Nine ways machine learning can improve supply chain management

Efficiency and cost-effectiveness are the biggest challenges facing supply chain management today. Businesses are continually striving to reduce costs, enhance profit margins, and provide exceptional customer service. In such a competitive market, disruptive technologies like Machine Learning (ML) and Artificial Intelligence (AI) have opened up exciting opportunities for companies. Are you grabbing these opportunities?

Artificial Intelligence and Machine Learning have recently become buzzwords across different verticals, but what do they mean for modern supply chain management?

To begin with, integrating machine learning in supply chain management can help automate several mundane tasks and allow the enterprises to focus on more strategic and impactful business activities.

Using intelligent machine learning software, supply chain managers can optimise inventory and find most suited suppliers to keep their business running efficiently. An increasing number of companies today are showing interest in the applications of machine learning, from its varied advantages to fully leveraging the vast amounts of data collected by warehousing, transportation systems, and industrial logistics.

It can also help enterprises create an entire machine intelligence-powered supply chain model to mitigate risks, improve insights, and enhance performance, all of which are incredibly crucial to build a globally competitive supply chain model.

A recent study by Gartner also suggests that innovative technologies like Artificial Intelligence (AI) and Machine Learning (ML) would disrupt existing supply chain operating models significantly in the future. Considered as one of the high-benefit technologies, ML techniques enable efficient processes resulting in cost savings and increased profits.

Before going into the details of how Machine Learning can revolutionize the supply chain and discussing the examples of companies successfully using ML in their supply chain delivery, let’s first talk a bit about Machine Learning itself.

Machine learning is a subset of artificial intelligence that allows an algorithm, software, or a system to learn and adjust without being specially programmed to do so.

ML typically uses data or observations to train a computer model wherein different patterns in the data (combined with actual and predicted outcomes) are analyzed and used to improve how the technology functions.

Machine Learning (ML) models, based on algorithms, are great at analyzing trends, spotting anomalies, and deriving predictive insights within massive data sets.

These powerful functionalities make it an ideal solution to address some of the main challenges of the supply chain industry.

Here are a few of the challenges faced by logistics and supply chains that Machine Learning and Artificial Intelligence-powered solutions can solve:

Inventory management is extremely crucial for supply chain management as it allows enterprises to deal and adjust for any unexpected shortages. No supply chain firm would want to halt their company’s production while they launch a hunt to find another supplier. Similarly, they wouldn’t want to overstock as that starts affecting the profits.

Inventory management in the supply chain is mainly about striking a balance between timing the purchase orders to keep the operations going smoothly while not overstocking the items they won’t need or use.

With mounting pressures to deliver products on time to keep the supply chain assembly line moving, maintaining a double check on quality as well as safety becomes a big challenge for supply chain firms. It could produce a significant safety hazard to accept substandard parts not meeting the quality or safety standards.

Further, environmental changes, trade disputes, and economic pressures on the supply chain can easily turn into issues and risks that quickly snowball throughout the entire supply chain causing significant problems.

Issues faced in logistics and supply chain due to the scarcity of resources are well known. But the implementation of AI and machine learning in the supply chain and logistics has made the understanding of various facets much easier. Algorithms predicting demand and supply after studying multiple factors enable early planning and stocking accordingly. Offering new insights into numerous aspects of the supply chain, ML has also made the management of the inventory and team members super simple.

A steep scarcity of supply chain professionals is yet another challenge faced by logistics firms that can make the supplier relationship management cumbersome and ineffective.

Machine learning and artificial intelligence can offer useful insights into supplier data and can help supply chain companies make real-time decisions.

With some of the largest and renowned firms beginning to pay attention to what machine learning can do to improve the efficiency of their supply chains, let’s understand how machine learning in supply chain management addresses the problems and what are the current applications of this powerful technology in supply chain management.

There are several benefits that machine learning delivers to supply chain management including:

Machine Learning is a sophisticated yet exciting subject that can solve several issues across industries.

The supply chain, being a big data reliant industry, has many applications of machine learning. Elucidated below are the top 9 use cases of machine learning in supply chain management, which can help drive the industry towards efficiency and optimisation.

There are several benefits of accurate demand forecasting in supply chain management, such as decreased holding costs and optimal inventory levels.

Using machine learning models, companies can enjoy the benefit of predictive analytics for demand forecasting. These machine learning models are adept at identifying hidden patterns in historical demand data. Machine learning in the supply chain can also be used to detect issues in the supply chain even before they disrupt the business.

Having a robust supply chain forecasting system means the business is equipped with resources and intelligence to respond to emerging issues and threats. And, the effectiveness of the response increases proportionally to how fast the enterprise can react to problems.

Logistics hubs usually conduct manual quality inspections to inspect containers or packages for any kind of damage during transit. The growth of artificial intelligence and machine learning have increased the scope of automating quality inspections in the supply chain lifecycle.

Machine learning-enabled techniques allow for automated analysis of defects in industrial equipment and to check for damages via image recognition. The benefit of these power automated quality inspections translates to reduced chances of delivering defective or faulty goods to customers.

A Statista survey identified visibility as an ongoing challenge that grapples the supply chain businesses. A thriving supply chain business heavily depends on visibility and tracking, and continuously looks for technology that can promise to improve visibility.

