Bottom line:Machine learning makes it possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks’ success, while constantly learning in the process.
Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms are finding these new patterns in supply chain data daily, without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data with many using constraint-based modeling to find the core set of factors with the greatest predictive accuracy. Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management and more are becoming known for the first time. New knowledge and insights from machine learning are revolutionizing supply chain management as a result.
The ten ways machine learning is revolutionizing supply chain management include:
- Machine learning algorithms and the apps running them are capable of analyzing large, diverse data sets fast, improving demand forecasting accuracy. One of the most challenging aspects of managing a supply chain is predicting the future demands for production. Existing techniques range from baseline statistical analysis techniques including moving averages to advanced simulation modeling. Machine learning is proving to be very effective at taking into account factors existing methods have no way of tracking or quantifying over time. The example below shows how a wide spectrum is being used to accomplish demand forecasting, and Lennox is using machine learning today. Source: Machine Learning - A Giant Leap for Supply Chain Forecasting, Material Handling and Logistics Conference (PDF, 28 pp., no opt-in).
- Reducing freight costs, improving supplier delivery performance, and minimizing supplier risk are three of the many benefits machine learning is providing in collaborative supply chain networks. The following is an example of how machine learning is being used today to identify horizontal collaboration synergies between multiple shipper networks. Source: Machine Learning & AI in Transport and Logistics, Frank Salliau & Sven Verstrepen Logistics Meets Innovation Vlerick Brussels – Nov. 15th, 2017 (PDF., 82 pp., no opt-in).
- Machine Learning and its core constructs are ideally suited for providing insights into improving supply chain management performance not available from previous technologies. Combining the strengths of unsupervised learning, supervised learning and reinforcement learning, machine learning is proving to be a very effective technology that continually seeks to find key factors most affecting supply chain performance. Each of the endpoints defined in the taxonomy below is derived entirely by algorithm-based logic, which ensures algorithms scale across a global enterprise. Source: DHL, Artificial Intelligence in Logistics, 2018 (PDF, 45 pp., no opt-in).
- Machine learning excels at visual pattern recognition, opening up many potential applications in physical inspection and maintenance of physical assets across an entire supply chain network. Designed using algorithms that quickly seek put comparable patterns in multiple data sets, machine learning is also proving to be very effective at automating inbound quality inspection throughout logistics hubs, isolating product shipments with damage and wear. The machine learning algorithms in IBM’s Watson platform were able to determine if a shipping container and.or product were damaged, classify it by damage time, and recommend the best corrective action to repair the assets. Watson combines visual and systems-based data to track, report and make recommendations in real-time. Source: DHL, Artificial Intelligence in Logistics, 2018 (PDF, 45 pp., no opt-in).
- Gaining greater contextual intelligence using machine learning combined with related technologies across supply chain operations translates into lower inventory and operations costs and quicker response times to customers. Machine learning is gaining adoption in Logistics Control Tower operations to provide new insights into how every aspect of supply chain management, collaboration, logistics and warehouse management can be improved. The graphic below shows how contextual intelligence gained from machine learning streamlines operations. Source: DHL, Artificial Intelligence in Logistics, 2018 (PDF, 45 pp., no opt-in).
- Forecasting demand for new products including the causal factors that most drive new sales is an area machine learning is being applied to today with strong results. From the pragmatic approaches of asking channel partners, indirect and direct sales teams how many of a new product they will sell to using advanced statistical models, there is a wide variation in how companies forecast demand for a next-generation product. Machine learning is proving to be valuable at taking into account causal factors that influence demand yet had not been known of before.
- Companies are extending the life of key supply chain assets including machinery, engines, transportation and warehouse equipment by finding new patterns in usage data collected via IoT sensors. The manufacturing industry leads all others in the volume of data it produces on a yearly basis. Machine learning is proving to be invaluable in analyzing machine-derived data to determine which causal factors most influence machinery performance. Also, machine learning is leading to more accurate measures of Overall Equipment Effectiveness (OEE), a key metric many manufacturers and supply chain operations rely on.
- Improving supplier quality management and compliance by finding patterns in suppliers’ quality levels and creating track-and-trace data hierarchies for each supplier, unassisted. On average, a typical company relies on external suppliers for over 80% of the components that are assembled into a given product. Supplier quality, compliance and the need for track-and-trace hierarchies are essential in regulated industries including Aerospace and Defense, Food & Beverage, and Medical Products. Machine learning applications are being introduced that can independently define product hierarchies and streamline track-and-trace reporting, saving thousands of manual hours a year a typical manufacturer invests in these areas.
- Machine learning is improving production planning and factory scheduling accuracy by taking into account multiple constraints and optimizing for each. In manufacturers who rely on build-to-order and make-to-stock production workflows, machine learning is making it possible to balance the constraints of each more effectively than had been manually in the past. Manufacturers are reducing supply chain latency for components and parts used in their most heavily customized products using machine learning as a result.
