AI and NLP in the Supply Chain

With all this talk of Supply Chain issues, let’s explore how AI and NLP can help optimize the industry.

By VICTOR ANJOS

How can you create efficient supply chain management? This is an open question for many suppliers, distributors, manufacturers, and retailers. Today, amid shifting supply chain market dynamics, changing ways of working, increasingly volatile demand, businesses are wondering how to make their supply chain less vulnerable to disruption. Machine learning holds the answer to many well-known as well as emerging supply chain challenges.

Use cases of machine learning in the supply chain are numerous. The benefits of machine learning and AI can be traced in every part of the supply chain including procurement, manufacturing, inventory management, warehousing, logistics, and customer service. Let’s dive deeper into the advantages of machine learning in supply chain management and machine learning use cases in the supply chain.

KEY CHALLENGES IN THE SUPPLY CHAIN

Businesses can improve supply chain management using machine learning making it more resilient to any disruptions. The global supply chain market is grappling with uncertainty, fragility, and lack of transparency. According to the recent Supply Chain Complexity survey by Körber, only 1 in 10 businesses can stay ahead of their supply chain challenges. In addition to growing customer expectations, lack of visibility, and operational complexity, companies are now faced with a unique set of challenges: transportation complications, remote work, shortages because of unexpected increased demand, etc. According to McKinsey, there are 5 major sources of vulnerability in the supply chain caused by the pandemic. And machine learning use cases in the supply chain serve as a ready-made blueprint of activities regarding what supply chain professionals should begin with in order to solve major supply chain issues.

In recent years, we all have been witnessing the transformation of the traditional linear supply chain into digital supply networks (DSNs). COVID-19 has only accelerated this process making companies revisit their global supply chain strategies amid the new reality. With the help of technologies such as IoT, artificial intelligence, and machine learning, it is possible to transform traditional, linear supply chains into connected, intelligent, scalable, customizable digital supply networks.

”Traditional supply chains follow specific, predefined workflows: Do A, then B, then C. This is how most manufacturing execution systems work. The opposite is a nondeterministic system, where workflows aren’t predefined and the automation itself has flexibility in how it handles business rules. Enhanced digital manufacturing solutions can shuffle and optimize manufacturing workflows, avoid unplanned downtime, and reduce product line switching costs.”

There are 8 types of machine learning use cases in the supply chain. So let’s take a closer look at them:

Inventory Management

Storing and maintaining inventory in a good condition is costly. So supply chain professionals should approach inventory planning very thoroughly as it has a direct impact on a company’s cash flow and profit margins. Inventory management is one of the most typical machine learning use cases in the supply chain. Machine learning can help solve the problem of under- or over-stocking. Based on the data that can be sourced from many areas like the marketplace environment, seasonal trends, promotions, sales, and historic analysis, with ML you can predict the demand growth. And you can prepare to fill your stores in advance as well as prevent excesses of goods or important parts for manufacturing.

Warehouse Management

In warehouses, machine learning is used to automate manual work, predict possible issues, and reduce paperwork for warehouse staff. For example, computer vision makes it possible to control the work of the conveyor belt and predict when it is going to get blocked. NLP and OCR allow warehouse specialists to automatically detect the arrival of packages and change their delivery statuses. Cameras scan barcodes and labels on the package and all the necessary information goes directly into the system.

Also, machine learning helps to program autonomous vehicles and robots which are widely used in warehouses. With the help of guides that are built in the system, autonomous vehicles and robots help receive, pack/unpack, transport as wells as upload/unload boxes. Computer vision in this case helps find a free place for a box, control whether it is placed correctly, and prevent collision of robots and vehicles in warehouses.

Logistics and Transportation

ML helps understand where a package is in the entire logistics cycle. It allows supply chain professionals to track the location of goods during transportation. Also, it provides visibility into the conditions under which the package is being transported. With the help of sensors, retailers can monitor such parameters as humidity, vibration, temperature, etc.

Besides, ML helps with real-time route optimization. It tracks weather and road conditions and gives recommendations on how to optimize the route and reduce driving time. This way, trucks can be diverted any time on their way when a more cost-effective route is possible.

We will explore this topic in a little more depth below.

Production

With ML, it is possible to identify quality issues in line production at the early stages. For instance, with the help of computer vision, manufacturers can check if the final look of the products corresponds to the required quality level. If the products have some defects, it becomes easy to detect them before they reach the customers.

