As the name indicates, supply chain predictive analytics is all about predicting future trends, including metrics, such as your delivery times, inventory levels, and further key metrics of the supply chain process. The goal of these tools is to learn from the past by analyzing historical data to identify trends and offer actionable recommendations to enhance future performance.
To get a sense of what this means, picture a mainstream supply chain: where you take raw materials, go through a series of processes that turn them into finished goods, and ship that product to consumers. “There is an entire supply chain of goods and services that are part of it and then supply chain management is how does the goods and services flow through that chain effectively and efficiently.
Data has become the lifeblood of the supply chain, but a common challenge for many organizations is that it collects massive amounts of supply chain data without full visibility on how to better use that data. Such a gap may rapidly turn into a hindrance to the effective utilization of big data.
The valuable supply chain is critical in reducing need in a saturated marketplace today. It’s easy for something to go wrong, and mistakes can snowball quickly, resulting in unhappy customers and a tarnished business image. Predictive analytics tackles this problem by using data, artificial intelligence (AI) and machine learning to anticipate outcomes, optimize performance and recommend enhancements.
It is, therefore, unsurprising that predictive analytics is being embraced by a wide range of firms to intelligently, efficiently, and resiliently manage the supply chain. The impact of predictive analytics in determining the future of supply chain operations is clear and evident as the market for these solutions is anticipated to increase to $38 billion by 2028.
Introduction to Supply Chain Predictive Analytics
Supply chain predictive analytics uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes in a supply chain. Businesses can use what they know about patterns and trends to predict what may occur in the future, taking steps to prevent potential problems from occurring before they actually happen.
For example, a company can use output and revenue to determine the revenue for the coming months and determine their profitability. The model will focus on two variables, one of which will be dependent while the other will be independent.
From inventory optimization to transportation efficiency, it ensures logistics professionals can better streamline their operations and make a more dynamic supply chain.
There are various predictive analytics models – classification model, clustering, forecast, time series, etc. All of them predict future values based on historical data arranged in multiple different ways.
The Impact of Data in Predictive Analytics for Supply Chains
Predictive analytics, in a supply chain, is data-based in nature. Companies that have access to high-quality, real-time data will make forecasts and predictions that are simply more accurate. Supply chains produce data in far greater volumes — sales figures, inventory levels, transportation routes, supplier performance, weather conditions and consumer behavior. When aggregated and analyzed, this data can find insights that enable businesses to optimize operations, minimize waste and improve decision-making. For instance, a company can use past purchasing patterns over time to forecast seasonal demand increases or shifts in consumer preference. It enables them to follow suit accordingly in terms of production schedules, or stock levels, ensuring that they’re always prepared to meet the needs of their customers, without overstocking or understocking products.
Types of predictive modeling
They are specific models that predict future trends by analyzing past data for patterns and trends. Some popular predictive analytics models are classification, clustering and time series models.
Classification models
They belong to the supervised machine learning models’ class: classification models. Aspects of these models group data around historical data providing connections within some set of data. However, this model can also help segment (in the context of segmentation) the customers or prospects in this case. It can also be used to produce answers to yes/no questions or true/false questions, and are commonly used for fraud detection and credit risk assessment. Some examples of classification models are logistic regression, decision trees, random forest, neural networks, and Naïve Bayes.
Clustering models
Clustering models are considered a part of the unsupervised learning models. They cluster the data with similar features together. For instance, an online retail platform can use the model to group customers in similar clusters through shared attributes and then create marketing campaigns for each group. Examples of clustering algorithms are k-means clustering, mean-shift clustering, density-based spatial clustering of applications with noise (DBSCAN), expectation-maximization (EM) clustering implemented using Gaussian Mixture Models (GMM), hierarchical clustering, etc.
