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How Machine Learning Can Improve Supply Chain Efficiency

By Kaushik Pal
Published: April 27, 2022 | Last updated: April 30, 2022
Presented by AltaML
Key Takeaways

In order for a business to succeed, it must have a properly managed supply chain. Machine learning is helping to improve the accuracy and efficiency of supply chain management.

Source: iStock/Yozayo

In the current global economy, competition is fierce across different business domains. Each and every organization is striving to improve business efficiency and reduce expenditures. Supply chain management is one of the critical tasks for business owners. Knowing how to implement an efficient supply chain management system is key. Innovative, disruptive technologies like Artificial Intelligence (AI) and Machine Learning (ML) can offer some excellent solutions. These AI and ML solutions can help business to predict a reliable demand forecasting model (also called demand sensing.) The old predictive forecasting techniques are becoming obsolete as those models are not built to learn continuously and make decisions the way the new AI-driven demand sensing models are.

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In this article, we will explore how AI and ML can improve modern supply chain management.

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What is AI and ML?

Artificial intelligence is a combination of different processes and algorithms. AI can simulate some aspects of human intelligence like self-learning, problem solving and responding to given input. Machine learning and deep learning (DL) are subsets of AI solutions.

Machine learning comes under the "limited memory" category of AI, where the AI solution can learn over the time and develop itself. Different ML algorithms are used in the AI solutions to improve the efficiency.

These powerful AI/ML solutions, like those created by AltaML are used to solve some of the challenges faced in the supply chain industry.

Supply Chain & Supply Chain Management

The supply chain is a combination of all the activities required to move a product/service from inception to the end users. The supply chain includes people, resources, information, channels and modes of transportation. All these entities are linked together to complete the cycle from procurement to fulfilment. Reverse logistics also come into play; consider waste management for fast fashion, or recycling. In this regard it is not just a supply chain, but a circular process.

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Supply chain management can be defined as a process to integrate all the activities required to fulfill the demand and supply life cycle. The Covid-19 pandemic has had a very negative impact on the global supply chain. Organizations that have always been focussed on lean supply chain management to optimize the cost and meet end-user demand are now needing to consider risk management and mitigation. With the help of technologies like AI/ML, a high level of efficiency and visibility in supply chain management can be achieved.

Pain Points in Logistics and Supply Chain Management

Supply chain management is a very complex process. The pandemic has created a lot of uncertainties in the global supply chain. The set of challenges include logistics and transportation issues, increased customer expectations, unexpected demand, lack of visibility and operational complexities.

Let us try to summarize these pain points:

  • Demand and supply planning: Unexpected increase and decrease in demand leads to speculative order placing and resultant excess inventory storage. A proper inventory management system helps the organization to keep a balance between demand and supply, reducing the “bullwhip effect” where small fluctuations are amplified as they travel upstream.

  • Reactive management: Unplanned events and uncertain notifications mean management is constantly reacting, rather than proactively planning ahead.The detrimental effects of a lack of scenario planning were made starkly evident during the pandemic.

  • Supply network planning: Lack of planning upstream and downstream in a network leads to shortage or excess of inventory. It can also cause deployment issues across the network. Insufficient inventory leads to long wait times and potential customer loss.

  • Safely and quality Inefficient supply chain makes it difficult to deliver products/services on time. As a result, maintaining a proper quality process and safety become a challenge.

  • Lack of information management: Critical and necessary information is not always available when needed. It leads to loss in sales and profit margin.

  • Scarcity of resources: This is a well-known problem in supply chain. Due to shortage of resources, logistics and supply chain cannot operate efficiently.

  • Cost inefficiency: Financial planning is very important for any supply chain. Organizations must have plan for financial flexibility and back-up to sustain disruption and increased cost.

  • Technical downtime: Any technical downtime can cause a problem in the supply chain process. So, a proper back-up and fail-over strategy should be in place to support downtime.

