How Predictive Analytics Helps Improve Supply Chain Operations

by | Nov 8, 2021

7 min read

McKinsey & Company says 44% of supply chain companies report that AI has helped them reduce costs significantly.

Around 61% of supply chain companies that introduced AI into their supply chains recorded cost reductions. And 53% of the surveyed companies reported a significant increase in revenues.

Plus, a third of the companies in the survey recorded a 5% revenue boost with the help of implementing AI-based operational solutions.

One of those AI solutions is predictive analysis.

Predictive Analytics helps businesses foresee supply chain disruptions and optimize their operations. In turn, they proactively accommodate risks instead of reacting to emergencies.

It also generates insights into seasonal buying patterns helping businesses make more reliable business decisions – fast.

Currently, tech giants like Amazon, Facebook, and Google use predictive analytics to analyze user data. By 2024, half of all supply chain companies will opt for applications that support advanced analytics and AI capabilities.

What Is Predictive Analysis And How Does It Help Supply Chains Grow?

According to a survey of 200 senior-level supply chain executives in late 2020, 72% of businesses reported disruption in trade and commerce while only 2% stated they were prepared to deal with contingencies.

Supply chain businesses that adopted the ‘new risk mindset’ were largely satisfied with the risk reports they offered to their management and boards.

Major risks included economic conditions, supply chain disruptions, and cyber security.

While most other organizations lacked adequate preparation systems due to poor risk analysis, they were eventually caught off guard when it came to supply chain resilience. Amid this chaos is where predictive analytics comes into play with its unmatched risk aversion capabilities.

How?

Predictive analytics refers to the use of big data, machine learning, and advanced computer algorithms to make forecasts for crucial business decisions. For this reason, it is also known as advanced analytics.

Advanced analytics allows businesses to understand past successes and failures and make calculated business decisions based on historical data. Businesses primarily use predictive analytics for demand forecasting by making sense of the pre-recorded data.

For example, during low-demand seasons, businesses use predictive analysis to achieve an optimal inventory level to cater to demand while minimizing stock.

This allows them to stay a step ahead when planning and mitigating overstocking risks.

This is one reason why using predictive analytics in the supply chain grew from 17% to 30% in the past few years. Also, 57% of supply chain managers declared the intention to use predictive analytics in their system in the next five years.

Robotic Process Automation (RPA) and Predictive Analysis

Even before the pandemic hit us out of nowhere, supply chains had begun relying on the operations of AI.

One such example is the Robotic Process Automation (RPA) to carry out preliminary tasks error-free. This is known as cognitive automation. Where a software program is fed into a business’s operation to overtake routine-based tasks with precision and simulated intelligence.

The result is the best of both worlds!

On one hand, cognitive automation mirrors human intelligence and understanding to carry out operations. On the other hand, it eliminates the human error factor.

With a quicker system of supply chain simulation in place, operations and decision-making are expedited with the help of Robotic Process Automation (RPA) and predictive analysis.

Why Do Supply Chains Use Predictive Analytics?

While there is always some risk involved in altering your supply chain setups, a futuristic supply chain plan will ultimately pay off.

Supply chains have been under a bit of rough weather ever since conversations around environmental sustainability became afloat. And then, there is something called financial sustainability that is also contingent on smooth supply management.

A well-rounded, well-assessed supply chain system will not only cut back environmental threats but will lead to greater profitability.

29% of companies that incorporated predictive analytics achieved high levels of ROI against 4% who failed to detect any change.

If organizations come up with fool-proof mechanisms to deal with unforeseen challenges successfully, then predictive analytics is the way to go. This allows them to reduce risks, minimize fraud, and optimize business operations with ease and agility.

Here are examples of several industries that are successfully leveraging AI and predictive analytics for service and profitability.