Time series models
The difference is that time series models take different data points in a certain time frequency, for example daily, weekly, monthly, et cetera. You often do this by plotting the dependent variable against time and looking for seasonality, trends, and cyclical behavior that may suggest the need for specific transformations and model types. There are many time series models available — autoregressive (AR), moving average (MA), ARMA, and ARIMA models are commonly used. For instance, a call-center can use a time series forecasting model to predict the number of calls it will receive per hour for different times of the day
Key Benefits of Predictive Analytics in Supply Chain Management
The integration of predictive analytics into supply chain management offers several key benefits, including:
- Security: Every modern organization must be concerned with keeping data secure. A combination of automation and predictive analytics improves security. Specific patterns associated with suspicious and unusual end user behavior can trigger specific security procedures.
- Risk reduction: Most businesses are not only protecting data, they are also minimizing their risk profiles. For instance, data analytics can help a company that offers credit understand whether a customer is at high risk of defaulting. Predictive analytics might help other companies assess whether they have enough insurance coverage.
- Operational efficiency: More efficient workflows translate to improved profit margins. For example, understanding when a vehicle in a fleet used for delivery is going to need maintenance before it’s broken down on the side of the road means deliveries are made on time, without the additional costs of having the vehicle towed and bringing in another employee to complete the delivery.
- Improved decision making: Running any business involves making calculated decisions. Any expansion or addition to a product line or other form of growth requires balancing the inherent risk with the potential outcome. Predictive analytics can provide insight to inform the decision-making process and offer a competitive advantage.
What are the Technologies Powering Predictive Analytics in Logistics?
Some critical technologies that enable predictive analytics in supply chain management are:
Machine Learning and AI: These technologies allow the system to keep learning from new data, thereby enhancing prediction accuracy over time.
Internet of Things (IoT): IoT devices capture real-time data via sensors in products, vehicles and warehouses, which are then used as input to predictive models for increased accuracy.
Big Data Analytics: To predict the future requirements, Processing the large amount of structured and unstructured data from various sources is pivotal.
Cloud Computing: It provides storage, computing, and analysis facilities from anywhere in the world through cloud-based platforms.
Obstacles to Adoption of Predictive Analytics in the Supply Chain
While predictive analytics offer numerous advantages, supply chains can also encounter challenges in the implementation phase:
Data Quality and Availability: If there are discrepancies or inadequacies in the data, they can render the predictive models ineffective. Integration with Legacy Systems: Integrating new predictive analytics tools with legacy systems can be systematic and costly.
Skill deficits: Companies might struggle to find workers who are trained to work with and interpret predictive models.
Implementation Cost: Though the advantages in the long run are huge, implementation of predictive analytics technologies takes up a considerable investment which is a hindrance by some organizations.
Predictive Analytics Applications in Supply Chain
If your brand is able to overcome the challenges mentioned above, you will be able to tap into the power of supply chain predictive analytics. A lot of scenarios can gain from this method by incorporating the scattered supply chain data and clean it and by feeding it to the predictive analytics algorithms. Here are some common use cases of supply chain predictive analytics:
Supply and Demand Forecasting
Precise demand prediction is one of the most important ways to improve the supply chain management process by monitoring important supply chain metrics. When supply chain leaders use predictive analytics, it helps them satisfy customer demand while minimizing inventory expenses. Historical data can help supply chain managers look at past trends and forecast demand.
Predictive Maintenance
Predictive Maintenance A supply chain predictive analytics tool can help supply chain managers reduce operational costs and downtime by detecting potential problems before they happen. Aside from utilizing supply chain predictive analysis for production planning and scheduling, companies can also implement predictive models to optimize the maintenance process and avert costly breakdowns that could have been avoided with a little foresight. Predictive maintenance is one of the most popular supply chain analytics applications that allow the companies to gain an edge over its competitors by getting the perfect balance between the optimum productivity levels and operational costs. Predictive equipment monitoring solutions significantly reduce cost incurred by unplanned downtime for a business by allowing organizations to schedule repairs ahead of time, rather than dealing with unplanned equipment shutdowns that delay production or eliminate excessive products due to out-of-date machinery parts etc.
Logistics planning
Because transportation costs account for a significant portion of the final product price, supply chain predictive analytics can determine the frequency and quantity of transportation required to meet demand while minimizing costs.
