Predictive Analytics in Supply Chain
The old saying, "Why fix something that isn't broken," is no longer true as technology advances. Predictive analytics in the supply chain help you make better decisions.
According to Forbes, Businesses that excel at customer experience perform nearly 80% better than their rivals, and 84% of companies that focus on improving their customer experience report an increase in revenue.
With product margins shrinking by the day and competition increasing, manufacturers are turning to customer service to make up for lost revenue and differentiate themselves. Supply chain predictive analytics is one way for businesses to improve customer responsiveness, set the right expectations, meet commitments, and enrich customer relationships.
But what exactly is this technology, and how can you use it? This article discusses predictive analytics for the supply chain and some practical applications for your business.
What is Predictive Analytics in Supply Chain?
Almost all industries have implemented predictive analytics or plan to do so. The best way to describe supply chain predictive analytics is the capacity to foresee future supply chain activities using data.
"The main driver of predictive analytics' increasing adoption is the desire to reduce the daily risks and frauds."
According to MarketsandMarkets:
A company's ability to deliver its goods or services to the final consumer is made possible by the supply chain, which is full of risks. Suppliers, manufacturers, and retailers will all come into contact with the product, as will the businesses in charge of supplying the manufacturer with crucial components. With so many connections, a single disruption can immediately cause ripple effects throughout the supply chain. Customer satisfaction comes first, then service, and finally, the bottom line suffers.
Customer satisfaction and a company's ability to deliver on its promises go hand in hand. Deliveries that are incomplete, damaged, or delayed do not encourage repeat business from clients. Everybody must contribute for the supply chain to be effective. A company has more time to take action to reduce or eliminate a risk if it can identify threats to its ability to deliver as promised sooner and with greater clarity.
How is Predictive Analysis Transforming Supply Chain?
Predictive analytics and supply chains have both changed significantly over the past decade. It is not surprising that more and more businesses want to integrate predictive analysis solutions into their operations to support supply chain management initiatives. These solutions are widely available at reasonable prices and reasonably simple to integrate with other systems for small businesses.
Because it enables businesses to make more informed decisions about their supply chains than they otherwise could have through conventional methods, predictive analytics has become very popular. Businesses are interested in predictive data science, and governments worldwide are beginning to use cutting-edge predictive tools for their objectives.
Applications for big data analytics encompass the entire supply chain, from suppliers and procurement to production, logistics, sales, and the final consumer. Predictive planning, forecasting, and predictive maintenance are some of the most popular predictive solutions in supply chain management, which will be discussed next.
Use Cases of Predictive Analytics in the Supply Chain
Following are some use cases of predictive analytics in the supply chain.
Using patterns in historical data sets, forecasting is the process of anticipating future events. Its main goal is to create a mathematical model that accurately forecasts future trends and predicts what will happen given variables or conditions.
Companies can use predictive analytics to forecast future customer demand. This is among the main benefits of predictive technology. It enables organizations to act before an increase in sales occurs (rather than after customers complain about missed deadlines and lost revenue opportunities).
Forecasting demand can help predict future market trends and supply, accordingly, assisting in enterprise resource planning. For example, the predictive model could assist businesses in estimating demand for their products in a specific region, allowing them to either expand production or look for partners with spare capacity who could provide additional units when sales are expected to increase.
Because transportation costs account for a significant portion of the final product price, 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.
By identifying potential problems before they occur, a predictive analytics solution can assist supply chain managers in lowering operational costs and downtime. Additionally, to using 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.
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.
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.
Results of Predictive Analytics in Supply Chain
Each company has its unique way of projecting requirements over various time horizons, but those that use predictive analytics are one step ahead (Netflix, Amazon, Google, etc.). The world's top research and advisory firm, Gartner, claims that businesses that adopt predictive supply chains see the highest returns on their investments. In addition, their inventories are reduced by 20% to 30% due to the precise demand forecast.
