How to ensure 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.” - Automate Your Supply Chain With Forrester’s Automation Framework, Forrester Research Inc., January 13, 2020
Benefits of machine learning in the supply chain
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. Let’s take a closer look at the full list of advantages machine learning has to offer:
Machine learning use cases in the supply chain
Machine learning applications in the supply chain are revolutionizing the way retailers and suppliers work. As a branch of artificial intelligence, machine learning uses data to train a computer model so it can adjust to conditions without being programmed to do so. This way, the machine can teach itself over time, improving the accuracy of its own algorithms. There are a number of machine learning methodologies used in the supply chain. Gartner predicts that by 2023, intelligent algorithms and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.
There are 8 types of machine learning use cases in the supply chain. So let’s take a closer look at them:
#1 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.
For the forecast to be accurate, you need to have a wide range of data. When the number of data sets is insufficient for the effective analysis, machine learning offers several methods of how to solve the problem:
- Data augmentation allows you to significantly increase the diversity of data available for training models, without actually collecting new data. The augmentation techniques used in deep learning applications depends on the type of data. To augment plain numerical data, techniques such as SMOTE or SMOTE NC are popular. For unstructured data such as images and text, the augmentation techniques vary from simple transformations to neural network generated data, based on the complexity of the application.
- Incremental learning is a method of machine learning which does not require a large amount of data for training a model. Instead, learning starts with a very simple model typically predicting the average value with some degree of deviation. When a data scientist enters new data examples, the model is trained to be able to predict more accurate results. Over time, the number of data sets is good enough to make reliable forecasts.
- Reinforcement learning is one of three basic machine learning techniques alongside supervised learning and unsupervised learning. It uses rewards and punishment as signals for positive and negative behavior. In robotics and industrial automation, RL is used to enable the robot to create an efficient adaptive control system for itself which learns from its own experience and behavior.
When it comes to data, there arises the question which data storage solution to choose: data warehouse or data lake. Data lakes are often used as a part of machine learning or advanced analytics solutions. They are often used in ML projects as they let collect data from multiple sources in real-time and store it in its original format. A data lake is ideal for those who want an in-depth analysis of broad-spectrum data that is gathered over a longer period of time, while a data warehouse is perfect for operational processes and day-to-day activities. However, many companies are now using both storage options, especially when a data warehouse is built upon a data lake, and it uses the data from a DL that has been cleansed and structured.
Another example of the ML application in supply chain is the case of computer vision (CV) in inventory management. It is used extensively in a number of ways. First, it is applied in order to count and classify items that arrive. Also, CV helps detect visual damages of the package. With the help of computer vision, the software is also able to classify objects it “sees”. For example, robots equipped with cameras will inspect your storages and automatically build a real-time picture of your inventory. CV is one of the areas where all sort of machine learning techniques - supervised learning, unsupervised learning, and reinforcement learning - can be applied.
#2 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.
One of our clients, a German-based Fortune 100 multinational engineering and technology company, needed to streamline management of 400+ warehouses around the globe. They partnered with the N-iX specialists to modernize and build a scalable logistics platform. The solution is in the development phase. N-iX works on a computer-vision solution for cameras installed in warehouses based on industrial optic sensors and lenses and Nivida Jetson devices. This solution will allow the client automatically detect arriving packages, scan barcodes, and change the delivery statuses of the boxes. Also, our team is responsible for the development of the multiplatform CV mobile app. This product will help the client with object detection, package damage detection, OCR, and NLP for document processing. The modernized and scalable logistics platform will significantly improve the efficiency of warehouses in over 60 countries, reducing operational overhead and warehouse downtime.
#3 Logistics & 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.
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.
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.
#6 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.
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.
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.
How to make ML work for supply chain management
There are three significant steps you should take to adopt machine learning in supply chain management. They are:
Understand your supply chain’s structure
Before implementing machine learning into your supply chain, you should evaluate your entire supply chain’s structure:
- Determine the critical components of your operations.
- Conduct a detailed analysis of the supplier network including Tier 1 suppliers and sub-tier suppliers.
- Identify hidden relationships and nodes of interconnectivity.
- Quantitatively diagnose the relative fragility of the supply chain.
- Identify bottlenecks and risk factors in the supply chain.
- Draw meaningful comparisons with peers and industry benchmarks.
- Assess the security of the supply chain.
- Evaluate your functional maturity against the process, people, and technology.
Establishing transparent business KPIs and calculate ROI
To understand under what circumstances machine learning use cases in your supply chain would be advantageous to your business, you need to conduct a Discovery Phase and calculate ROI. You need to estimate TCO and the profitability you will gain in the short term and in the long run.
Also, it is important to prepare a detailed plan defining your goals and requirements needed to reach them. To eliminate inconsistencies, it is obligatory to align machine learning KPIs with business KPIs. In other words, you should define the business problem in ML terms.
Ensuring an effective ML engineering process
The success of machine learning use cases in the supply chain heavily depends on the following aspects:
- Set up a multifunctional team of professionals with expertise in data science, DevOps, Python, Java, QA, business analysis, etc.
- Start with a business problem statement.
- Establish the right success metrics.
- Choose the right tech stack.
- Consider your data readiness: focus on data quality and quantity.
- Develop, train, test, and optimize models.
- Deploy and retrain models.
- Monitor model performance.
Use cases of machine learning in supply chain management are versatile. Here we have listed the ones which bring the most value to supply chain professionals. If you have to manage a wide network of suppliers, warehouses, logistics service partners, supply chain management can become a daunting task. But technologies such as machine learning and AI can help you at all stages of the supply chain management. ML algorithms will correctly forecast demand, improve logistics management, help you reduce paperwork, and automate manual processes. As a result, you will get end-to-end visibility into your supply chain while ensuring it works more efficiently, requires fewer operational costs, and is less vulnerable to disruptions.
- Automate your supply chain with Forrester’s Automation Framework, Forrester Inc., January 13, 2020
- Supply-chain recovery in coronavirus times—plan for now and the future by McKinsey & Company
- Five insights: what supply chain complexity looks like in 2020 by Körber
- COVID-19: Managing supply chain risk and disruption by Deloitte
- 7 Machine Learning best practices by Oracle
- Automation in logistics: Big opportunity, bigger uncertainty by McKinsey & Company
- How to improve supply chains with machine learning: 10 proven ways by Forbes