- Combining machine learning with advanced analytics, IoT sensors, and real-time monitoring is providing end-to-end visibility across many supply chains for the first time. What’s needed in many supply chains today is an entirely new operating platform or architecture predicated on real-time data, enriched with patterns and insights not visible with previous analytics tools in the past. Machine learning is an essential element in future supply chain platforms that will revolutionize every aspect of supply chain management.
References
Bendoly, E. (2016). Fit, Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics.Journal Of Business Logistics,37(1), 6-17.
CB Insights, Stocked Up: 150+ Companies Attacking The Supply Chain & Logistics Space, November 30, 2016
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management.Journal Of Management Information Systems,32(4), 4-39.
Council of Supply Chain Management Professionals (CSCMP) Supply Chain Quarterly, Machine learning: A new tool for better forecasting, Joseph Shamir, Q4, 2014
Council of Supply Chain Management Professionals (CSCMP) Supply Chain Quarterly, Paving the way for AI in the warehouse, Luke Waltz Quarter 1 2018issue
DHL, Artificial Intelligence in Logistics, 2018 (PDF, 45 pp., no opt-in)
DHL, Logistics Trend Radar, 2016 (PDF, 55 pp., no opt-in)
Govindan, K., Cheng, T., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management.Transportation Research: Part E,114343-349.
Hahn, G. J., & Packowski, J. (2015). A perspective on applications of in-memory analytics in supply chain management.Decision Support Systems,7645-52.
IDC, Digital Transformation Drives Supply Chain Restructuring Imperative, July 2017
IDC, Overcoming Supply Chain Complexity with Predictive Logistics, August, 2017 (PDF, 8 pp., no opt-in)
Jayant, A. (2013). Evaluation of 3PL Service Provider in Supply Chain Management: An Analytic Network Process Approach.International Journal Of Business Insights & Transformation,6(2), 78-82.
Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management.International Journal Of Logistics Management,29(2), 676-703.
Mackelprang, A. W., Robinson, J. L., Bernardes, E., & Webb, G. S. (2014). The Relationship Between Strategic Supply Chain Integration and Performance: A Meta-Analytic Evaluation and Implications for Supply Chain Management Research.Journal Of Business Logistics,35(1), 71-96.
Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges.Production Planning & Control,28(11/12), 873-876.
Schoenherr, T., & Speier-Pero, C. (2015). Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential.Journal Of Business Logistics,36(1), 120-132.
Stanford University, Department of Management Science and Engineering, Lectures in Supply-Chain Optimization (PDF, 261 pp., no opt-in)
Tata Consulting Services, Using Machine Learning to Transform Supply ChainManagement
The Hackett Group, Analytics: Laying the Foundation for Supply Chain Digital Transformation (PDF, 10 pp., no opt-in)
Tiwari, S., Wee, H., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries.Computers & Industrial Engineering,115319-330.
World Economic Forum, Impact of the Fourth Industrial Revolution on Supply Chains (PDF, 22, no opt-in)
FAQs
How does machine learning help supply chain management? ›
- Predictive Analytics. ...
- Automated Quality Inspections For Robust Management. ...
- Real-Time Visibility To Improve Customer Experience. ...
- Streamlining Production Planning. ...
- Reduces Cost and Response Times. ...
- Warehouse Management. ...
- Reduction in Forecast Errors. ...
- Advanced Last-Mile Tracking.
Machine learning techniques can improve supply chain visibility and help businesses follow orders from shipment to delivery. Real-time data allows for a better customer experience, as you can provide more accurate delivery information.
What are the five main areas to consider in supply chain management SCM practices? ›Supply management is made up of five areas: supply planning, production planning, inventory planning, capacity planning, and distribution planning.
What is machine learning in supply chain? ›Machine learning is used to process the large volume of input data and train the ML model. As a result the ML model can predict more accurate result and train itself over the time period. Here are some interesting ML use cases in supply chain.
How can machine learning help in logistics? ›Its algorithm and the logistics companies that use it can quickly analyze large, diverse data sets, improving demand forecasting accuracy. In the collaborative supply chain and logistics segment, machine learning helps to reduce freight costs, improve supplier delivery performance, and reduce supplier risk.
How is AI used in supply chain management? ›Today's supply chain management uses AI solutions to power its inventory optimization, where the warehouse and stock managers are informed on real-time control of parts, components, and finished goods.
Which machine learning algorithm is used for inventory management? ›The methodology uses ABC analyses for determining classes and Artificial Neural Network (ANN), Bayesian network, and Support Vector Machine (SVM) algorithms to predict different classes for inventory items. [6].