Benefits of Predictive Analytics in Supply Chain Management
Predictive analytics, combined with the use of big data, can help manufacturing companies rebuild their supply chains and reap significant benefits. Our current age of Industry 4.0 necessitates a more sophisticated and nuanced approach to supply chain management, including logistics, production, purchasing, warehousing, and inventory control. Retailers, suppliers, and manufacturers must redesign their supply networks and incorporate intelligent technology into the operational lifecycle because of the turbulence nowadays.
Predictive analytics is expected to benefit a manufacturing company's processes in the following ways:
⦁ Reduced costs for maintenance
⦁ High effectiveness
⦁ Reduced likelihood of production overruns
⦁ Better customer engagement
⦁ Finding areas where improvements and optimizations can be made
⦁ Preventing interruptions to the supply chain
⦁ Decision-making using data and automated algorithms
⦁ Decreased customer churn
In turn, a well-designed and scalable enterprise SCM (Supply Chain Management) platform can significantly improve internal process efficiency in a variety of ways:
⦁ Reducing the time required for manual labor and reducing paperwork for in-house staff
⦁ Improving manufacturing inventory management.
⦁ Tracking staff activity in near real-time, effectively managing box delivery statuses, and forecasting warehouse load.
⦁ Improving Planning effectiveness while reducing operational overhead and warehouse downtime
⦁ Increasing cost-effectiveness.
How do Major Companies Utilize Predictive Analytics?
Predictive analytics is being used by big businesses to optimize their logistics and supply chain. They are establishing standards for how businesses use predictive analytics and making advancements that promote economic expansion.
⦁ Apple and the supply chain model: To establish real-time visibility into demand patterns, anticipate online orders for products like the Apple Watch and the iPhone, and avoid delayed shipments, they are using forecasting capabilities.
⦁ Amazon and whole foods: To gain access to physical stores, their customers, and the corresponding data, Amazon acquired Whole Foods. Utilizing real-time data, they optimize Supply chain analytics through anticipatory shipping and stocking. In addition to reducing food waste in the US, they are making shopping easier for consumers and suppliers.
⦁ Predictive analytics can increase the certainty of shipment ETAs (Expected Time of Arrival), decrease network latency, protect profit margins, and shorten cycle times, as is evident in businesses like Apple and Amazon.
Challenges of Implementing Supply Chain Analytics
If the supply chain industry wants to build a system with high forecast accuracy, it must overcome several challenges:
⦁ 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. The 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.
⦁ The raw 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 data is useless unless it is thoroughly cleaned. Companies must put in the necessary work to make the data usable for a 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.
⦁ The "set-it-and-forget-it" assumption misses the target. 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.
Future of Predictive Analytics in Supply Chain
Thus, we have demonstrated in this article how important predictive analytics is to enable cost savings and boost productivity. Data-driven decision-making is replacing human-driven decision-making in our industry.
According to Gartner, more than three of four supply chain heads stated that their digital transformation projects are not coordinated. Hiring a Chief Digital Officer to lead the digital transformation and start a data-driven supply chain transformation could be one solution. Unfortunately, some companies can't afford to pay for these services. Another choice would be collaborating with a technology company to offer logistics-related predictive analytics services.
Investing in a predictive analytics solution may become necessary to remain competitive with other players using this solution. Customers' and businesses' demand for faster and less expensive shipping keep rising. Because of this, supply chain and logistics companies that do not incorporate predictive technologies into their operations risk being unable to compete in today's competitive market.
Although there are many different types of analytics, supply chain predictive analytics are the most popular. Each business will experience various benefits from supply chain predictive analytics, but you can anticipate increased visibility, deeper insights, better decision-making support, better resource planning, and supply chain optimization.
Using these tools and techniques, you can increase your bottom line and make your supply chain more responsive to changes in the market. Don't pass up these opportunities because predictive analytics is becoming increasingly crucial for businesses of all sizes.