9 Ways Machine Learning Can Transform Supply Chain Management
In a fiercely competitive market where businesses are constantly striving to enhance profit margins, reduce costs, and provide exceptional customer experience, disruptive technologies like Machine Learning (ML) and Artificial Intelligence (AI) offer some excellent opportunities.
Machine Learning techniques process large volumes of real-time data to bring automation into the process and improve decision making – across various industries.
Machine Learning in Supply Chain
ML and AI for business have recently become buzzwords across different verticals. But what do they actually mean for modern supply chain management?
To begin with, integrating machine learning in supply chain management can help automate a number of 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 businesses today are showing interest in the applications of machine learning, from its varied advantages to fully leveraging the huge 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 extremely 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 revolutionise 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.
What is Machine Learning?
Machine learning is a subset of artificial intelligence that allows an algorithm, software or a system to learn and adjust without being specifically 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 analysed and used to improve how the technology functions.
Machine Learning (ML) models, based on algorithms, are great at analysing 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.
How Machine Learning is Transforming Supply Chain Management: 9 Ways
Machine Learning is a complex yet interesting subject that can solve a number of issues across industries.
Supply chain, being a heavily data reliant industry, has many applications of machine learning. Elucidated below are top 9 use cases of machine learning in supply chain management which can help drive the industry towards efficiency and optimization.
1. Predictive Analytics
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 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 business can respond to problems.
2. Automated Quality Inspections For Robust Management
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.
3. Real-Time Visibility To Improve Customer Experience
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 constantly 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.
4. Streamlining Production Planning
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 which helps in identification of possible areas of inefficiency and waste.
Further, the use of machine learning in supply chain in creating a more adaptable environment to effectively deal with any sort of disruption is noteworthy.
5. Reduces Cost and Response Times
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.
6. Warehouse Management
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 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.
7. Reduction in Forecast Errors
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 supply chain also ensures that it is done with the greatest 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 supply chain can reduce forecast errors up to 50%.
8. Advanced Last-Mile Tracking
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 supply chain constitutes 28% of all delivery costs.
Machine learning in 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 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.
9. Fraud Prevention
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 normal 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.
Challenges In Logistics and 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
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 supply chain is largely 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.
- Quality and safety
With mounting pressures to deliver products on time to keep the supply chain assembly line moving, maintaining a dual check on quality as well as safety becomes a big challenge for supply chain firms. It could produce a big 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.
- Problems due to scarce resources
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 various factors enable early planning and stocking accordingly. Offering new insights into various aspects of the supply chain, ML has also made the management of the inventory and team members become super simple.
- Inefficient supplier relationship management
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.
Why is Machine Learning Important to Supply Chain Management?
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-
- Cost efficiency due to machine learning, which systematically drives waste reduction and quality improvement
- Optimisation of product flow in the supply chain without the supply chain firms needing to hold much inventory
- Seamless supplier relationship management due to simpler, faster and proven administrative practices
- Machine learning helps derive actionable insights, allowing for quick problem solving and continual improvement.
Companies Using Machine Learning to Improve Their Supply Chain Management
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 heavy investments in intelligent software systems, transportation and warehousing.
b) Microsoft Corporation – Technology
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 a huge amount of data which needs to be integrated on a central 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 a 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.
c) Alphabet Inc.– Internet Conglomerate
A well known technological giant and a highly innovative technological company, Alphabet relies on a flexible and responsive Supply Chain which can collaborate across regions in a seamless fashion.
Alphabet’s Supply Chain leverages machine learning, AI and robotics to become completely automated.
d) Procter & Gamble – Consumer Goods
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 replaces the jobs of entire crew members.
Existing ships of the company use algorithms to accurately sense what is around them in the water and accordingly classify items based on the danger they pose to the ship. ML and AI algorithms can also be used to track ship engine performance, monitor security and load and unload cargo.
Transform Supply Chain Management with Machine Learning Power
Improving the efficiency of the supply chain plays a crucial role in any enterprise. Operating their businesses within tough profit margins, any kind of process improvements can have a great impact on the bottom line profit.
Innovative technologies like machine learning makes it easier to deal with challenges of volatility and forecasting demand accurately in global supply chains. Gartner predicts that at least 50% 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 supply chain industry.
But, to be able to reap 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 resources availability in the supply chain industry.
FAQs
1. How can machine learning improve demand forecasting accuracy in supply chains?
Unlike traditional statistical methods, Machine learning uses its ability to analyze large and complex data sets while detecting sophisticated patterns and nonlinear relationships, improving forecasting accuracy.
2. Can machine learning help identify and mitigate potential supply chain disruptions?
Machine learning algorithms use proficient algorithms that can conduct in-depth analyses of historical data to discover interesting insights and patterns that can indicate potential supply chain disruptions.
3. How does machine learning optimize inventory levels and reduce holding costs?
Different algorithms like neural networks, decision trees, and reinforcement learning are used in inventory optimization. They analyze supplier lead times, previous sales data, and related variables to optimize inventory levels and reduce holding costs.
4. How is AI transforming supply chain management?
Organizations today are using AI in numerous facets of global supply chains, such as tracking inventory, predicting demands for specific parts and components, enhancing worker safety, managing warehouse capacity, optimizing shipping and delivery, and ensuring the integrity of transactions.
5. Is it expensive to implement machine learning in supply chain management?
Implementing machine learning in supply chain management can be expensive due to initial setup costs, data infrastructure requirements, and continual maintenance expenses.
6. Can machine learning algorithms optimize warehouse operations and picking strategies?
Machine learning algorithms can be used in a warehouse to automate manual tasks, spot potential issues, and reduce staff paperwork. Furthermore, if equipped with computer vision, it can be leveraged to identify warehouse packages and scan barcodes.
It can also enhance navigation and coordination in fulfillment centres, resulting in better product placement and monitoring of warehouse equipment.