  • Pfizer’s virtual lab is coming up with advanced treatments for patients’ pregabalin. It uses a combination of AI, predictive analytics, and simulation to achieve that.
  • The automotive industry has begun leveraging predictive analytics to identify factors for improved service, resource optimization, and service distribution.
  • Retail sectors apply predictive analytics for inventory planning and predict outcomes of their marketing campaigns.

 

Benefits of Predictive Analytics

Introducing predictive analytics in your supply chain does more than execute your daily business operations. It allows you to develop strategic roadmaps that do not react – but respond quickly to challenging situations. Allowing for growth and target revenues.

Logistics forecasting gives companies an invaluable competitive advantage.

Here are some of the ways predictive analytics is applied in the supply chain industry:

1. Automated Heavy-Lifting of Supply Chain Operations

Problems with supply chains arise most often because veteran businesses with legacy systems do not think it’s necessary to make changes. This leads to an inefficient, outdated supply chain system

It’s impossible to manually sift and interpret a large amount of historic data that only gets bigger by the minute. This has made the transition to a software-based supply chain exigent.

It does most of the heavy lifting by taking care of all that data and countless applications. By data crawling across applications, businesses can sift through supply chain bottlenecks for a more holistic and integrated mode of operation.

2. Data-driven Supply Chains And Predictive Industry Models

Initially, supply chains were based on a reactive model. The recent shifts in climatic, social, political, and economic conditions have brought the need to base supply chains on predictive models.

Supply chain simulation with predictive analytics allows businesses to test products and systems in a virtual environment. AI and predictive analytics allow businesses to complete automation by digesting enormous data.

But that’s not all, predictive analytics does more than that.

Computer vision (a part of AI) is an extension of advanced analytics, can offer predictions and adapt to changing scenarios without human intervention. It aims to emulate the human vision framework and empowers computers to recognize pictures and videos in a manner humans do.

Costly visual inspection tasks can be automated using computer vision and advanced analytics.

Amazon’s Panorama is one such appliance that uses machine learning. It is not just an appliance, it’s a full-fledged software development kit (SDK). It enables users to deploy computer vision capabilities to existing on-site cameras and help make automated predictions with great accuracy and negligible latency.

Supply chains across industries are using AWS panorama to remove supply chain bottlenecks and improve management and operations.

3. Moving From Reactive To Predictive Supply Chain Transportation

According to a report by Advance Market Analytics, the global Predictive Analytics Market will see a 24% GAGR rise by 2024.

Now, why is this fact important?

Predictive analytics in supply chains allows businesses to report the estimated time of arrivals of shipped products (ETA).

This allows for a smoother end-to-end supply chain. Having delay notices in advance can allow several response measures to mitigate risk and avoid negative customer experiences.

Shipment visibility like this keeps both the business and its customers in the loop leading to greater customer satisfaction. Predictive analytics in the supply chain are reshaping transportation system management and heading towards long-term growth.

Predictive analytics in supply chains involves integrating services based on real-time data to meet the customers’ evolving demands.

4. Carrier-level Supply Chain Visibility and Risk Mitigation

Enabling end-to-end supply chain visibility to ensure accurate ETAs, supplier visibility, logistics status, active alerts, and location status is now possible with predictive analytics and machine learning tools. Tracking carrier-level components and logistics data accurately helps supply chains control and trace their operations with precision.

Conclusion

Introducing predictive analytics within your supply chain operations will not only help preserve your businesses’ ecosystem but make digital integration and growth easier with the right data at the right time.

  1. Predictive analytics gets rid of the guesswork and lays the foundation for insightful forecasts for large supply chains with a huge manufacturing stake.
  2. Supply chain shocks coming from future trade wars, outbreaks, pandemics, terrorism, and accidents can be dealt with in a timely manner with predictive analytics.
  3. Predictive analytics also makes supply chain businesses more agile, connected, and scalable.

Need help building an agile digitized supply chain and reducing shipping costs marginally? Book a free demo here.

Topics: Predictive Analytics, Shipping Trends, Supply Chain Operations
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