In many cases, supply chain surprises manifest from poor visibility. Reliance on historical data can limit an organization’s ability to sense possible exposure to disruption that can impact everything from minor irregularities to extreme shifts in demand. This is especially the case when past or current circumstances do not indicate what will actually happen in the future. Adding real-time data can help in some cases, but that also adds complexity.
Supply chains have always been vulnerable to economic turbulence, industry changes, and operational shortfalls. But that doesn’t mean disruptions and risks that follow are entirely unavoidable.
Now that businesses have better access to predictive forecasting technologies that leverage data from the past and present, such a one-sided approach to forecasting, analytics, reporting, and planning is no longer justifiable. According to McKinsey & Company, applying machine learning and AI-driven forecasting (based on historical and real-time data) to supply chain management can reduce errors by 20%–50%. This then translates into fewer lost sales and lowering product unavailability by up to 65%. The value add of that level of forecast accuracy improvement justifies the move toward modern algorithms and AI.
Forecasting accurately with future-minded insight
“The variability that has plagued supply chain planning over the last few years has, in most cases, also created a deluge of data on disruption,” observes Bill Panak, Vice President of Data Science at Logility. “That disruption data is foundational for supply chain processes that companies build in the future.”
Acquiring this repository of historical data and real-time information, known collectively as big data, has become a natural part of doing business. The hard part is knowing how to use the massive volume of intelligence to get a comprehensive view that accurately forecasts how to respond to changes faster, improve supply chain agility, and optimize the company’s value.
Jonathan Doller, Business Consultant at Logility, believes this is where the concept of chunking can be beneficial. “By looking at big data one use case at a time, the noise of short-term or one-off events can be removed to deliver more meaningful insights into where trends are heading and see demand signals more accurately,” he explains. “In other words, dividing big data into packages of small data enables supply organizations to see the big picture and address trends and risks one step at a time through the right lens.”
Jim Hare, distinguished research vice president from Gartner, underscores the importance of turning to new analytics techniques known as ‘small data’ and ‘wide data’. “Taken together, they are capable of using available data more effectively, either by reducing the required volume or by extracting more value from unstructured, diverse data sources,” he advises.
This move toward greater forecast accuracy by leveraging big data in the supply chain is the genesis of future-proofing the supply chains with machine learning and AI. Risk is mitigated by fully leveraging data and science, increasing transparency, and improving decision-making. More importantly, organizations that leverage their big data can not only pinpoint the suppliers, processes, and facilities that are on a path toward failure, but also know which critical inputs are threatened by shortages or price volatility. That specificity of understanding, in turn, leads to better decisions on how to respond to disruptions.
Extracting more value and opportunity from data diversity
Supply chain variability itself is an uncertainty that can be predicted when more and better data are leveraged. What was once viewed as speculative and experimental is now routine, thanks to machine learning and data engineering. As a result, organizations can turn big data into finely tuned intelligence on emerging risks and changes in demand across daily, weekly, monthly, quarterly, and multi-year horizons – paving the way to a future-proof supply chain.
How can your business benefit from the data management and forecasting capabilities of the Logility Digital Supply Chain Platform? Find out by watching our on-demand webcast “How to Future-Proof Your Supply Chain: Using Big Data to Drive Forecast Accuracy.”
