Software is a future supply chain link

This Q&A with ARC Advisory Group's Steve Banker covers software's increasing role in supply chain management.

By Plant Engineering and Steve Banker March 5, 2018

Managing risk and speed in an evolving manufacturing process requires a strategic approach, and it will require a new generation of software to manage that strategy. Steve Banker, vice president of supply chain management for ARC Advisory Group, recently released a study discussing the latest software trends in supply chain management. He talked about those findings, and their future implications, with Plant Engineering content manager Bob Vavra:

Plant Engineering (PE): In your research discussing the next wave of supply chain software planning, you describe a “new supply chain planning technology wave.” Where are the opportunities in supply chain planning to improve, and how will better data help that take place?

Banker: SNEW data—social media, news, event, and weather data—has great potential to improve supply chain capabilities in three ways: improved forecasting, risk detection and response, and dynamic optimization.

The solution is already proven in terms of enabling enhanced supply chain resiliency capabilities. In the other areas, leading supply chain software suppliers have interesting product development in this area.

PE: What are the implications for manufacturers and suppliers in a faster supply chain? More importantly, are manufacturers ready to respond to it?

Banker: The planning-to-execution handoffs can be better optimized. For example, a supply planning solution grabs orders and creates optimized plans of which plants should make products for which customers. A transportation management system (TMS) then grabs the associated transport orders and creates optimized plans of how shipments should be routed in the coming days. Eventually, these plans are executed.

Now there is the possibility of taking all planned manufacturing and transport moves and sending them to a SNEW engine with predictive ETA capabilities. The engine can view origins, routes, and destinations and predict that certain inbound raw material loads or outbound finished goods won’t arrive on time because of things like road construction, a very large sporting event, or weather.

A large manufacturer might send thousands of shipments for a planning horizon and perhaps only 1% or 2% are flagged as being at risk. The supply planning and TMS solutions can then dynamically re-optimize those production schedules and loads.

What do manufacturers need to do to get ready for this? Basically, stay tuned. This is vision; there are no customer references for this type of solution yet. This type of solution is being actively developed, however.

PE: ‘Machine learning’ is another buzz word right now. How will supply chains and manufacturing lines work together as this technology begins to roll out?

Banker: Machine learning is a great technology to improve the predictive and scheduling capabilities of Big Data sets. SNEW is a Big Data set. In short, Big Data and machine learning are partners in exploiting the new capabilities that are being visualized. Artificial intelligence and machine learning are also being used to make implementation easier, risk management more proactive, and also improve the usability of these solutions.

But again, in most cases, these new capabilities have not yet been baked into the standard products.

Steve Banker is vice president of supply chain management for ARC Advisory Group.