Role of Machine Learning in Supply Chain

The open question is - how do you make your supply chain less vulnerable to uncertainties? The market dynamics of supply chain management have changed a lot based on factors like changing work processes and volatile demand. Supply chain is no longer a linear deterministic process, where the work-flow is a step by step predefined sequence. Rather, supply chain is now a nondeterministic work-flow, where the sequence can be shuffled to optimize the process. Automation is required to create better supply chain management. (Read also: How does machine learning support the supply chain?)

To start with, integrating AI/ML in the supply chain process can automate various common and repetitive tasks. Applying an intelligent ML model can help organizations to select the best options and run their business efficiently. The large volume of data collected from warehouses, logistics, suppliers and transportation systems can be analyzed by AI solutions to predict the demand supply requirements and balance the entire eco-system. The advantages of an AI-driven system can be found in every step of the chain, from inception, procurement, order processing, inventory through to logistics and end user delivery.

Machine Learning Use Cases in Supply Chain Management

Supply chain management is a very complex process, which is heavily dependent upon multiple kinds of data. At every stage of the supply chain, data is collected and used for processing. Now, AI applications, like those created by AltaML, are playing an important role in the supply chain industry.

Machine learning is used to process the large volume of input data and train the ML model. As a result the ML model can predict more accurate result and train itself over the time period.

Here are some interesting ML use cases in supply chain.

  • Inventory and Warehouse Management: Inventory and warehouse management are key use cases for ML implementation. The inventory planning should be very efficient to balance the demand and supply cycle. ML algorithms can be applied on the data collected from various sources like historical data, seasonal demands, market movement (up and down), and promotions. And, the result can be used to improve the efficiency of inventory storage. Similarly, different ML models are also used to automate the warehouse processes.

  • Logistics Management: Machine learning is used to track the goods’ location starting from pick-up to delivery. ML is also used to predict the optimized route for transportation, as well as the most efficient mode, best lead time and greenhouse gas (GHG) emissions per mode selected.

  • Production and Quality Management: With the help of ML, checks can be made on the quality of the product and matched with the required specification. So, the production line is always well controlled and maintained. Computer vision can be used to facilitate quality control management practices for products coming off of a factory line, which is important for everything, from food to automotive parts.

  • Predictive Analytics: Predictive Analytics is very important for demand-supply management.ML can help to predict the demand in advance. So, the inventory is always balanced and optimized. Investment can be proactively redeployed within the network based on demand sensing signals.

  • Security and Prevention of Fraud: Machine learning models can analyze the large volume of data and raise an alert for fraudulent activities. For example, duplicate payments to vendors can be flagged and potential fraudulent charges mitigated. ML can be used to implement anti-fraud process and tighten security.

  • Delivery tracking and Customer Service: ML is also used to track the delivery of goods at every stage. External data sources can be used to reduce the lead time prediction error rate. This has proven this by improving accuracy by up to 85% when a package will be delivered from overseas. So, the customer is always updated with the latest status. It enhances the customer satisfaction and controls the end-to-end delivery.

Conclusion

As per Gartner prediction, 50% of the global companies will be using AI/ML by 2023, meaning supply chain efficiency must be increased manifold in the coming days. AI-driven supply chain management is the answer the industry must embrace.

In today’s competitive world, an efficient supply chain is critical to business success. Disruptive technologies like AI and ML play an important role to make it better every day.


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Written by Kaushik Pal | Contributor

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Kaushik is a technical architect and software consultant, having over 20 years of experience in software analysis, development, architecture, design, testing and training industry. He has an interest in new technology and innovation areas. He focuses on web architecture, web technologies, Java/J2EE, open source, WebRTC, big data and semantic technologies. Kaushik is also the founder of TechAlpine, a technology blog/consultancy firm based in Kolkata. The team at TechAlpine works for different clients in India and abroad. The team has expertise in Java/J2EE/open source/web/WebRTC/Hadoop/big data technologies and technical writing.

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