The Future is Now: Improving the Supply Chain with Predictive Analytics
In today's highly competitive business environment, a trusted supply chain that functions seamlessly is essential. Even small errors can have an impact on the entire supply chain, resulting in lost revenue, unsatisfied customers, and irreparable damage to your brand.
Now more than ever, companies need to have the ability to make informed decisions quickly and accurately to stay ahead of their competitors. Predictive analytics has emerged as a game-changer in this regard, allowing organizations to gain a competitive edge by making data-driven decisions.
What is Supply Chain Predictive Analytics?
Supply chain predictive analytics is the use of data mining, machine learning, and statistical analysis to identify patterns and trends in supply chain data and make predictions about future performance and outcomes.
The goal of supply chain predictive analytics is to improve decision-making and strategic planning by providing a more accurate understanding of future demand, supply, and other key factors that can impact the supply chain. This allows businesses to proactively manage and optimize their supply chain operations, reducing costs, improving efficiency, and enhancing customer satisfaction.
What Are the Different Types of Predictive Analytics Methods?
Minitab's predictive analytics module consists of proprietary methods such as CART® (Classification and Regression Trees), the original Random Forests®, a classification algorithm consisting of many decision Trees, TreeNet®, Minitab's own gradient boosting methodology, and MARS®, an innovative tool that automates the building of accurate predictive models for continuous and binary dependent variables.
Developed by the inventors of tree-based modeling techniques, Minitab is the only company in the world to offer these branded and popular methods. Minitab has made these methods accessible to everyone, not just data scientists, no matter where they are on their analytics journey.
How Can Predictive Analytics Improve the Supply Chain?
Forecasting is about anticipating future events based on patterns found in historical data sets; it's mostly about finding a suitable mathematical model that accurately forecasts future trends and predicts what will happen under specific conditions. It helps to indicate everything from sales volumes of individual products, market demands, seasonal fluctuations, and more.
Predictive analytics allows organizations to take steps before an actual increase in sales occurs, not after customers start complaining about out-of-stock items. Demand forecasting can predict future market trends and supply accordingly, helping in enterprise resource planning. As an example, the predictive model could help companies estimate the demand for their products in a specific region, so they could either expand production or look for partners with spare capacities who could provide additional units at certain times when sales are expected to increase.
Inventory management is one of the most critical processes that predictive analytics can improve. Having too much inventory in stock can be costly, while not having enough for expected sales could mean losing potential customers. The predictive model helps organizations maintain just the right level of supplies at all times – which usually means lower investment costs and less waste due to overproduction or understocking.
Companies adopt supply chain analytics to determine how much inventory should be kept on hand based on historical data about customer behavior patterns combined with upcoming events such as holidays or an end-of-season sale period, which might cause increased purchases of specific items.
An extension of inventory optimization is the prevention of stockouts. It is a big challenge for retailers, as shoppers will quickly turn to another company if they can't quickly get the products they need.
Data analytics for inventory optimization can help with calculating the lead times – the number of days it takes for an item to reach your warehouse after you place an order. This lead time can then be merged with the current sales data to estimate the safety stock and inform retailers of when they need to place a reorder request.
A predictive analytics solution can help supply chain managers reduce operational costs and downtime by identifying potential problems before they occur. In addition to predictive analysis for production planning and scheduling, companies can use predictive models to simplify the maintenance process, helping avoid expensive breakdowns that could have been prevented with little preparation.
Predictive equipment monitoring solutions help businesses reduce costs associated with unplanned downtime by enabling them to schedule repairs ahead of time rather than dealing with unexpected equipment breakdowns that result in production delays or excessive product waste caused by outdated machinery parts.
Predictive fleet optimization solutions help supply chain businesses find new ways to combine important supply chain metrics and data from different sources such as vehicle location information, delivery time estimates based on distances covered per day, and other relevant metrics that affect the route planning process. In predictive routing models, factors like expected travel times are combined with ongoing events specific for each company, such as available fleet, drivers' schedules, cargo, loading places, holidays, etc.
Predictive analytics can help logistics providers optimize their routes by identifying road segments where traffic tends to slow down or gets congested. This way, they have a better understanding of how long it takes to transport a certain amount of cargo on specific roads without any surprises along the way. Predictive modeling is also helpful when reacting quickly if unexpected events occur, such as extreme weather conditions requiring changing routes or temporarily altering schedules.
For manufacturers, predictive analytics can be used to optimize pricing strategies by identifying optimal price points based on historical data about product sales volume at different prices and market conditions such as currency exchange rates, inflation, and raw material costs.
Supply chain managers can use predictive models to create a baseline model that considers historical data and produces an accurate prediction about what will happen if certain conditions remain unchanged. Should they choose discounted prices? Or increase their margins? By predictive modeling, companies gain deep insights into how different factors affect buying decisions – such as price changes or promotional campaigns – which helps supply chain professionals adapt pricing strategies accordingly and increase revenue from sales even further.
Supply chain companies adopt predictive analytics for risk management to identify possible risks that may cause disruptions along the supply chains. The popularity of social media and the sea of data we all share create new models that utilize big data analytics and help mitigate supply chain disruptions. A company may use social media data about strikes, fires, or bankruptcies to monitor supply chain disruptions and take proactive steps before its competitors by mapping supplies chains and recording social data on strikes, fires, and bankruptcies.
Without predictive analytics, companies are forced to make business decisions based on past data. In contrast, supply chain predictive analytics uses historical data and real-time trends to prepare models for multiple scenarios and identify possible solutions. This way, businesses know exactly how to respond to issues such as delivery delays, shipping rate spikes, and carrier capacity constraints.
Predictive models help companies gain insights into customer behavior and therefore have the potential to improve customer experience. Computer models can identify what customers are likely to buy next and when they may cancel or return a product. Predictive analytics in supply chain management algorithms can identify predictive patterns and trends about buying personas, which enables companies to recommend products or offer personalized pricing based on the information they have gathered from customers.
Predictive analytics can also be used to identify customer segments, which will make it easier for businesses to adjust supply chain networks and product prices according to demand at different price points or introduce new products on the market if certain types of buyers are more likely to purchase them.
Predictive analytics can find patterns and trends in manufacturing processes, enabling manufacturers to anticipate and stop quality problems before they arise. Analyzing data from numerous sources, including sensor readings, machine records, and quality control inspections, can be used to do this. Manufacturers can detect patterns and abnormalities in data that point to future quality problems and take preventative action by utilizing AI and ML algorithms to identify them.
This can greatly lower the amount of defective goods produced and raise the standard of the entire product line, increasing consumer happiness and loyalty. Additionally, producers can avoid wasting time and money on rework and scrap by spotting and fixing quality problems early in the production process.
Leverage the Power of Predictive Analytics with Minitab
If there's anything that separates organizations, it's their ability to forecast requirements accurately. Whether it's simply the next day's sales or something more complex, such as the long-term life product cycle, organizations that use predictive analytics have a head start.
Research conducted by Gartner shows that companies embracing predictive supply chains can cut inventory by between 20-30% thanks to more accurate demand predictions.
With Minitab's powerful software, you can easily make data-driven decisions based on predictive analytics. Our market-leading, flexible, and user-friendly software helps you discover insights, predict outcomes, and improve your results that will streamline every aspect of your supply chain.
Original blog article is written by Jon Finerty for Minitab