How Machine Learning Is Transforming Supply Chain Management
Supply chain management is a complicated business. A lack of synchronization or one missing entity can interrupt the entire chain and result in millions in losses.
In a market environment where businesses are continually striving to cut costs, increase profits, and enhance customer experience, disruptive technologies like machine learning offer a window of opportunity. By exploiting the enormous amount of real-time data and leveraging the cloud power, it improves decision making, process automation, and optimization. It can create an entire machine intelligence-powered supply chain model. It also helps companies improve insights, mitigate risks, and enhance performance, all of which are crucial as the global supply chain war wages on.
Gartner recently announced that innovative technologies like blockchain and Artificial Intelligence (AI)/machine learning would significantly disrupt existing supply chain operating models. In addition to advanced analytics and Internet of Things (IoT), machine learning is considered one of the high-benefit technologies. This is because it allows dynamic shifts across industries and enables efficient processes that result in significant revenue gains or cost savings.
So, it is no surprise then that, in another industry update, Gartner predicted that at least 50% of global companies would be using AI-related transformational technologies in supply chain operations by 2023.
There are three key ways in which these transformational technologies empower businesses:
Monitoring: By connecting equipment, products, and vehicles with IoT sensors, companies can monitor goods and operations in real time.
Analyzing: Advanced analytics convert data into actionable insights and help businesses understand the reason behind specific incidents and how they impact the business.
Acting: Valuable insights as a result of data crunching help businesses address planning challenges and automate processes to improve efficiency.
So, adopting machine learning in supply chains is critical for companies to stay competitive in the long run. However, what aspects of the supply chain will be impacted by machine learning? Let us find out.
A Myriad of Benefits to Supply Chains
If you get the algorithms right, the benefits of using machine learning are innumerable. The algorithms can predict supply trends based on human behavior, resulting in personalized customer service with lower inventories and better utilization of resources. We take a look at several such benefits of machine learning below.
Brings Real-Time Visibility Which Improves Customer Experience
According to a Statista survey, visibility is a significant organizational challenge for 21% of supply chain professionals. Visibility has been a buzzword in supply chain circles for more than a decade now and every technology so far has promised to improve visibility in some way. But, is machine learning contributing anything here?
The combination of IoT, deep analytics, and real-time monitoring is improving supply chain visibility, helping businesses achieve delivery commitments and transforming the customer experience. By examining historical data from various sources, machine learning workflows discover complex interconnections between various processes along the value chain.
Amazon is a prime example as it is using machine learning to enhance its customer experience by gaining an understanding of how product recommendations influence customers’ store visits.
Cuts Costs and Reduces Response Times
As per Amazon’s regulatory filing in 2017, their shipping costs increased from $11.5 billion in 2015 to $21.7 billion in 2017. And, it’s not just Amazon. Many other players are struggling because of rising shipping costs. In fact, in one survey, more than 24% of supply chain professionals expressed that delivery costs are the biggest challenge for B2C companies.
By applying machine learning to handle demand-to-supply imbalances and trigger automated responses, businesses can improve the customer experience, while minimizing costs. Operational and administrative costs can also be reduced by integrating freight and warehousing processes and improving connectivity with logistics service providers.
Machine learning algorithms’ ability to analyze and self-learn from historic delivery records and real-time data helps managers and dispatchers optimize the route for each vehicle. This allows them to save costs, reduce driving time, and increase productivity.
Machine learning can also be used to detect issues in the supply chain before they disrupt the business. Having an effective supply chain forecasting system means a business has the intelligence to respond to emerging threats. And, the faster a business can respond to problems, the more effective the response will be.
Streamlines Production Planning and Identifies Demand Patterns
When it comes to machine learning’s role in optimizing complex supply chains, production planning is just the tip of the iceberg.
Sophisticated algorithms are trained on existing production data in such a way that they start identifying future buying, customers’ ordering behavior, and possible areas of waste. This helps businesses tailor production and transport processes to actual demand as well as improve their relationships with specific customers.
For example, by anticipating and acting on the specific needs of your customers before they even arise, businesses can establish themselves as reputed brands capable of recognizing customer needs.
There is so much volatility in global supply chains that it will be challenging to forecast demand accurately, without technologies like machine learning. However, reaping the full benefits of machine learning might take years. So, businesses should plan for the future and start taking advantage of the machine learning solutions available today.
Investing in machine learning and the related technologies today means increased profitability and more resources for your business tomorrow. Businesses that can use machine learning in their supply chains will have better plans, resulting in less “firefighting” and fewer inefficiencies.
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