Supply chain data: How it enables predictive analytics and transforms supply chain management
As the name implies, the term “supply chain predictive analytics” helps supply chain organizations predict future trends, such as delivery time, inventory level, and other supply chain metrics. The primary goal of predictive tools is to analyze historical data to determine and formulate future trends.
To understand supply chain data and supply chain predictive analytics, start by picturing a traditional supply chain – the sequence of processes that transform raw materials to finished goods – and then to the distribution of the finished goods. Supply chain management tracks how goods and services flow through the chain effectively and efficiently.
The challenge is that most organizations have tons of supply chain data, but they have zero visibility into how it works. This can quickly become the Achilles heel that will undermine your big data legacy.
A reliable supply chain that runs like clockwork is a must in the highly competitive world of commerce. Even minor errors can affect the whole supply line, leading to customer dissatisfaction and impairing your business reputation. Here is where supply chain predictive analytics step in and take a vital role. Supply chain predictive analytics leverage data, AI and machine learning to predict business effectiveness and provide actionable insights on performance and upgrades.
That’s why many businesses have started to adopt predictive analytics to upgrade their supply chain management and run it in a much more intelligent and thought-out manner. According to statistics, the market size of predictive analytics is estimated to reach $38 billion by 2028.
Why is data underutilized in supply chain predictive analytics?
Difficulty in accessing data
Supply chain data is dispersed and difficult to access. Data sources for supply chain and logistics companies abound distributor reports, warehouse slips, ERP data, SCM software data, financial data, and so on. These supply chain data is frequently in various formats, from Excel spreadsheets to physical warehousing notes on where the inventory is located. Because of the dispersion of data, it isn't easy to develop statistical models that look for patterns to develop predictive supply chain models.
Poor data hygiene
The raw supply chain data is of poor quality and must be cleaned. The caliber of the data used to fuel the predictive algorithms affects how accurate the predictions are. Unfortunately, much of the supply chain data is useless unless it is thoroughly cleaned. Companies must put in the necessary work to make the data usable for a supply chain predictive analytics system, which includes standardizing differently named fields into a common terminology, linking the same item ID across different departments, and digitizing handwritten notes.
Insufficient understanding of all processes
Often digital solutions used by supply chain organizations are not integrated with each other, meaning that the supply chain data they collect is siloed and does not contribute to an understanding of supply chain processes as a whole. If that’s the case with your company, you should ensure interconnection of all business systems to get a 360° picture of your workflow.
“Set it and forget it”
The "set-it-and-forget-it" assumption misses the target. Supply chain predictive analytics, unlike descriptive analytics, is concerned with the future. And the future is ever-changing. The best predictive models account for this and adjust their predictions as new data arrives. If supply chain companies want to transition from business insights that focus on a fixed past to big data analytics that focus on an ever-changing future, they must adopt a different mindset. This entails more dynamic decision-making and agility in responding to market changes, as well as more robust data engineering systems capable of handling new incoming data and fast analyses without breaking.
Use Cases of Supply Chain Predictive analytics
If organizations can overcome the challenges mentioned above, they have the potential to harness the power of supply chain predictive analytics. By collecting and cleaning the dispersed supply chain data and feeding it into predictive analytics algorithms, numerous scenarios can benefit from this approach. The following are some common uses 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.
By identifying potential problems before they occur, a supply chain predictive analytics solution can assist supply chain managers in lowering operational costs and downtime. Additionally, to using supply chain predictive analysis for production planning and scheduling, businesses can also use predictive models to streamline the maintenance process and prevent expensive breakdowns that could have been avoided with just a little forethought.
One of the most popular supply chain analytics applications is predictive maintenance, which gives businesses a competitive advantage by optimizing productivity levels while minimizing operational costs.
By enabling companies to schedule repairs in advance rather than having to deal with unplanned equipment breakdowns that cause production delays or excessive product waste due to out-of-date machinery parts, etc., predictive equipment monitoring solutions help businesses reduce the costs associated with unplanned downtime.
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.
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).
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.
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.
How supply chain predictive analytics transform supply chain management
There are multiple benefits of leveraging predictive analytics in supply chain, including:
Faster data processing
supply chains accumulate huge amounts of data from sales details, invoices, delivery notes, customs documents, etc. which would take innumerable hours to process manually. A solution backed by predictive analytics can handle it in a matter of minutes.
Increased supply chain strategy effectiveness
When a business is aware of its supply chain strengths, weaknesses, and potential risks, it has a solid ground on which to build an effective strategy.
Improved resource management
Predictive analytics helps to distribute resources and workforce more intelligently, focusing effort where it is really needed.
Effective risk prediction
With risk forecasts derived from predictive analytics, companies can get ready to manage, or avoid completely, supply chain risks.
Insights backed by predictive analytics data can help you stay a step ahead of your competitors and achieve much better results.
Supply chain predictive analytics enable organizations to cast aside guesses and assumptions, and make well-informed decisions based on accurate and reliable data. So it is a must-have for businesses aiming to deliver the best customer service, streamline logistics, and boost production performance with predictive equipment maintenance.