Predictive-route-planning can determine the fastest routes based on traffic, distance, weather, and delivery point. Furthermore, smart sensors can monitor vehicle conditions, fuel consumption, and driving style.
Inventory Management
Supply chain managers can use predictive analytics to establish the ideal inventory level for each location to satisfy demand while paying the least amount of money. This allows for a reduction in both safety stock and inventory. When a company has multiple distribution centers, this ability becomes extremely useful because it allows supply chain managers to determine where the stock should be kept (centrally or regionally).
Customer Experience
Predictive models assist businesses in gaining insights into customer behavior and, as a result, have the potential to improve customer experience. Computer models can predict what customers will buy next and when they will cancel or return an order. Predictive analytics in supply chain management algorithms enables businesses to recommend products or provide individualized pricing based on customer data by identifying predictive patterns and trends about buying personas.
This strategy assists consumers and retailers in retaining existing customers while attracting new ones by providing differentiated product recommendations more likely to appeal to them than alternative options.
Predictive analytics can identify customer segments, making it more straightforward for businesses to modify supply chain networks and product prices based on demand at various price points or introduce new products to the market if certain buyers are more likely to buy them.
Pricing Optimization
When a product’s demand is forecasted, the price can be dynamically adjusted to what the market can bear. The strategy used by Uber and some airlines is the best example of predictive pricing.
By identifying ideal price points based on historical data about product sales volume at various prices and market conditions like currency exchange rates, inflation, etc., manufacturers can use predictive analytics to optimize pricing strategies.
Additionally, a predictive system can help companies lower the risk of potential "pricing mistakes," which may have been brought on by human error during manual calculations, delays in obtaining factual information required to set prices appropriately, and other factors.
Case Studies: Real-World Success with Predictive Analytics
Several companies have successfully implemented predictive analytics to transform their supply chains:
- Walmart: From its humble beginning as a one store discount retailer, today, Walmart has a total of 10,500 stores and clubs in 24 countries and eCommerce websites which employs approximately 2.2 million associates worldwide. In the financial year ended January 31, 2021, Walmart recorded a total revenue of $559 billion, a profit of $35 billion with the growth of the eCommerce market. Walmart being a data-focused company is driven by the philosophy of ‘Everyday low cost’ offered to its customers. They rely significantly on their data science and analytics department — also referred to as Walmart Labs — for R&D and data analytics in their supply chain to optimize operations. Walmart: Operates the largest private cloud in the world handling 2.5 petabytes of data per hour!
- Amazon: An American multinational technology company founded in Seattle, Washington, originally as an online bookseller, it has since become the largest online sales company, the largest Internet company by revenue, the largest Internet company by market capitalization, and the largest provider of virtual assistants and cloud infrastructure services. It maintains about 1 billion gigabytes of data on 1.4 million servers and powers data analytics of its supply chain and other business lines to predict how it can serve its customers better.
Key Examples of Data Analytics at Amazon:
Recommendation Systems: Collaborative filtering analyzes 152 million customer purchases to suggest products, generating 35% of Amazon’s annual sales.
Retail Price Optimization: Predictive models determine optimal prices based on customer behavior, competitors, and profit margins to enhance sales and retention.
Fraud Detection: Machine learning identifies high-risk transactions using real-time and historical data, reducing retail fraud and excessive product returns.
Final remarks
Supply chain data analytics is changing the way organizations work, providing unprecedented efficiencies and growth opportunities. The ability to achieve in time business processes should rely more on the predictive accurate analytics, machine learning, big data, global network and scalable enterprise solutions to govern aspects like inventory management, pricing strategies, fraud detection. Tools like these offer enhanced visibility, greater insights into customer behavior, and improved decision-making guidance. Businesses are no longer reactive but proactive in satisfying customer needs through custom-tailored solutions that include recommendation systems, dynamic pricing models, and better fraud prevention. By adopting supply chain analytics organizations can achieve greater efficiency, enhanced resource planning, and a competitive edge in a saturate marketplace.
