Utilizing Big Data for a Predictive Supply Chain Model

Manufacturing industries have a come a long way in integrating analytics tools and technologies into the supply chain process. The companies who continue to rely on traditional supply chain models are already struggling to remain competitive.

In our previous blogs, we point out the various tools and technology that help reduce inventory and streamline the supply chain. Based on DHL’s (Deutsche Post DHL Group) whitepaper on The Predictive Enterprise: Where Data Science Meets Supply Chain, we are able to see the power of a predictive supply chain.

“The next generation of data analytics will save our customers time and money by moving to a ‘predict and fix’ model… All across our company, we are driving down operating costs and increasing uptime for our customers by turning big data into valuable, actionable information.” – Doug Oberhelman, Chairman and CEO, Caterpillar Inc.

The Predictive Supply Chain

The ability to turn big data into valuable, predictive information is what every supply chain model should be using.

Gary Keatings, Vice President of Global Solutions Design Center of Excellence and Product Development, DHL Supply Chain, explains, “from supply chain data, we can calculate the current cost to serve for each product line, each market, etc., and can adjust the supply chain strategy accordingly. As a supply chain intelligence partner, we can help our customers run their entire business better to improve the bottom line.”

Organizations are already seeing a high return on investment in multiple areas of the business including product quality, revenue, asset utilization, product launches, order cycle time and more. What’s more? Gartner explains that, depending on the industry, companies are cutting 20 to 30 percent out of inventory, while increasing the average fill rate by three to seven percentage points.

Hiring a New Supply Chain Skillset

In order for these business analytics and tools to be interpreted and used effectively in the supply chain, manufacturing companies will need to hire data scientists.

Data scientists will need three kinds of expertise in “technical knowledge about tools and technology capabilities, functional knowledge about supply chain management and mathematical knowledge of algorithms.” Without the right people to help interpret the data within the supply chain, manufacturing companies will have a hard time competing in the industry.

As technology and the Internet of Things continues to expand manufacturing models, the MEP Supply Chain Optimization program is a strategic approach to honing the opportunities and finding solutions to constraints.

For more on Where Data Science Meets Supply Chain, download the DHL whitepaper here.