One of the other wide-spread use cases of machine learning in the supply chain is predictive maintenance of the equipment. ML ensures reactive and preventative maintenance of equipment based on real-time asset data rather than a predefined calendar. By improving asset maintenance, supply chain professionals can significantly decrease maintenance costs.

Also, ML helps to reduce the number of no-fault-found (NFF) cases. NFF is a unit that is removed from service following a complaint of the perceived fault of the equipment. If there is no anomaly detected, the unit is returned to service with no repair performed. The lower the number of such incidents is, the more efficient the manufacturing process gets.

”Machine learning uses complex algorithms to suggest optimal solutions to business leaders so that they can make well-informed decisions.”

Chatbots

Intellectually independent chatbots which are based on the machine learning technology are trained to understand specific keywords and phrases that trigger a bot’s reply. They are widely used in supplier relationship management, sales, and procurement management allowing staff focus on value-added tasks instead of getting frustrated answering simple queries. With time, they train themselves to understand more and more questions. They learn and train from experience.

For example, you write to a chatbot: “I have a problem with shipping the package”. The bot would understand the words “problem” “shipping” “package” and would provide a predefined answer based on these phrases.

Customer Service

Consumers expect up-to-date information on their delivery status. Thanks to ML, it is possible to predict the delivery of the parcel taking into account all the changing conditions. As a result, consumers receive a much stronger customer experience with more accurate delivery date predictions. With machine learning, retailers can:

Identify parcels with the risk of an issue and suggest mitigation measures
Automate notification flow depending on previous consumer interactions
Determine when to communicate with consumers for maximum engagement

Also, machine learning techniques allow the company to offer an exceptional customer experience. ML does this by enabling the company to gain insights into the correlation between product recommendations and subsequent website visits by customers.

Security

Machine learning algorithms can analyze huge amounts of data and draw patterns for every business to protect it from fraud. For instance, in the supply chain, ML helps identify fraudulent transactions, prevent credential abuse, accelerate fraud investigations, and automate anti-fraud processes. Moreover, with ML, supply chain professionals can automate the process of monitoring whether all parts as well as finished products meet the quality or safety standards.

Business

From a business perspective, machine learning provides valuable insights that simplify and accelerate decision-making. It enables senior executives to quickly evaluate the best and worst possible scenarios. Machine learning uses complex algorithms to suggest optimal solutions to business leaders so that they can make well-informed decisions.

For instance, stock level analysis can identify when products are declining in popularity and are reaching the end of their life in the retail marketplace. Price analysis can be compared to costs in the supply chain and retail profit margins to establish the best combination of pricing and customer demand.

”With machine learning, retailers can:

Identify parcels with the risk of an issue and suggest mitigation measures
Automate notification flow depending on previous consumer interactions
Determine when to communicate with consumers for maximum engagement”

Better Scalability

Big corporations spend enormous resources on marketing to reach potential customers. Most small businesses lack the financial base to engage in big marketing. Small businesses need marketing to stand any chance of surviving. Most small businesses go all out to tell people about their products and services.

The benefits of marketing are enormous for all enterprises. Marketing helps you build a consistent customer base to keep your business afloat.

Another area of marketing that AI has revolutionized is email marketing. Email marketing has the highest return on investment for all marketing strategies. Before AI, many marketers committed a lot of errors in their marketing campaigns. One of these errors was not knowing the right time to install a particular strategy. For example, a premature sale to a potential customer may chase the prospect away.

With AI, marketers can program their campaigns to target potential customers only when they are ready to take a step.

”Goldman Sachs estimates that automating contract management accelerates negotiation cycles by 50% and while cutting the operating costs by up to 30%. ”

Summing it up

Machine learning use cases in the supply chain help retailers, suppliers and distributors drive transformational changes that are so much needed today in the face of the pandemic. Machine Learning delivers unprecedented value to supply chain operations: from cost savings through reduced operational overhead and risk mitigation, to enhanced supply chain forecasting, speedy deliveries, and improved customer service, to name a few. McKinsey forecasts that the most significant benefits of machine learning will be in providing supply chain professionals with more significant insights into how supply chain performance can be enhanced, anticipating anomalies in logistics costs and performance before they occur. Machine learning is also providing insights into where automation can deliver the most significant scale advantages.

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