Supply Chain Predictive Analytics 

Supply chain professionals are working round the clock in the fast-evolving competitive world of business. Small errors and mistakes could impact your SC network and operations, and result in the form of customer dissatisfaction and jeopardizing the reputation of your business. That’s why businesses and companies are employing predictive analytics to update their SC processes and perform various processes in an intelligent way. Today, we’ll discuss supply chain predictive analytics; its definition; how it works, its usage, advantages, and disadvantages.

  • 57% of the companies that aren’t using SC predictive analytical tools, plan to use them in the next five years
  • Reports of the MHI Annual Industry Survey said that the growth of SCM predictive analytical tools increased by 30% in 2020
  • The market size of SC predictive analytics will reach 38 billion dollars by 2028

What is Supply Chain Predictive Analytics? 

Supply chain predictive analytics outlines forecasting future trends like exchange rate, sales demand, and other SC metrics and KPIs. It employs regression analysis and statistical modeling on the historical data to comprehend market trends and predict future trends.

Such types of tools have been prevalent ever since the development of computer and internet technology. The differentiating factor is the capability of new computers to process large amounts of information quickly and efficiently with advanced data mining methods and practices; it helps them to analyze structured and unstructured data.

However, SC predictive analytics doesn’t help you to forecast the future, rather it offers you probable theories of what is likely to happen based on historical data, trends, and patterns. The element that would make predictive analytics possible is the data availability as a result of the widespread application and digitalization of practices and methods.

How SC Predictive Analytics Work 

Some of the main steps on how supply chain predictive analytics work are as follows;

Data Collection

The process of SC predictive analytics starts with gathering data from various sources like CRM custom documents, delivery notes, invoices, purchases, supplier contracts, trade deals, sales reports, and sales forms. SC is a difficult and complicated process comprised of a lot of unstructured data. However, some companies gather data from social media activity, and the information from the public database.

Data Preparation

Algorithms and machine learning require organized and clean data for modeling and training. It plays a significant role in removing all the non-relevant fields before feeding them into the predictive models. However, data researchers perform various preprocessing steps in cleaning data and removing outliners and noises.


After gathering the necessary and relevant data, the SC predictive analytical tool starts playing its part. It focuses on analyzing and investigating data further to observe what type of benchmarking and evaluation standards you can use with minimum investment.


You can implement predictive modeling in various ways depending on the goals and requirements of the company. The predictive models rely on historical data and records to forecast future behavior and events. You could do so by establishing predictive analytical models with various test data sets, testing model outcomes, training based on historical data, and machine learning algorithms.


You could implement the model in the manufacturing environment after testing. It comprises integration with other data sources and production systems. However, the predictive solution requires constant evaluation, which means that businesses and companies need to analyze the impact of the model.

Application & Usage of SC Predictive Analytics 

Some of the main applications and usage of supply chain predictive analytics are as follows;

Demand Forecasting

It is a great challenge for companies to accurately predict the demand; the demand is never linear and various factors impact them. Predictive analytical tools help businesses and companies amplify demand forecasting through existing and previous trends.

Forecasting Pricing Strategies

Instead of predicting the demand for a particular product, you could make adjustments in the prices to find out the response of the market. For instance, Uber, airlines, and other ride service-providing companies employ predictive pricing strategies.

Inventory Management

SC predictive analysis helps you to know the optimum level of managing inventory. It helps SC managers to be aware of the detailed inventory requirements in terms of usage, location, and region. You could decrease the safety stock level and place the inventory where required.

Predictive & Logistics Maintenance

Transport and shipping costs often contribute significantly to the final price of the product. SC predictive analysis helps you to know the optimum level of shipping frequency while maintaining limited cost. Companies could predict the speedy and quick route that you could take in the congested traffic while covering the long distance and managing the delivery points.

Advantages of SC Predictive Analytics 

Some of the main benefits and advantages of SC predictive analytics are as follows;

  • Offers you a key insight to gain a competitive edge and achieve better results
  • Risk forecasting and analysis would help you to manage and avoid the risk completely
  • Allocating workforce and resources intelligently and efficiently at the most needed place
  • Establishing an effective business strategy after being aware of strengths, weaknesses, opportunities, and threats
  • Gathering data from various sources and processing them quickly and speedily

Disadvantages of SC Predictive Analytics 

Some of the main disadvantages of supply chain predictive analytics are as follows;

  • Expenses of new hardware, software, and data experts and researchers would increase
  • Shortage of expertise in the field of IoT, AI, ML, and functionality of other tools and equipment
  • Businesses and companies employ various digital tools and applications
  • Difficult to comprehend various complicated SC processes; you should make sure to develop interconnection among different systems and units
  • Limited record of historical data to make accurate predictions, because not all companies carefully gather and store data

Conclusion: Supply Chain Predictive Analytics 

After an in-depth study of the supply chain predictive analytics; we have realized that SC predictive analysis is significant for business growth and productivity. If you are learning about SC predictive analytics, then you should keep in mind the abovementioned elements, applications, advantages, and disadvantages.

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