In the supply chain industry, rising customer expectations have given rise to larger product ranges, more complex logistics, and shamelessly fast lead times. All of this has led to soaring costs throughout the supply chain network. And minimizing the effect of these factors manually at each individual level is again a recipe for magnified operational costs. This is where Machine Learning in logistics can help breathe a sigh of relief!
Let’s explore how-
Machine Learning in Supply Chain
Integrating ml 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.
Before going into the details of how Machine Learning can revolutionise 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.
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.
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. Inventory management in the 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.
- 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.
- 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.
How Machine Learning Can Improve Supply Chain Efficiency
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.
Also read RPA in Telecom
Top 7 Use Cases of Machine Learning in Supply Chain
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 7 use cases of ml in logistic management which can help drive the industry towards efficiency and optimization.
There are several benefits of accurate demand forecasting in supply chain management, such as decreased holding costs and optimal inventory levels.
Using machine learning solution 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.
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.
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. Machine learning does this by enabling the company to gain insights into the correlation between product recommendations and subsequent website visits by customers. These insights make Amazon’s recommendation engine very powerful and effective in driving up their revenue.
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.
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 the supply chain can reduce forecast errors up to 50%.
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 the 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.
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.
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:
a) com – eCommerce
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.
e) Rolls Royce – Automotive
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.
With thin profit margins in the current supply chain scenario, any kind of process improvement can have a huge impact on the bottom line profitability. Machine Learning techniques in supply chain processes large volumes of real-time data to bring automation into the process and improve decision making.
While Machine Learning can help cut costs and improve profit margins, it is crucial to plan the implementation of machine learning after consulting with machine learning experts.