April 25, 2023
How much could your business accomplish if you could see the future? You could prepare for upcoming changes in the market, adjust to consumer demands before they happen, and plan for things that seemed unforeseeable. This is the power that predictive analytics gives you and it is changing the game for supply chain success. It’s not a crystal ball, but it is based on your business and industry data. A growing number of supply chain companies and distributors are starting to implement predictive analytics in their management and decision-making processes to help their businesses succeed in these volatile times.
What Is Predictive Analytics?
Predictive analytics is the process of analyzing past data to identify patterns and forecast supply chain metrics such as sales trends, materials costs, and product demand. This is accomplished using a combination of statistical modeling, regression analysis, and historical data to predict a future outcome. Today’s technology allows us to analyze massive amounts of data, both structured and unstructured.
It’s important to note that predictive analytics differs from descriptive and diagnostic analytics, which look at data to determine what happened and why something happened. In contrast, predictive analytics is using data to determine what will happen. Although it’s named “predictive analytics,” in reality it’s more like “probability” analytics. Your reports will be based on probability calculations from gathered data. So although it might not be a hard and fast prediction, it should follow pretty closely to what will happen in the future.
In supply chain management, certain aspects of the process are very conducive to the analysis of data to improve predictions. These include balancing inventory levels, transportation costs, and customer demand. In the case of customer demand, companies have information about past sales, so by analyzing the data that they already have, they can make predictions on future orders. Those predictions allow them to better plan production schedules, inventory management, and so on, reducing the risk of stockouts or overstocking.
For example, if your customers are ordering a lot of one product and not a lot of another, you want to make more of the product in higher demand. The less inventory you can keep while still being able to supply your customer demand, the better off you’ll be. It’s a delicate balance, and predictive analytics makes finding that balance a little bit easier.
How Does It Affect Supply Chain Management?
Most supply chain businesses currently lean heavily on descriptive analytics, which tells you why something happened. Predictive analytics gives you the ability to take one step forward, ask the question “what will happen?” and gain insights that help you stay ahead of the curve.
That advantage is gained through a myriad of benefits. Predictive analytics gives you insights into your offerings that then ensure you don’t get stuck with obsolete products. It reduces storage costs as you are finding the balance of just enough inventory, specifically inventory of the right products. Your customers have a better experience from start to finish, resulting in higher customer satisfaction. Transportation costs are reduced as you avoid paying expedited shipping because you don’t have products in stock and must rush to manufacture them to fulfill orders.
Not only does predictive analytics help improve the supply chain from you to your customer, but it also helps you improve the process between you and your suppliers. In situations where you’re building something from raw materials, anticipating customer demand allows you to know how much raw material you need to order, and know how quickly your supplier can respond to an order you place.
Predictive analytics is an essential tool in supply chain management because it enables companies to optimize their operations and improve their bottom line.
Explore strategic solutions to logistics issues
I have seen the power of predictive analytics as we’ve worked with a client to solve some logistical challenges. This client provides the CO2 used in soda machines in restaurants and for some industrial applications. They had set routes that the drivers would follow to deliver the product to their customers. More and more frequently, customers were running out of CO2 before their next delivery. They would call our client asking for more product to be delivered immediately, diverting the driver from their planned route to serve that frustrated customer.
We examined their sales history and were able to anticipate when customers would be running out of CO2, planning deliveries before they would likely run out. A new delivery consistency and routes were established, taking into account which customers needed which delivery frequencies.
Our analysis also assessed factors such as seasonality. For example, some locations at the beach would have extremely heavy traffic during the summer, but less so during the off-season. Based on past data we could predict when they would need different quantities of CO2. Predictive analytics even allowed us to help them through the disruptions of the COVID-19 pandemic, continually updating data and generating delivery routes on a more frequent basis.
Trending Use Cases for Supply Chain Predictive Analytics
There is so much data available today, so you can use industry data to augment the data you have from your organization to come up with better predictions. 57% of companies not currently using predictive analytics plan to begin within the next 5 years.
We are seeing predictive analytics applied very successfully to a wide variety of industries. Predictive pricing strategies are being used by companies such as Uber, some airlines, and short-term rental management companies. Delta has been using predictive analytics with Airbus Skywise to monitor data on its aircraft and parts, anticipating maintenance needs. This predictive maintenance saves time and money. Amazon uses predictive analytics to provide anticipatory shipping, where popular items are at local warehouses to cut down on fulfillment time. UPS invests $1 billion annually into analytics technology. When they eliminate one mile from every driver’s route each day, the potential savings add up to $50 million.
There are so many unique and powerful ways predictive analytics can improve efficiency and positively impact your bottom line. Companies that have successfully used predictive analytics to forecast inventory needs have been able to cut 20-30% out of their inventory. This generated margin improvements of one to two percentage points. Predictive analytics is the key to cost-savings and operations optimization.
Get Ahead of the Curve
Predictive analytics can keep you one step ahead of your competitors so your business is always ready to adapt to a changing market. Big data and predictive analytics allow companies to anticipate their customers’ behavior, find opportunities to strengthen their supply chains, and make better decisions.
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