How is machine learning used in procurement? ›Machine learning in procurement is the application of self-learning automated statistics to solve specific challenges or improve operational efficiency. With machine learning (ML), procurement can deliver the highest quality in relation to volume and bottom-line impact.
What are the 10 key elements of supply chain management? ›- Integration. Every business needs strategic planning for the better functioning of operations. ...
- Operations. ...
- Purchasing. ...
- Distribution. ...
- Agility. ...
- Innovation. ...
- Performance Measurement. ...
- Alignment.
The functions of a supply chain include product development, marketing, operations, distribution, finance, and customer service. Today, many supply chains are global in scale. Effective supply chain management results in lower costs and a faster production cycle.
What are the 6 types of supply chain management? ›
- Continuous Flow. This is one of the most traditional models on the list. ...
- Fast chain. The fast chain model is one of the new names in supply chain strategies. ...
- Efficient Chain. ...
- Agile. ...
- Custom-configured. ...
- Flexible.
The process of SCM involves every aspect of business operations, including logistics, purchasing and information technology. It integrates materials, finances, suppliers, manufacturing facilities, wholesalers, retailers and consumers into a seamless system.
What are the features of supply chain management? ›- Ability to integrate throughout the supply chain. ...
- Real-time and collaboration capabilities. ...
- Process optimization abilities. ...
- Analytics and forecasting. ...
- Customization. ...
- Cloud-based access and mobility. ...
- Security. ...
- Scalability.
- Better collaboration with suppliers.
- Better quality control.
- Shipping optimisation.
- Reduced inventory and overhead costs.
- Improved risk mitigation.
- Stronger cash flow.
- A more agile business.
- Better visibility and data analytics.
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.
What is an example of a supply chain? ›Examples of supply chain activities include farming, refining, design, manufacturing, packaging, and transportation.
What is machine learning? ›Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
How is logistics and supply chain field benefiting from AI? ›AI in supply chain and logistics provides real-time tracking mechanisms to gain timely insights including the optimal times by where, when, and how deliveries must and should be made.
What is logistic regression in machine learning with example? ›What is logistic regression? Logistic regression is an example of supervised learning. It is used to calculate or predict the probability of a binary (yes/no) event occurring. An example of logistic regression could be applying machine learning to determine if a person is likely to be infected with COVID-19 or not.
Why is AI important in logistics? ›Essentially, with AI in logistics and supply chain management, you'll reduce error rates, bring down operational costs and ensure you (and your customers) experience minimal stockouts.
Can artificial intelligence transform the supply chain? ›
Fortunately, artificial intelligence (AI) has proven itself to be an innovation with the potential to transform supply chains by eliminating inefficiencies and creating insights that enable more effective planning and decision-making.
Which companies use AI in supply chain management? ›- Coupa.
- Epicor.
- Echo Global Logistics.
- LivePerson.
- Infor.
- Covariant.
- Zebra Technologies.
- HAVI.
AI can analyze warehouse processes and optimize the sending, receiving, storing, picking and management of individual products. It can also analyze fleet performance and ensure the right distribution channels to get goods to retailers and other customers in good time.
How can AI and IoT be used in inventory management and forecasting give real world examples? ›AI-powered IoT devices automate the entire warehouse management process. They accumulate information from different sources and analyze the data to help improve the warehouse operations. For instance, companies install IoT sensors in their facilities to monitor the movement and use of material inside their facilities.
What is DRL algorithm? ›The DRL algorithm based on value function Q ( s , a ) needs to clarify the actions that an agent can perform, which means that the action space of the agent is discrete.
What is predictive procurement? ›In procurement specifically, predictive analytics uses data from your spend management, supplier information, and raw materials data to perform analysis and determine patterns while predicting future trends and outcomes.
What is procurement analytics? ›Procurement analytics is the process of tapping into the large reservoir of procurement data using quantitative and analytical methods to develop a set of actionable insights to. drive value and improve operations.
What is Blockchain in procurement? ›Blockchain in procurement is the secure method for tracking products or transactions from sourcing to obtaining and paying the materials. Blockchain technology allows the integration of innovations in the procurement process and supply chain management.
What are the 4 main elements on the supply chain? ›Integration, operations, purchasing and distribution are the four elements of the supply chain that work together to establish a path to competition that is both cost-effective and competitive.
What are the 3 types of supply chain strategies? ›- Strategic Planning. This level of supply chain management is responsible for developing long-term plans that outline the company's overall objectives and goals. ...
- Tactical Planning. ...
- Operational Execution.
What are the 5 biggest supply chain challenges? ›
- 1 - Huge E-commerce Growth. ...
- 2 - Sudden Shortages. ...
- 3 - Centralized Inventory. ...
- 4 - Limited or Insufficient Visibility. ...
- 5 - Patchwork Logistics.
- Purchasing. The first function of supply chain management is purchasing. ...
- Operations. ...
- Logistics. ...
- Resource Management. ...
