For the last several years, we’ve all read and heard how artificial intelligence (AI) and machine learning (ML) either are or will revolutionize how we operate. A lot of the discussion is focused on theory and ideas. Let’s make this real and discuss both the why and share practical examples of how companies take advantage of these advances today.
Supply chain leaders are, more than ever, expected to make complex and time-critical decisions and extract actionable insights from huge volumes of data to enhance operations, mitigate risks and maximize opportunities. As complexity continues to increase, advances in AI and ML) are helping organizations improve the quality and speed of decisions with a higher degree of data-driven automation, enabling improved performance and business growth.
AI and machine learning are at the center of digitalization strategies, fast becoming invaluable tools for improving end-to-end multi-enterprise supply chain planning. Machine learning, a subset of AI, permits algorithms within software to identify patterns and predictions without explicit instructions, with the ability to educate itself over time based on new data and experiences to enhance its performance.
That all sounds great, right? But that’s the theory; what about how companies take advantage of this technology to optimize their supply chains today? The truth is, despite its many applications, there is still confusion in the market around the practicality and the benefits of AI and ML. So, let’s shed some light and turn what’s often clear as mud into clear as crystal. Below we explore some key areas where ML helps solve challenges for the supply chain today, followed by a few real-world examples:
Improve the Quality of the Supply Chain Model
Supply chains that traditionally rely on static data and manual tasks to correct or adjust plans find these time-consuming processes are no longer adequate to keep pace in today’s challenging marketplace. Companies are increasingly urged to respond to rapidly changing market dynamics and forces in near to actual real-time. This is where Machine Learning comes in – to ease access to real-time data and remove latency from decision making. Machine Learning works as a core element in a digital supply chain twin model, improving data quality and accuracy for enhanced planning strategies. It can help predict lead times, clean sales history to reduce stockouts, cleanse master data, and more – bringing to light key areas where companies can act to best fulfil demand.
Machine learning algorithms can help predict when an event will happen. This presents an excellent opportunity for companies to gain a more accurate view of their supply chain and what to expect, for improved forecasting and planning.
Compare Decisions and Identify the Best Course of Action
Data-driven, confident decisions are critical for effective supply chain planning, and machine learning aids in the process by harnessing data and highlighting the best course of action for a situation. The technology allows planners to perform what-if scenarios to compare courses of actions: they can foresee the eventual impact for decisions and identify the optimal outcome. As the machine learning algorithms learn and improve, the system starts to recognize which factors are most relevant and develops better plans moving forward.
Real-world Examples from Your Peers
Oil & Gas
A company specializing in bulk gas storage and distribution with daily deliveries to 200,000 customer locations, is leveraging machine learning to automate demand and replenishment planning and optimize its transportation routing. The company is using the latest technology in supply chain planning to bring in a variety of data signals including IoT, weather, POS, and customer sales history to drive the supply chain plan. IoT sensors placed on bulk storage units allow the company to measure inventory levels at different locations. The system uses automated pattern recognition algorithms driven by machine mearning to look across demand readings from the units, syndicated weather data, and usage data to predict demand. With more accurate customer orders, the oil and gas company can automatically re-plan replenishment needs and optimize truck route delivery.
Food & Beverage
Machine learning helps companies by automatically adjusting inventory positions, and that’s exactly what a global flavor and spice manufacturer is doing. The company is using multi-echelon inventory optimization (MEIO) to adjust its inventory at each stocking location and SKU level by looking at nodes across the supply chain. The supply chain system considers variability such as cycle times and transportation lead times, and it models the supply chain to recognize purchase and distribution points for raw materials, along with distribution centers for blends of raw materials incorporated into other mixes. The system also highlights transfer capability within the network to reduce costs for storage and transportation. The manufacturer can identify the optimal inventory balance at the right locations and optimize its transportation routing, which translates to better profits and service levels.
A global manufacturer of baby gear with regional warehouses in multiple countries, distributing thousands of SKUs across 80+ markets, has harmonized data across subsidiaries to improve service levels. The company is using real-time data, advanced analytics and machine learning to sense and respond faster to events, identify channel-specific growth, and analyze the differences between consumer demand and replenishing channel inventory. The role of AI to improve the company’s supply chain model has been critical to enhance master data, especially lead time. With a digital supply chain twin and machine learning analyzing data, the company is identifying relationships between different stakeholders within the network through clustering and segmentation. The cleansing of data, driven by ML algorithms helps to resolve gaps while also automating updates in data to enhance the planning drivers and improve lead time variability.
The world’s largest manufacturer and distributor of ice brings in POS, IoT and weather data to increase forecast accuracy and improve its daily transportation delivery routings. The company installed IoT sensors in its iceboxes to monitor ice levels in real-time. The company’s supply chain planning system analyzes data from the sensors, along with POS to predict when iceboxes will run out. Using machine learning, the company automates demand sensing and adjusts order recommendations. The team can identify anomalies, such as a spike (or drop) in demand and can adjust inventory to minimize delayed shipments and stockouts, and automatically re-plan replenishment related decisions every day. The system also calculates if the truck has enough ice to meet new deliveries, then routes a truck to new locations or suggests an alternate delivery route. The company can also see when more ice than expected gets delivered and can forecast potential shortages for final deliveries. This is a great example of how machine learning helps to compare and identify best course of action, as the company benefits from system recommendations, for example based on using a different truck for final deliveries and allocate the remaining ice to reduce waste, save time and money.
Embracing AI and ML to Stay Ahead of Supply Chain Complexity
It’s now more critical than ever for supply chain leaders to harness the latest tools and technology to empower them to make better and faster decisions, to support new business models and stay ahead of unforeseen challenges.
The overwhelming volume and variety of data from different sources – combined with the urge to make sense of all the information to enable business growth – has made AI and machine learning a strategic must for supply chains seeking to optimize planning efficiencies and get ahead of a rapidly evolving marketplace.
Ready to explore a single, AI-driven software solution that connects and orchestrates your entire supply chain? Schedule a demo of our powerful Atlas Planning Platform and we’ll show you how you can leverage advances in AI and ML to bring more intelligence to your decisions and elevate prediction, clustering, segmentation, and more to accelerate your strategies.