Optimizing the supply chain with predictive inventory management
Managing inventory can be a tricky balancing act. In some ways it’s reminiscent of “Goldilocks and the Three Bears,” always looking for the perfect amount. Warehouses without enough inventory have unsatisfied customers. On the other hand, those with too much inventory run out of storage and accrue costs.
Fortunately, machine learning and predictive analytics have made it much easier to keep track of products and predict future needs based on client behavior, helping keep warehouses properly stocked. With the help of machine learning, supply chains can be greatly improved through predictive inventory management.
Image and Pattern Recognition
Image recognition and machine vision is already fairly common in warehouses where it is used to inspect products and guide robots. In the supply chain, it has further use in recognizing products and knowing when it is time to restock.
Coca-Cola has partnered with Salesforce to use its Einstein AI image recognition platform. When shown a picture of a display cooler, Einstein AI can recognize types of Coca-Cola products and determine how many of each are in the display. The partnership was announced in 2017 and is being refined, but it could eventually eliminate the need for those delivering the soda to count inventory on their stops.
Speaking at the 2017 Dreamforce Conference, Coca-Cola CIO Barry Simpson said, “Being able to find better ways to optimize our inventory and turn that into actions for our customers as fast as we can and then optimize our sales in that outlet, that’s very important for us.”
As the image recognition of Einstein AI and other similar engines is generalized, it can be used in places much larger than a beverage display case. Before long, it could be used in warehouses to accurately count inventory.
Computer vision and image recognition are a vital part of pattern recognition which combines the visuals and underlying data to discover the most probable reason for statistics and to rule out irregularities. Software programs such as the Atonix Digital Monitoring & Diagnostics tool are being used by businesses to detect patterns and irregularities in electricity usage. Similar software could be developed to enhance inventory management by giving the tools needed to prepare for ebbs and flows in product demand.
Demand Forecasting and Predictive Replenishment
Budgeting, and marketing planning are important to any business. In the supply chain, inventory management and production, and raw material planning are also vital. Demand forecasting is the process by which supply chain managers use data to enhance all the previously mentioned aspects of their business.
In its company blog, supply chain planning software provider Arkieva defines demand forecasting as “the process by which historical sales data are used to develop an estimate of the expected forecast of customer demand. Demand forecasting provides an estimate of the goods and services that customers will purchase in the foreseeable future.”
Software such as that available from Arkieva harnesses the data analytics power and image and pattern recognition abilities to provide accurate demand forecasting which, in turn, enables predictive replenishment. Advance knowledge of when to order and restock is an important part of increasing both efficiency and profits in the supply chain.
Predictive replenishment takes advantage of the insight gained from demand forecasting and reduces the chance of items being out of stock when they are needed. Inventory management is thereby optimized by anticipating customer needs through point of sale (POS) statistics and historical data. Such a proactive, rather than reactive, method of managing inventory increases customer satisfaction while enhancing transportation and order fulfillment.
Taking Advantage of the Tech
Supply chain managers looking to streamline their business and maximize profits by employing predictive inventory management have several options. BOSS takes a closer look at a few of the most notable tools that can provide valuable insight into the supply chain.
Inveritas – The cloud-based platform from Deloitte monitors track performance against company goals and issues alerts when KPIs are off; analyzes inventory to discover why current levels exist and how to optimize them; and creates plans to enhance performance via real-time insights.
Arkieva Supply Planner – This supply chain management software features an electronic data interchange and assists in inventory management, order fulfillment, sales and operations planning, and more. Training is available courtesy of text files, live online support, and in-person support. Free trials are also available.
ForecastX – An Excel-enabled software, ForecastX is easy to use for beginners but still provides valuable insight. Supply chain managers can simply enter historical data into Excel, then generate a demand forecast in one click. The software also allows interaction and collaboration between multiple planners and stakeholders.
VoxPilot – The VoxWare product claims to provide a 10 to 15 percent increase in productivity by aggregating and analyzing supply chain data and presenting real-time data, predicting future events and needs, and developing an action plan. In addition to demand forecasts, VoxPilot provides truck loading and scheduling recommendations.
Birst Supply Chain Analytics – The software from Birst offers insight and predictive analysis globally and across the supply chain. In addition to forecasting across multiple product lines, Birst Supply Chain Analytics allows for internal and external communication and synchronized demand planning among partners.
Choosing a Platform
Ultimately, the size of a supply chain manager’s company, as well as the sector in which it operates, will determine which inventory management platform is best. Some businesses that work with a variety of clients might even choose multiple platforms. Regardless, predictive inventory management aided by AI and machine learning is quickly becoming a necessity in the supply chain.