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

Posted on by Teddy Nee

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:

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.

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.

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.

Reducing forecast errors up to 50% is achievable using machine learning-based techniques.

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.

Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today.

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.

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.

Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains.

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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10 Ways Machine Learning Is Revolutionizing Supply Chain Management

Posted on by Teddy Nee

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.

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.

Machine Learning and its core constructs are ideally suited for providing insights into improving supply chain management performance not available from previous technologies.

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.

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.

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.

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.

Improving supplier quality management and compliance by finding patterns in suppliers’ quality levels and creating track-and-trace data hierarchies for each supplier, unassisted.

Machine learning is improving production planning and factory scheduling accuracy by taking into account multiple constraints and optimizing for each.

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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How can Lean Six Sigma help Machine Learning?

Posted on by Teddy Nee

Note that this article was submitted and accepted by KDnuggest, the most popular blog site about machine learning and knowledge discovery.

I have been using Lean Six Sigma (LSS) to improve business processes for the past 10+ year and am very satisfied with its benefits. Recently, I’ve been working with a consulting firm and a software vendor to implement a machine learning (ML) model to predict remaining useful life (RUL) of service parts. The result which I feel most frustrated is the low accuracy of the resulting model. As shown below, if people measure the deviation as the absolute difference between the actual part life and the predicted one, the resulting model has 127, 60, and 36 days of average deviation for the selected 3 parts. I could not understand why the deviations are so large with machine learning.

After working with the consultants and data scientists, it appears that they can improve the deviation only by 10%. This puzzles me a lot. I thought machine learning is a great new tool to make forecast simple and quick, but I did not expect it could have such large deviation. To me, such deviation, even after the 10% improvement, still renders the forecast useless to the business owners.

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2015 New Year’s Resolutions for the Supply Chain Industry

Posted on by Teddy Nee

2015 New Year’s Resolutions for the Supply Chain Industry

Resolution #1 – Stop using the term VISIBILITY

People say that information is power. I beg to differ. I say, an informed decision is power. The visibility term has been over used. I’ve even heard some say that getting visibility to your supply chain is 80% of the challenge. They must not have run a supply chain. I see many supply chain leaders that have visibility, some in excel and some in automated tools. The ones that don’t have visibility can easily call the supplier and get it. Getting visibility isn’t the challenge. The real 80% challenge is “what are you doing with the visibility?”

Resolution #2 – Read only ONE “Cool Theme” report

In 2015, I resolve to read only one Cool Theme report. I’m tired of research analysts peddling these themes as a means to gain an edge on readership. Yet, I watch the audience during some of these Cool Theme presentations. And, half the people are on their smartphone working core issues back home, while the Analyst is talking about how supply chains should save the Panamanian golden frog, reduce the ozone layer, produce products with plastic wire from 3D printers and generate forecasts from Facebook posts!

Resolution #3 – Stop moaning about Bad Data

Let’s face it, everyone has some form of bad data. And, when you include all your tiered suppliers, they have bad data. The one constant is that you will never fix all the internal and external bad data. Yet, I still hear supply chain leaders say they need to focus first on fixing the data. I’ve seen many presentations from “Top 25” supply chains and how they’ve cleaned data, and why they should be considered a top tier supply chain story.

Resolution #4 – Fix the Disruption you can influence, not the Disruption you are concerned with

There are two types of disruptions. That which you are concerned with, and that which you can influence.

Volatility, regulation, geopolitics, economics, energy, and the list goes on. These are in your Circle of Concern. They happen, and you should be concerned. Yet, many supply chain leaders face fail to focus on the Circle of Influence, the area where you can make a difference.

Resolution #5 – Scrap the Talent Research, Make Planners more Productive

After reading all the Talent Research done in 2014, the topics of attrition, retiring professionals, and university-business alignment, I notice a big gap. The one thing missing in all this Supply Chain Talent research is the concept of being more productive with the talent you already have.

How can every supply chain improve productivity? In every supply chain I’ve seen in my past 25 years, there’s one constant – they all use some form of Excel – mostly to search for exceptions. Planners spend half their day dumping ERP and BI data into Excel, and then search for exceptions.