Have you ever thought about the journey of a product before it reaches your doorstep? From manufacturing to delivery, numerous processes and stakeholders are involved in the supply chain. But what if we could predict and anticipate future events and trends in the supply chain and make informed decisions based on real-time data and insights? That’s where supply chain predictive analytics comes in.
According to a recent report by Markets and Markets, the global supply chain predictive analytics market is set to grow from $2.2 billion in 2019 to $4.8 billion by 2024, at a compound annual growth rate (CAGR) of 17.3%. This indicates that more and more organizations are recognizing the benefits of using predictive analytics to improve their supply chain operations. Companies like IBM, Oracle, SAP, Microsoft, SAS Institute, and Amazon Web Services are offering a range of solutions that help organizations better understand their supply chain operations and make more informed decisions based on real-time data and insights.
Overall, supply chain predictive analytics is critical for companies looking to improve their supply chain performance and gain a competitive edge. With the market set to continue growing in the coming years, it is likely that we will see more and more organizations adopting these advanced analytics tools and techniques in order to drive business success.
What is Predictive Analytics?
Predictive analytics is the process of using statistical and machine learning algorithms to analyze historical data and make predictions about future events or trends. Predictive analytics helps identify patterns and relationships in data to make accurate forecasts about future outcomes.
According to a report by Grand View Research, the global predictive analytics market size was valued at $7.23 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 23.2% from 2021 to 2028. This highlights the increasing importance and adoption of predictive analytics across various industries.
Predictive analytics typically involves a number of different steps, including data collection and preparation, data analysis and modeling, and the application of statistical and machine learning algorithms to make predictions. Some common applications of predictive analytics include predicting customer behavior and preferences, forecasting sales or revenue, identifying fraudulent activity, optimizing manufacturing processes, and predicting equipment failures. The results of predictive analytics can be used to inform decision-making at all levels of an organization, from strategic planning to daily operations.
Applications of Predictive Analytics
Here are a few examples on how predictive analytics is applied in various industries:
- Retail: Brick-and-mortar as well as online stores predicting seasonal trends, identifying locations where stock is most needed and matching locations with quantity. Economic conditions and customer demand in the geography are also calculated
- Healthcare: The pandemic was the biggest example before us – hospitals and healthcare institutions analyzed large volumes of data to identify issues like bed availability, predicted outbreak levels of the virus in certain areas, etc.
- Financial institutions: Data fraud prevention, outlier transaction identification, and ascertaining what the customer demands are so as to design appealing reward/loyalty programs are some areas that financial institutions apply predictive analytics
- Entertainment: Even opened Netflix and found suggestions related to what you were previously watching? The algorithms are trained on multiple datasets and use AI to understand your preferences and enhance your viewing experience
- Weather: Although not extremely accurate, weather predictions do successfully inform us of extreme weather events like storms and hurricanes, their expected travel path and duration, and other similar life-saving information. Forecasters merge current trends with historic data to arrive at conclusions
The above examples serve one very important function: they make us realize that predictive analytics is all around us, and it works exceedingly well. Supply chain industries that develop their infrastructure to incorporate predictive analytics will soon outstrip those who do not do so.
With that being said, let us look at
How Predictive Analytics is Used in the Supply Chain
The use of predictive analytics in the supply chain is rapidly growing as companies seek to optimize their operations and gain a competitive edge. Let us explore how predictive analytics is used in the supply chain.
1. Demand Forecasting
One of the most important applications of predictive analytics in the supply chain is demand forecasting. By analyzing historical sales data, market trends, and other relevant factors, predictive models can forecast future demand with greater accuracy than traditional methods. According to a report by MarketsandMarkets, the global demand forecasting software market is expected to reach $3.2 billion by 2023, growing at a CAGR of 7.9% from 2018 to 2023.
Example: Amazon uses predictive analytics to forecast demand for its products, enabling it to optimize its inventory levels, reduce stockouts, and improve customer satisfaction
2. Transportation Management
Predictive analytics can be used in transportation management. By analyzing data on shipping routes, carrier performance, and other factors, companies can optimize their transportation networks to reduce costs and improve delivery times. According to a report by Research and Markets, the global transportation management system market size is expected to reach $19.2 billion by 2025, growing at a CAGR of 15.6% from 2020 to 2025.
