In Part 1 of this series we explored the essentials of deploying a live supply chain. In Part 2 we look at why data scientists will be increasingly key to supply chain success.
When it’s completed in 2030, the Square Kilometer Array will be the largest telescope ever built, and will capture 35,000 DVDs of data every second. When astronomers showed off an early iteration in July 2016, they pointed it at a moon-size section of sky. What did they find? Nearly 1,300 previously unknown galaxies.
Supply chain operators can be forgiven for feeling like those astronomers. The trove of new data they’re capturing — from business systems, IoT devices, social media, and so on — has the potential to transform their views of customers, suppliers, manufacturing, logistics, and more. But making sense of all that data can be more than challenging. For that, they’ll increasingly need data scientists.
From business as usual to business-critical
Actively managing supply chain performance has never been more business-critical. Globalization, regulatory requirements, technology complexity, volatility of supply and demand, and greater dependence on suppliers have all increased business risk. The only way to make sure the supply chain operates in a way that meets customer needs and drives business success is by leveraging data in as close to real time as possible.
Increasingly, that data will be both structured and unstructured. Structured information from business systems includes traditional transactional data such as purchasing, production orders, and sales.
But you can’t operate a truly real-time, or “live,” supply chain without unstructured data. And that will come from a variety of sources. The rapidly falling cost of IoT technology means you can embed sensors in everything from production equipment to low-cost consumer goods. Social media can contribute customer sentiment about companies and products to help you sense demand, risk, and opportunities. Crowdsourcing apps can let you track everything from weather to traffic to holiday spending.
Data scientists to the rescue
In the meantime, logistics operators are grappling with an aging, shrinking talent pool. Logistics employs 6 million people in the United States, but it will need another 270,000 new workers per year to keep up with growth. At the same time, 60 million baby boomers will exit the workforce over the next nine years, but only 40 million younger workers will replace them, according to U.S. Census data.
It’s no wonder 79% of participants in the 2016 Third-Party Logistics Study feel unprepared for the impact of the labor shortage on their supply chains. And only 38% of executives are “extremely or very confident” their supply chain has the competencies it needs.
In particular, a live supply chain requires the data scientists — and technology — that can wring the most value from your data. That starts with identifying relevant data sources, figuring out how to capture the data streams, and understanding how to harmonize it at the most granular level. It continues with the ability to parse useful information from data noise, and to analyze the useful information to extract new insights.
Those insights then need to be placed in the proper context for each function. The same information holds different value — and needs to be delivered in different ways — for R&D, production planners, logistics managers, executive decision makers, and so on.
Perhaps most important, data scientists must empower the supply chain with predictive analytics that let you quickly and accurately forecast demand. That needs to happen before competitors make the same predictions — and before your customers realize they have desires your business isn’t meeting.
Thanks to sophisticated scientists and technology, researchers just determined that the universe holds 10 times more galaxies than previously thought. With the right talent and tools, what vast new opportunities will your supply chain discover?
Learn more about how running a live supply chain can help you thrive today and innovate for tomorrow, visit us at SAP.com.Comments