In an episode of the popular British show The Office, a branch manager of a paper company walks through the warehouse with a district administrator. The administrator remarks that inventory levels are "scary" and admonishes the branch manager not to tie up any more cash purchasing stock for the warehouse.

This small scene underscores a pervasive problem in any industry selling physical products: How much inventory is too much? Too little? And how does a company pivot quickly with sudden changes in customer behavior or supply chain disruptions? With the advent of big data, inventory challenges may become a thing of the past.

Companies should leverage machine learning for smart inventory control

Machine learning uses algorithms to process extraordinary amounts of data on behalf of humans, who are great at interpreting patterns. Supply chains and shopping patterns are becoming more complex as the world becomes more connected, and machine learning expands what humans can do with supply chain data.

A data science approach to inventory forecasting and control can differentiate businesses from competitors. Companies can more accurately forecast demand to keep latent inventory low rather than making inventory purchases the way the branch manager above did––based on historical trends rather than what's happening now.

McKinsey estimates that companies can improve inventory levels by 35% and lower costs by at least 15% by implementing AI-enabled supply chain tools. In a different study focusing on Germany but with worldwide impact, McKinsey estimated that just by implementing AI-enabled supply chain management, companies could reduce inventory forecasting errors up to 50% while scaling back inventory in holding by 50%. Those are significant margins when organizations lose cash and sales by incorrectly predicting how much stock to hold.

How does machine learning affect inventory management?

Algorithms ingest big data––sales, demographics, competitor information, item promotions, and even the weather––to find patterns and trends before they happen. They can help businesses manage inventory levels, plan for inventory spikes or lulls, and predict natural events that cause disruption. Companies switching from outdated manual methods can leverage machine learning for many tasks.

Improve management of seasonal items and slow movers

Traditionally, manual processes focusing on historical data could determine likely patterns when everything went according to plan. Add in one anomalous detail like COVID-19, and these patterns become hopelessly lost. Manual processes also fail when it comes to seasonal items because their movement is harder to predict.

Machine learning uses methods like top-down forecasting to better predict inventory for products with intermittent demand. It happens automatically and reduces mistakes made by manual processes.

Plan for promotion related changes in demand

Having a sale isn't the only way promotions will affect inventory movement. Companies can use machine learning to predict:

• Post promotion dips: Once customers stock up on a sale product, demand often slips.
• Halos: Items related to sale pieces may also experience an uptick in demand. For example, a sale on mattresses may also cause sales of the company's matching sheets to rise.
• Cannibalization: Sometimes, items similar to sale items may experience a dip in demand as consumers stock up on the sale version. This also happens across brands (a sale at one handbag store may cause a drop in a competitor brand).

In each instance, a manual analysis may miss patterns causing companies to over- or under-spend for inventory outside the related promotion.

Predict by location with greater efficiency

Companies already plan inventory levels based on location, but machine learning makes accuracy in smaller areas possible. It can help plan distribution levels to each individual store within a city, for example, or make small launches possible (and profitable).

Companies can go beyond store location analysis to future decisions––where to open new locations or what new products may work in which location. Businesses can minimize costly mistakes in inventory investments and offer better value for each location.

Real-time data is essential to machine learning-enabled supply chain management

These algorithms are only as good as the data that supplies them. If companies can harness the power of big data in real-time, they'll be able to make decisions based on the most current insights.

Real-time data offers

• Supply chain visibility: Companies can monitor their inventory levels as they move in real-time, giving customers better knowledge of what products are in stock and where orders are.
• Better pricing strategies: How much to charge for inventory is a complex question. Machine learning can account for customer behavior, profit margins, and other factors to set the ideal price and change the price when necessary.
• Improved order management: Backorders can affect a company's reputation. Real-time data improves inventory levels so that companies experience fewer backorder instances.
• Loss prevention: Inventory loss and shrinkage are serious weights on profit margins. ML notices patterns that humans can miss, which helps reduce both.
• Vendor performance monitoring: The supply chain consists of many moving parts. Monitoring vendor performance helps ensure companies experience fewer surprises in the supply chain that can affect inventory.

Real-time data powering machine learning tools can reduce bottlenecks caused by any one of the above situations. It can streamline operations and turn inventory back into an asset rather than a liability.

DataOS can power your ML-enabled supply chain with real-time data

A comprehensive data operating system eliminates silos from a complex supply chain and frees up data to be in motion. A data operating system makes data visible instead of locking data away in multiple tools, storage systems, and even other locations from partner vendors.

DataOS from The Modern Data Company offers a user-friendly dashboard with comprehensive governance controls. Stakeholders have access to the data they need in real-time while protecting sensitive data and can make decisions in real-time. Your data remains yours, and there is no need to replace existing tools. Instead, weave a connective tissue through your existing ecosystem with DataOS.