Example: UPS uses predictive analytics to optimize its delivery routes, resulting in an estimated savings of $400 million per year.
Transport and shipping costs often constitute a weighty percentage of the final product price. Predictive analytics makes it possible to determine the right shipping frequency and the right quantity that can meet demand while reducing costs.
Predictive Transport Management also includes determining the optimal routes, accounting for traffic congestion, weather, distance and delivery points. Smart sensors in vehicles monitor aspects like tire pressure, fuel consumption, vehicle condition, and even the driving style of the driver.
3. Production Planning
Predictive analytics can be used to optimize production planning. By analyzing data on production schedules, machine performance, and other factors, companies can identify bottlenecks in their production processes and take steps to improve efficiency. According to a report by MarketsandMarkets, the global production planning software market size is expected to reach $6.8 billion by 2022, growing at a CAGR of 8.5% from 2017 to 2022.
Example: Ford uses predictive analytics to optimize its production schedules, resulting in a 90% reduction in plant downtime
4. Risk Management
Predictive analytics can be used for risk management in the supply chain. By analyzing data on weather patterns, geopolitical events, and other risk factors, supply chain companies can identify potential disruptions and take steps to mitigate their impact. According to a report by MarketsandMarkets, the global risk analytics market size is expected to reach $45.9 billion by 2025, growing at a CAGR of 13.7% from 2020 to 2025.
In conclusion, supply chain predictive analytics is rapidly gaining momentum as a critical tool for businesses looking to optimize their supply chain operations. By leveraging the power of data and advanced analytics techniques, companies can gain valuable insights into their supply chain performance and make data-driven decisions to enhance efficiency, reduce costs, and improve customer satisfaction
5. Revenue Planning
There are tools in the market such as Logi-Sys by Softlink Global that let companies calculate the revenue margin through a cost and revenue mechanism. The mechanism can be set at a certain revenue margin, for example, 10%. It factors in the buying costs of sales professionals and stops them from proceeding if the revenue is expected to fall below 10% due to the buying costs. Such forecast provisions help set the cash flow within the organization.
6. Fleet Analytics
With predictive analysis, shippers, carriers and forwarders can understand which routes are the fastest in current time as well as upcoming periods. Management of staffing time is optimized. Fuel leakages and wastage is minimized. All of these have a direct impact on revenue and quality of service delivered. Using a transport management system integrated with a predictive analytics solution can open up a completely new system of shipment and fleet management.
7. Shipment Tracking
The usage of IoT and RFID devices for parts and equipment track-and-monitor is a remarkable application scenario. Supply Chain managers can thus trace their shipments in real-time, obtaining intel into accurate delivery estimations and providing the same to customers. Last-minute additions/revisions are also facilitated due to this, without these zero-hour events causing any delay or disruptions to the shipment flow.
8. Equipment Maintenance
In addition to part-and-equipment track-and-monitor mentioned in the previous point, predictive equipment monitoring identifies the mileposts when an equipment needs to be serviced. Accessory component failure triggers are issued for timely maintenance. Unplanned equipment outages are thus avoided, allowing for smoother machine activities and better throughput. Predictive maintenance also inculcates lean stock holding practices by ordering parts only as and when required.
9. Pricing and Demand Balance
The limitations of long-implemented cost-plus pricing strategies and those using predetermined margin can be overcome by a predictive pricing model. Such models are capable of adjusting the product price to market capacity (what it can bear) instead of product demand forecasting.
Shippers and 3PLs can use such models based on real-time market data and tech such as AI and ML to determine how much they should be ideally paying carriers. Global transportation markets are increasingly witnessing more accurate pricing as more freight pricing data is available, automated, and digitized.
While there are still some challenges to overcome, such as data quality and integration, advancements in technology and machine learning algorithms have made it easier than ever before to gather and analyze vast amounts of data in real-time. This will allow supply chain companies to identify trends and patterns that may have otherwise gone unnoticed and respond to potential disruptions proactively.
Moving forward, the future of supply chain predictive analytics is exciting. As more and more companies adopt this technology, we can expect to see further innovation and advancements that will transform the way businesses manage their supply chains. By embracing this trend, supply chain companies can position themselves to stay ahead of the competition and achieve long-term success.