject, and is a speaker at industry conferences. David is an analyst with Studio B.
ERP; supply chain partners; predictive analytics
Source: SANS ISC SecNewsFeed @ February 23, 2017 at 01:09PM