- Information Workflow.
Generally the key aspects of Supply Chain management are Purchasing (sourcing), Planning (scheduling) and Logistics (delivery). Sometimes logistics is separate, and procurement may be included with Purchasing, depending upon how location specific the procurement activities are.
What is the basic model for supply chain management? ›The three responsive supply chain models are the agile model, the flexible model, and the custom configured model. These models are ideal for “on demand” situations. They are ideal when there is a level of uncertainty in the product manufacturing.
How does information technology it impact a supply chain? ›One way is to streamline the process of tracking and distributing inventory. But the biggest benefits of technology in supply chains Management come from reducing costs, improving customer service, and increasing operational efficiency.
What is the main objective of supply chain management? ›The basic objective of supply chain management is to ensure minimum cost and maximum efficiency in every aspect of handling of raw material, component parts and finished goods as they move from production centre to the final consumer.
What is effective supply chain management? ›Supply Chain Management (SCM) involves the flow of goods and services in an efficient manner. It encompasses all the steps involved in procuring raw materials through to the finished goods, in a way that is streamlined and provides value to the customer.
What are the 5 basic steps of supply chain management? ›The Top-level of this model has five different processes which are also known as components of Supply Chain Management – Plan, Source, Make, Deliver and Return.
How can supply chain management be improved? ›- Increase your supply chain's visibility.
- Automate where it counts — and keep all necessary parts well-managed.
- Engage your IT department.
- Assess your training programs.
- Implement a good project plan.
Overall, supply chain management encompasses the design, planning, execution, control, and monitoring of supply chain activities to ensure that goods and services are delivered to your customers in a timely manner for the lowest cost.
What are the challenges of supply chain management? ›
- Material scarcity. ...
- Increasing freight prices. ...
- Difficult demand forecasting. ...
- Port congestion. ...
- Changing consumer attitudes. ...
- Digital transformation. ...
- Restructuring. ...
- Inflation.
Today's supply chain management uses AI solutions to power its inventory optimization, where the warehouse and stock managers are informed on real-time control of parts, components, and finished goods.
How is machine learning used in procurement? ›Machine learning in procurement is the application of self-learning automated statistics to solve specific challenges or improve operational efficiency. With machine learning (ML), procurement can deliver the highest quality in relation to volume and bottom-line impact.
Why is machine learning important to supply chain management elucidate couple of use cases of machine learning in supply chain? ›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.
Which machine learning algorithm is used for inventory management? ›The methodology uses ABC analyses for determining classes and Artificial Neural Network (ANN), Bayesian network, and Support Vector Machine (SVM) algorithms to predict different classes for inventory items. [6].
Can artificial intelligence transform the supply chain? ›Fortunately, artificial intelligence (AI) has proven itself to be an innovation with the potential to transform supply chains by eliminating inefficiencies and creating insights that enable more effective planning and decision-making.
Which companies use AI in supply chain management? ›- Coupa.
- Epicor.
- Echo Global Logistics.
- LivePerson.
- Infor.
- Covariant.
- Zebra Technologies.
- HAVI.
AI can analyze warehouse processes and optimize the sending, receiving, storing, picking and management of individual products. It can also analyze fleet performance and ensure the right distribution channels to get goods to retailers and other customers in good time.
What is predictive procurement? ›In procurement specifically, predictive analytics uses data from your spend management, supplier information, and raw materials data to perform analysis and determine patterns while predicting future trends and outcomes.
What is procurement analytics? ›Procurement analytics is the process of tapping into the large reservoir of procurement data using quantitative and analytical methods to develop a set of actionable insights to. drive value and improve operations.
What is Blockchain in procurement? ›
Blockchain in procurement is the secure method for tracking products or transactions from sourcing to obtaining and paying the materials. Blockchain technology allows the integration of innovations in the procurement process and supply chain management.
What is the effect of AI Artificial Intelligence in international supply chain management? ›A study out of McKinsey calculates that AI-enabled supply-chain management has enabled adopters to improve logistics costs by 15%, inventory levels by 35%, and service levels by 65%. Again, these are early results, and AI's potential in the supply chain may be with more focused, predictive outputs.
Which type of use case is an supply chain management application an example of? ›Use Case #5: Supply Chain Management
Big data is driving many (if not all) industries today, but supply chain management really is the perfect use case for data-driven insights, predictive analytics and productivity assurance.
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
How can AI and IoT be used in inventory management and forecasting give real world examples? ›AI-powered IoT devices automate the entire warehouse management process. They accumulate information from different sources and analyze the data to help improve the warehouse operations. For instance, companies install IoT sensors in their facilities to monitor the movement and use of material inside their facilities.
What is DRL algorithm? ›The DRL algorithm based on value function Q ( s , a ) needs to clarify the actions that an agent can perform, which means that the action space of the agent is discrete.