Companies around the world are holding onto an untapped power than can transform their digital supply chain: the substantial amount of data collected from consumers. These large pools of data are overwhelming to clean, analyze and use effectively, but through harnessing Big Data Analytics (BDA) and Artificial Intelligence/Machine Learning (AI/ML), companies will have the best chance of gaining competitive insights that will optimize performance and minimize risk.
The Digital Supply Chain Institute (DSCI) and the Center for Global Enterprise (CGE) worked with senior executives from 72 companies to develop the tools and insights needed to transform digital supply chains. They found that there are many ways companies can build an exceptional supply chain, but AI/ML is essential to capitalizing on the vast amounts of data companies collect.
The DSCI’s report, titled, “Driving Demand in the Digital Supply Chain: Algorithms and the Untapped Power of Applying Real-Time Big Data and AI/ML,” highlights ten ways that companies can utilize AI/ML to improve their digital supply chains:
- The Data 80-20 – Getting it Right. A company should spend 80% of effort on analyzing data and making decisions and 20% on collecting and cleaning the data.
- Real-Time Demand Shaping Beyond Forecast Accuracy. To increase and manage demand, companies should invest in technology to capture new data, develop new algorithms and use it to make decisions in real time.
- Battle of the Algorithms. Companies must collect more customer data and use AI/ML to develop algorithms that learn, improve, and eventually reveal hidden patterns in large volumes of data to unlock predictive power in information that will give supply chains the best competitive advantage in satisfying customers.
- Visibility Reduces Risk. Visibility into suppliers and customers is an essential way to reduce the risk of supply chain disruption. Developments in tracking and analytics will enable a true, transparent, end-to-end supply chain.
In the survey, 88% of respondents agree or strongly agree that use of real-time big data and AI/ML in our supply chain will improve risk management programs to be more predictive and preventive in managing demand.
- New Talent Management for Supply Chain Talent. New people with data scientist skills and deep analytical skills must be found and current people must be trained in databased decision making. Develop a new talent management plan with three components: (1) Hire people with the skills to develop algorithms that better predict and manage customer demand; (2) Improve overall level of data-driven decision-making skills across the workforce; (3) Change job descriptions and develop new compensation plans to attract and retain people with the right experience.
- New Business Models We Can’t Anticipate. Get the data and use AI/ML to gain advantage and use the data to discover new ways of adding value to customers. Once companies have identified, gathered, processed and aligned large amounts of data, they will start to discover new ways to create value in the marketplace.
- New Product, New Wins. Digital Supply Chain knowledge should be added to the process of deciding what to make or do and drive growth.
- Dynamic Pricing. Rapidly adjusting pricing as market conditions change will change the way that the supply chain is managed and drive profitability.
- Own It or Clone It and the Tools to Know. Decide what DSC functions should be outsourced and which should be revenue producers using new tools.
- Algorithm Council: The Secret Weapon. Form a cross-functional team, the Algorithm Council, to create algorithms that drive data collection, analysis, market focus, manufacturing, inventory and a host of other critical business decisions. The primary purpose of the Algorithm Council is to build algorithms addressing the right issues that lead to market dominance and make decisions that increase sales while increasing margin and/or lowering cost.
Read the full report here.