Manufacturing Magazine - June 2021 | Page 52

SMART MANUFACTURING

Jim Bureau , CEO of JAGGAER Agility and better collaboration : the supply chain pressures brought to light by the pandemic have led organisations to crave agility and the ability to quickly respond to change . With the visibility and real-time monitoring the smart supply chain provides , manufacturers and their downstream partners can work together more easily and effectively to solve issues at any point in the supply chain when they arise . This drives efficiency , performance , stronger relationships , and resilience .

Sustainability : in the broad sense – environmental but also social and economic – has also been an urgent issue , and manufacturing organisations are increasingly becoming more intentional about developing resource-efficient behavior and really knowing the suppliers with which they work . Driving efficiencies , optimising routes , increasing transparency across the supply base through greater traceability through n-tier suppliers , ensuring compliance , and limiting overproduction , waste , and excess inventory , are just a few of the ways a smart supply chain can further sustainability .

Hong Mo Yang , SVP and GM of the Manufacturing Sector at Blue Yonder Artificial Intelligence ( AI )/ Machine Learning ( ML ): there are many use-cases for AI / ML across the supply chain . From predicting demand and anticipating disruptions , to optimising

transportation routes , resource planning and customer fulfillment strategies , AI / ML is widely implemented to drive efficiencies , automation , and enable greater visibility and integration across the supply chain network .

Digital twins : to help supply chain leaders make the right decisions across the end-toend supply chain , a digital twin is critical . By creating a digital representation of the physical supply chain , companies can leverage the digital twin to make local and global decisions , increase situational awareness , and evaluate the impact of various scenarios with confidence . More importantly , organisations can anticipate the impact of decisions to strategic business objectives such as revenue growth , margin control , and customer satisfaction targets .

Demand for data scientists : as companies continue to invest in new technologies across AI / ML , IoT and robotics , combined with requirements for companies to aggregate higher volumes of data across internal and external resources , the demand for data scientists will grow . Today , data scientists are working to solve many challenges , such as modeling data and building plans to make the supply chain more sustainable , improving response times and agility with greater visibility and control , and automating decision-making processes with AI / ML and big data to enable companies to make smarter and more strategic business decisions .

52 June 2021