For the past eight years, I have been delivering lectures on supply chain management at the University of Texas at Dallas. This has given me a unique opportunity to interact with students pursuing their master’s and doctorate degrees in Industrial Engineering, Computer Science and Business Administration. Lately, I have noticed a step function change in students’ skills and competencies. Students are increasingly asking questions like: “How can an organization incorporate unstructured data from social media into their demand planning? How can an organization predict a delayed shipment and incorporate that into their supply planning?” When I was a student almost two decades ago and pursuing my master’s degree in Industrial Engineering at Purdue, supply chain management was not even a separate discipline in the program. Instead, it was baked into the Industrial Engineering program, which included concepts like the bullwhip effect, newsvendor problem, theory of constraints, and linear programming optimization. While these core concepts remain an integral part of the curriculum today, the very backbone of the supply chain has been shaken by the digital revolution. It is nice to see that universities have also evolved their curriculum to meet the needs of this digital revolution, as evidenced by new courses pertaining to data sciences, artificial intelligence (AI), machine learning and deep learning, among others.
Recently I had the opportunity to deliver a lecture on digital supply chains to my students at the University of Texas at Dallas. I discussed how even though the business processes of demand planning, supply planning and inventory planning remain the same, what has changed in this new digital world is an exponential increase in the volume, frequency and speed of data. Now organizations have an opportunity to harness this new data through digital assets such as social, news, events and weather (often referred to as SNEW data) and physical assets (such as radars, sensors, smartphones, etc.) to reinvent, redefine and reimagine current business processes and practices.
This ability to ingest real-time digital data signals from the edge into demand planning, supply planning and inventory planning systems will provide organizations with predictive visibility, so that they can sense a delay or disruption before it happens. Organizations can then leverage prescriptive analytics to mitigate that risk, with the goal of improving the customer experience and creating significant competitive advantage. I’ve noticed that students are increasingly interested in learning how to incorporate social sentiment from Twitter feeds or Google search data into planning systems. When these students graduate, they will likely want to work for organizations that have technology that enables them to pull this type of data into their supply chain plans. This reality is just one more incentive for organizations to invest in their supply chain digitalization efforts – otherwise it may affect their future recruiting efforts.
As part of the Intelligent Manufacturing survey, JDA collected responses from 271 U.S.-based professionals across the manufacturing and wholesale/distribution industries about their supply chain digitalization efforts. Nearly half of the companies surveyed (46 percent) are aggressively pursuing supply chain digitalization strategies and technologies as a major initiative in 2018 for enhancing and redefining their supply chain processes. In fact, supply chain digitalization is no longer a “nice to have” capability, but has become a strategic mandate. Further, in direct response to the rapidly expanding influx of digital data available from sources such as the Internet of Things and SNEW data, 78 percent of survey respondents plan to invest in new talent over the next 12 months to better utilize these insights.
But where should organizations look to find the right supply chain talent? Dana Stiffler, Research VP at Gartner, addressed this topic in her keynote speech at the 2018 Gartner Supply Chain Executive Conference. During her presentation, she highlighted some statistics on the dearth of supply chain skills and competencies when it comes to data sciences, AI, machine learning and deep learning. She mentioned that from the Greater Chicago area – which has a population of approximately 3.6 million – there are only about .15 percent of people with production planning and forecasting skills, which further reduces to only 0.0001 percent of people when including AI and machine-learning skills. Yes, you read that correctly: only 0.0001 percent of 3.6 million people have skill sets that include AI and machine learning. This is just a glimpse of the digital supply chain talent gap.
As supply chain professionals, we work in a challenging industry with scarce resources, rapidly changing demands, a growing need for speed, agility and flexibility, and the best strategic business minds to keep our organizations moving forward. Yet, top talent is hard to attract and even harder to keep. Upskilling and reskilling of our people is a massive task that we must all be involved in if we want to keep our workforce engaged and productive. Partnering with universities and educational programs, and recruiting students from these universities, is a great way to combat the digital supply chain talent gap and prevent it from growing into a crisis.
For additional information, learn more about what a digital supply chain looks like for the digital age, how manufacturers are battling the Amazon effect and how digital technologies will be key to manufacturers’ future success.