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Supply chains are flush with data. From the litany of enterprise systems to the emergence of connected equipment to Internet of Things companies have available more data than at any point in history. But turning that data into competitive advantage is still a work in process for most supply chains.

Fortunately turning data into insights that can improve everything from process efficiency to customer experience is within the grasp of nearly every company. Technology is increasingly accessible to process data and understand more about the supply chain plumbing than ever.

The output of technology processing big data has become known as predictive analytics. Predictive analytics allows companies to leverage the treasure trove of data and make business decisions in real-time for daily operations and to inform long-term strategy. This ability to proactively make these decisions makes predictive analytics the next big thing in supply chain business intelligence. As technology continues to mature and costs associated with implementing advanced technology decreases, supply chain managers will accelerate their adoption of turning predictive analytics into a strategic advantage.

Below are 4 ways to use predictive analytics to improve your supply chain.

  1. Demand Forecasting
    Utilizing predictive analytics to forecast future demand for products is a key to the success of every business. By having an accurate picture of consumer demand companies are able to provide your customers with the products they want without a delay in service. Utilizing machine learning algorithms and cloud-based inventory management technology it’s possible to eliminate overstocking while enabling warehouses to work together to meet demand and increase customer satisfaction.
  2. Price Setting
    Relying on outdated pricing practices like cost-plus models often leads to products being sold at different prices at different locations. This can lead to poor customer experiences as well as missed profit opportunities. By using predictive analytics to set prices it’s possible to take into account the various factors that can affect sales and automatically adjust pricing to what the market will bear.
  3. Product and Content Placement
    Short-term events such as weather, shortages, and promotions, can greatly affect supply chain performance. Utilizing predictive analytics to detect unexpected conditions facilitate the ability to adjust on-site merchandising to better position products.
  4. Predictive Maintenance
    Reactionary and inefficient break-fix models can cause process outages due to parts not being available when needed for preventive maintenance. Using IoT sensors with predictive analytics will help optimize inventory of service parts, avoid the cost and disruption of unscheduled downtime, and provide higher customer satisfaction.

Predictive analytics is giving businesses the capability to improve their supply chain. Everything from delivery management, order lifecycle, shipping costs, and inventory management can be positively impacted by unlocking the data inside the business and applying predictive analytics.