Machine learning techniques, including a combination of deep analytics, IoT, and real-time monitoring, can be used to improve supply chain visibility substantially. Thus helping businesses transform customer experience and achieve faster delivery commitments. Machine learning models and workflows do this by analysing historical data from varied sources, followed by discovering interconnections between the processes along the supply value chain.

An excellent example of this is Amazon using machine learning techniques to offer exceptional customer experience to its users. ML does this by enabling the company to gain insights into the correlation between product recommendations and subsequent website visits by customers.

Machine learning can play an instrumental role in optimising the complexity of production plans. Machine learning models and techniques can be used to train sophisticated algorithms on the already available production data in a way that helps in the identification of possible areas of inefficiency and waste.

Further, the use of machine learning in the supply chain in creating a more flexible environment to deal with any sort of disruption effectively is noteworthy.

An increasing number of B2C companies are leveraging machine learning techniques to trigger automated responses and handle demand-to-supply imbalances, thus minimising the costs and improving customer experience.

The ability of machine learning algorithms to analyse and learn from real-time data and historic delivery records helps supply chain managers to optimise the route for their fleet of vehicles leading to reduced driving time, cost-saving and enhanced productivity.

Further, by improving connectivity with various logistics service providers and integrating freight and warehousing processes, administrative and operational costs in the supply chain can be reduced.

Efficient supply chain planning is usually synonymous with warehouse and inventory-based management. With the latest demand and supply information, machine learning can enable continuous improvement in the efforts of a company towards meeting the desired level of customer service level at the lowest cost.

Machine learning in the supply chain with its models, techniques, and forecasting features can also solve the problem of both under or overstocking and completely transform your warehouse management for the better.

Using AI and ML, you can also analyse big data sets much faster and avoid the mistakes made by humans in a typical scenario.

Machine Learning serves as a robust analytical tool to help supply chain companies process large sets of data.

Apart from processing such vast amounts of data, machine learning in the supply chain also ensures that it is done with the enormous variety and variability, all thanks to telematics, IoT devices, intelligent transportation systems, and other similar powerful technologies. This enables supply chain companies to have much better insights and help them achieve accurate forecasts. A report by McKinsey also indicates that AI and ML-based implementations in the supply chain can reduce forecast errors by up to 50 per cent.

Last-mile delivery is a critical aspect of the entire supply chain as its efficacy can have a direct impact on multiple verticals, including customer experience and product quality. Data also suggests that the last-mile delivery in the supply chain constitutes 28 per cent of all delivery costs.

Machine learning in the supply chain can offer great opportunities by taking into account different data points about the ways people use to enter their addresses, and the total time is taken to deliver the goods to specific locations. ML can also offer valuable assistance in optimising the process and providing clients with more accurate information on the shipment status.

Machine learning algorithms are capable of both enhancing the product quality and reducing the risk of fraud by automating inspections and auditing processes followed by performing real-time analysis of results to detect anomalies or deviation from standard patterns.

In addition to this, machine learning tools are also capable of preventing privileged credential abuse, which is one of the primary causes of breaches across the global supply chain.

Here are some of the top companies using machine learning to enhance the productivity of their supply chain management:

One of the renowned supply chain leaders in the eCommerce industry, Amazon, leverages technologically advanced and innovative systems based on artificial intelligence and machine learning such as automated warehousing and drone delivery.

Amazon’s robust supply chain has direct control over the main areas like packaging, order processing, delivery, customer support, and reverse logistics due to substantial investments in intelligent software systems, transportation, and warehousing.

The supply chain system of the technology giant Microsoft heavily relies on predictive insights driven by machine learning and business intelligence.

The company has a massive product portfolio that generates an enormous amount of data that needs to be integrated on a middle level for predictive analysis and driving operational efficiencies.

Machine Learning techniques have allowed the company to build a seamlessly integrated supply chain system enabling them to capture data in real-time and analyse the same. Further, the company’s robust supply chain utilises proactive and early warning systems to assist them in mitigating the risk and quick query resolution.

A well known technological giant and a highly innovative technical company, Alphabet relies on a flexible and responsive Supply Chain that can seamlessly collaborate across regions.

Alphabet’s Supply Chain leverages machine learning, AI, and robotics to become completely automated.

The consumer goods leader, P&G, has one of the most complex supply chains with a massive product portfolio. The company excellently leverages machine learning techniques such as advanced analytics and application of data for end-to-end product flow management.

Rolls Royce, in partnership with Google, creates autonomous ships where instead of just replacing one driver in a self-driving car, machine learning and artificial intelligence technology replace the jobs of entire crew members.

Existing ships of the company use algorithms to sense what is around them in the water accurately and accordingly classify items based on the danger they pose on the boat. ML and AI algorithms can also be used to track ship engine performance, monitor security, and load and unload cargo.

Improving the efficiency of the supply chain plays a crucial role in any enterprise. Operating their businesses within tight profit margins, any kind of process improvements can have a significant impact on the bottom line profit.

Innovative technologies like machine learning make it easier to deal with challenges of volatility and forecasting demand accurately in global supply chains. Gartner predicts that at least 50 per cent of global companies in supply chain operations would be using AI and ML related transformational technologies by 2023. This is a testament to the growing popularity of machine learning in the supply chain industry.

But, to be able to reap the full benefits of machine learning, businesses need to plan for the future and start investing in machine learning and related technologies today to enjoy increased profitability, efficiency and better resource availability in the supply chain industry.