Predictive Analytics uses statistical algorithms, machine learning, and data mining techniques to analyze large amounts of data and make predictions about future events. This offers valuable insights that can help businesses identify possible disruptions more quickly and recover more efficiently.
Supply Chain is the ideal business area for analytics
The performance of key-Supply Chain tasks such as demand forecasting, inventory optimization and supply network coordination relies on the accuracy and the availability of data. New data technology and IT solutions can provide the insights needed to run a Supply Chain more effectively. This type of optimization of physical processes through data can be done at various levels, ranging from a single warehouse to an entire Supply Chain.
The popularity of Predictive Analytics in Supply Chain management is on the rise, with the Predictive Analytics market projected to grow to $28 billion by 2026, representing a nearly three-fold increase in size. The growth of this market is driven by the increasing demand for data-driven decision-making and the need for businesses to remain competitive in an ever-evolving market.
By leveraging Predictive Analytics, Supply Chain leaders can effectively address Supply Chain challenges and achieve cost savings while simultaneously improving service levels.
Most common Supply Chain improvements through analytics
Some of the key benefits of Predictive Analytics in Supply Chain management include:
Mitigating Supply Chain disruptions
Predictive Analytics provides real-time insights into the Supply Chain, allowing organizations to identify and mitigate risks.
Improved demand forecasting
Predictive Analytics enables organizations to forecast demand more accurately, by tapping into new data sources.
Increased operational efficiency:
Identify inefficiencies in the Supply Chain and take corrective actions to optimize processes, reduce lead times and costs.
Improved customer satisfaction
Enabler for enhanced customer service by improving delivery times, reducing stockouts, and ensuring the availability of the right products at the right time.
Removing waste from operations and logistics and as such effectively saving money. Data is the new raw material, enabling your Supply Chain to help reach business goals through shorter cycles days, with less working capital employed.
Supply Chain leaders are exploring various applications of Predictive Analytics to gain a competitive advantage. For instance, element61 created a forecasting system for a retail company that relied on historical sales data, enabling them to forecast sales up to 100 days in advance, considering weather, promotions, seasons, and special events.
No longer pilots projects, but core process improvements through analytics
Amazon uses Predictive Analytics to optimize logistics by predicting purchasing habits, dispatching products from nearby hubs, and optimizing delivery routes, leading to better last-mile sustainability.
Predictive Maintenance is another application of Predictive Analytics where we want to proactively steer our maintenance process by analysing sensor data. element61 has implemented a predictive maintenance solution at multiple industrial customers to optimize operations and become more efficient in their maintenance processes. Some of the customers they have been working for are Atlas Copco, Katoen Natie & Sabcobel.
Consider implementing Predictive Analytics to optimize your Supply Chain and gain a competitive advantage.
Talk to an expert ?
Our team of experts can help you harness the power of data to forecast demand, optimize inventory levels, and mitigate Supply Chain risks. Contact us today to learn more about how Predictive Analytics can transform your Supply Chain into a more efficient and resilient operation, staying ahead of the curve in today's fast-paced business environment.