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July 14, 2020
Inventory forecasting is a critical business requirement for any enterprise that relies on the movement of supplies to and from warehouses. Whether it’s a manufacturing operation, logistics vendor, large distributor, or a large retailer, managing optimal inventory is a critical function of efficient operations management by creating the right balance between profit and cost. While there is a multitude of advantages in properly managing and moving inventory, there are three core benefits that most businesses highlight and that inventory management with AI can boost:
One of the biggest goals for companies that maintain inventory is minimizing stock-out situations. AI and Machine Learning can be used to forecast demand, supply constraints and to manage inventories better. However, the key to a better model is not limited to just an “accurate” prediction. It’s also critical to understand how developers built that prediction. AI models are often misunderstood or untrusted because of their “black box” nature. It’s crucial to create models that provide a high degree of transparency in the features used, to give the line of business users a clear understanding of the data behind a model and what data sets it relied upon. For example, data included might be time-series data sourced from part demand history, combined with order history data from customers to understand supply and demand roles.
Another high-value use-case is lowering holding costs for inventory management. Inventory management with AI can leverage data about product movement and warehouse space and the impact on warehousing needs of seasonality. Once again, a vital requirement is to build AI models from many data sources and data tables to uncover the hidden patterns that may prove most meaningful. For example, looking at order rates is a prominent factor in managing holding costs (the fewer the orders, the less inventory is needed, the less warehouse space is necessary.) Similarly, aspects like weather patterns that impact product shipment, customer payment issues that prevent orders from being fulfilled, and other seemingly unrelated data might also impact your AI model.
A third key area where inventory management with AI can boost business performance is inventory waste and shrinkage. Once again, the benefit of a well-built AI model is predicting likely factors that contribute to waste and providing insights into unknown factors. An additional problem that is specific to inventory management with AI is that of model processing time. In most AI applications, data feeds into AI models in large batches that then provide predictions. With warehousing situations, issues like broken delivery robots, warehouse equipment that needs urgent maintenance are all issues that can contribute to waste. In these situations, having the option to deploy models in a real-time scenario can also benefit predicting when equipment issues could be at the heart of inventory waste.
One of the common threads throughout all the use cases of inventory management with AI is data. Data, of course, is at the heart of any AI or Machine Learning model. In a traditional AI/ML development process, developers collect data, prepare, and aggregate it into a large flat table – essentially a CSV file fed into various machine learning algorithms to build predictive models. Enterprises with established AI and ML development practices often employ large multi-functional teams responsible for each step of the process. One of these “experts” is typically a subject-matter expert – someone who knows the ins and outs of (in this case) inventory management who can provide insights into what factors can influence a model. Expert insight helps to narrow the list of data tables and columns to be valuable in the model.
This type of manual process, however, has two deficiencies. First, it relies upon a high amount of human interaction, which is expensive and hard to scale. More significant, however, is the built-in bias that often comes with relying exclusively on subject matter experts. AI models often rely on the same data sets, the same columns, and the same underlying assumptions because subject matter experts tend to “know” what to look for. But what if other factors influence your model that you have not considered? For example, what if fuel prices are causing increases in logistics costs and lengthening delivery times? What if factors like accounts payables and receivables are influencers? With a manual process, it’s simply impossible to account for all probable permutations.
How big of a problem are manual AI processes creating by relying only on subject matter experts? According to research by Seagate Technology, 68% of data available to enterprises is unused. Of course, not all of this data can be useful to an AI model. Still, the sheer vastness of available data makes it impractical – or often impossible – to ensure that a business will factor the “unknown unknowns” in model development. The process of building AI models has, in recent years, become increasingly automated. Data prep tools have made it easier to cleanse and prepare the data for models. AutoML platforms have automated the selection and tuning of machine learning algorithms, and real-time analytics solutions have made deploying and using models more accessible and faster. One issue that is still problematic is that of scaling the subject matter experts. Even in this area, however, technology can help.
Automated Feature Engineering (AutoFE) uses AI to discover patterns and build feature tables for machine learning. Not all AutoFE platforms, however, are alike. The latest generation of tools can traverse your entire data warehouse, exploring millions of rows of data and hundreds of tables to discover, evaluate and reveal patterns hidden within the data that might have gone unnoticed. AutoFE tools can provide a massive boost to organizations, allowing even small teams to scale their AI practice. While they are not a complete replacement for leveraging your in-house subject matter experts, they can complement their work for a powerful combination.
Inventory management with AI can provide significant benefits to businesses of all sizes. From empowering more diverse use-cases to allowing the company to leverage its real-time data to build more effective and more diverse models. At the heart of this revolution in managing inventory is tools that accelerate and automate the process of building AI models. Armed with some willpower, the right set of in-house experts, and the right automation platforms, just about any organization can benefit from managing inventories with AI.
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