Supply chains were once plagued with inefficiencies because they operated using stale or outdated data. Not anymore. In today’s fast-paced and ever-changing business landscape, predictive analytics plays a critical role in enhancing operational efficiency and maintaining a competitive advantage. Here’s how predictive analytics is breathing life into supply chains and making them more resilient.
What is Predictive Analytics?
Predictive analytics uses available data to “predict” future trends, such as exchange rates, consumer demand, and other factors important to supply chain operations and metrics. The technique requires the use of statistical modeling and regression analysis to uncover and interpret trends in historical data and determine future trends.
These techniques have been employed for decades, since the invention of computers. However, what has changed is the amount of unstructured and structured data now available as well as the capability of computers to quickly analyze that data to make useful and accurate predictions.
Far from a crystal ball, predictive analytics can’t predict the future, but instead will use probability theories to figure what is likely to occur based on trends and patterns revealed in historical data. In addition to having the right data and processing power available, predictive analytics requires an accurate mathematical model and visual dashboard to analyze and present the results in a useful manner.
How Supply Chain Managers Are Using Predictive Analytics
As more and more businesses leverage available data in their operations, the predictive analytics market is projected to grow to $38 billion by 2028. Here are some of the ways supply chain professionals are using predictive analytics to improve efficiency and boost profits.
1. Demand Forecasting
Predictive analytics can forecast demand by analyzing trends in historical data related to variables such as customer demand, seasonality, weather, and economic conditions. These forecasts can help organizations stay on top of their manufacturing schedules, shipping strategies, and inventory levels. Forecasting demand accurately allows businesses to remain proactive, reduce waste due to overstocking, and save costs on emergency shipping.
2. Inventory Management
Predictive analytics allows businesses to find optimal inventory levels to minimize stock while satisfying demand. Using advanced models, these solutions allow supply chain professionals to determine precise inventory requirements by usage, location, and region. Using this strategy, warehouses can reduce safety stock and place inventory where it is most needed. This is especially useful when a business has multiple distribution centers or uses omnichannel distribution methods.
3. Predictive Pricing Strategies
Using historical cost-plus pricing models or other models that produce a pre-determined margin can be inefficient and limiting. Instead, predictive pricing can forecast demand for individual products so a business can dynamically adjust prices to charge what the market can bear. Amazon, airlines, and even Uber use some form of this strategy to maximize profits.
4. Shipping and Logistics
Transportation and shipping costs can account for a significant portion of a company’s cost of goods sold. Using predictive analytics, a business can minimize these costs by determining the optimal quantity and frequency for shipping. Predictive route planning can take into account factors like distance, weather, traffic, delivery points, fuel costs, and more to produce the best possible results.
5. Managing Supplier Performance
Predictive analytics can help identify the best suppliers for your business in terms of delivery efficiency, sustainable practices, costs, and more. It can also analyze your current group of suppliers and market conditions to gain more visibility into supplier performance. Also, businesses can use these tools to uncover opportunities for improvement and outline best practices for operations.
Success Stories of Predictive Analytics for Supply Chain Resilience
Many companies are already using predictive analytics with great success. Here are a few examples:
- Walmart collects data from millions of online searches and transactions to better manage its inventory, optimize its supply chain, and improve its in-store and online shopping experience.
- Western Digital used a Predictive Risk engine during the COVID-19 pandemic to save costs and protect its supply chain from massive disruptions.
- UPS uses predictive analytics to optimize its delivery routes, lower fuel consumption, and boost overall efficiency.
- Maersk and DHL are logistics providers that have optimized their supply chains and logistics operations by anticipating demand and reducing waste.
Considerations and Challenges in Implementing Predictive Analytics
Predictive analytics can be a life-saver for businesses struggling with various supply chain challenges. If you are thinking of using this solution for your business, here are several things you’ll want to consider improving your chances of success:
1. Data Availability and Quality
Predictive analytics is most effective when complete and accurate data is used. Ideally, the data will be free of errors, duplicates, and inconsistencies and integrated from a variety of sources so you have a comprehensive dataset to apply to your models.
2. Data Security and Privacy
Ensuring that you comply with data governance regulations and policies and protecting sensitive data can be challenging when using predictive analytics for supply chain management. However, it’s a critical consideration. You can solve this by encrypting data to prevent unauthorized access.
3. Change Management and Organizational Resistance
Change can be tough for management and workers to accept, but the rewards make pursuing predictive analytics worth the effort. As an organization, you must commit to training, effective communication, and new programs that will showcase the benefits of using these solutions.
4. Optimization and Continuous Improvement
Predictive analytics models are getting better all the time. It’s essential that your company continually assess and update its approach based on new insights and data. For organizations, this means taking in feedback, using machine learning for optimization, and staying up to date on industry trends.
Being able to get ahead of issues before they happen is key to building supply chain resilience. While other forms of analytics can help organizations analyze the aftermath of a situation, predictive analytics provides insights for future forecasting and visibility. This enables supply chain managers to shift and adapt as needed to mitigate future risks and setbacks.
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