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How To Improve Supply Chains With Machine Learning: 10 Proven Ways

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Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionizing supply chain management include:

  1. Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems.
  2. The wide variation in data sets generated from the Internet of Things (IoT) sensors, telematics, intelligent transport systems, and traffic data have the potential to deliver the most value to improving supply chains by using machine learning.
  3. Machine learning shows the potential to reduce logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings.
  4. Reducing forecast errors up to 50% is achievable using machine learning-based techniques.
  5. DHL Research is finding that machine learning enables logistics and supply chain operations to optimize capacity utilization, improve customer experience, reduce risk, and create new business models.
  6. Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today.
  7. Reducing risk and the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point across supply chains today.
  8. Machine learning is making rapid gains in end-to-end supply chain visibility possible, providing predictive and prescriptive insights that are helping companies react faster than before.
  9. Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains.
  10. Capitalizing on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilization and reducing operating costs.

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Supply Chain Planning Systems Become Increasingly Intelligent

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Machine learning is hot. Solution providers in supply chain planning (SCP) tell me customers want to know how these technologies will be used in future SCP solutions. But machine learning is just one form of intelligence that can be embedded in SCP applications. The growing intelligence of these solutions ranges from better integration frameworks all the way up to fully automated planning.

Better Integration Frameworks

Integration frameworks allow data from multiple sources and networks to be pulled into planning solutions much more easily. Logility’s Karin Bursa, an executive vice president, points out that “many companies have multiple ERP systems.” She sees faster integration with better certainty and master data management, as a key differentiator for Logility. The master data logic understands the range of data that is appropriate for a particular field and can track and highlight when inappropriate data gets entered. Logility’s solution also uses net change logic. In other words, their system only looks at data elements that have been updated or changed. This makes same day or inter-day data updates more efficient.

Robust Role-based Views

This is not a new area of investment; it has been going on for several years. Many suppliers have invested in easier to use interfaces, particularly excel style interfaces. These interfaces have workflows that allow planners to tackle the most important planning problems in order of importance. Demand planners may want to view forecasts in units by week at ship to locations. Financial planners may want to see monthly views of revenues by business unit. Many suppliers offer integrated business planning (IBP) modules, sometimes called supply chain control towers or cockpits, that allow for a variety of views by the wide variety of actors in a corporation involved in balancing supply with demand in ways that maximize the company’s strategic objectives. Those objectives might differ by product or customer and can include things like profit maximization, achieving revenue targets, gaining market share, and other things as well.

Bigger, Better Solves

There are always new problems to solve. Omnichannel is the best current example of that. Manhattan Associate’s Scott Fenwick, director of product strategy, points out that when a new flow is supported, like order online but pick-up-in store, inventory allocation decisions need to change. But picking up that shift in the demand signal can be difficult. They are using machine learning to help solve this true demand problem.

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10 Ways Machine Learning Is Revolutionizing Supply Chain Management

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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:

  1. Machine learning algorithms and the apps running them are capable of analyzing large, diverse data sets fast, improving demand forecasting accuracy.
  2. 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.
  3. Machine Learning and its core constructs are ideally suited for providing insights into improving supply chain management performance not available from previous technologies.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Improving supplier quality management and compliance by finding patterns in suppliers’ quality levels and creating track-and-trace data hierarchies for each supplier, unassisted.
  9. Machine learning is improving production planning and factory scheduling accuracy by taking into account multiple constraints and optimizing for each.
  10. 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.

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Scientist Ng brings AI to manufacturing

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Artificial intelligence pioneer Andrew Ng launched a new AI company Landing.ai on Thursday.

On the same day, the company announced a strategic cooperation with electronics contractor Foxconn to develop a program that aims to bring AI and machine learning technologies to the manufacturing industry.

According to Ng’s statement, his company is developing a series of programs to help enterprises transform for the age of AI, including providing new technologies to optimize companies’ organizations structures, train employees, and more. The company’s businesses will start in the manufacturing industry.

Ng said the AI technology is conductive to manufacturing enterprises to improve quality testing process, shorten products’ design cycle, remove bottleneck of supply chain, reduce waste on materials and energy and raise output.

AI will revitalize manufacturing industry and generate jobs in the industry, he said. I In the age of AI, the employees need to accept new skills training to fit jobs that will be more complex than before, Ng added.

Landing.ai will provide solutions to some employees who are likely to be laid off, Ng said. Currently, the company is discussing the training plan with some potential partners including local governments.

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