Who could possibly have known, back in those carefree days of early January 2020, that the best-selling items of the coming months — the products to really fly off the shelves around the world — would be face masks, hand sanitizer, and toilet paper? But then, who could have known at the start of 1996 that Gwen Stefani’s look in the No Doubt video would make bindis a must-have fashion accessory for teenage girls who had never been near India? Or that undershirt sales in the U.S. would plummet in 1934 after Clark Gable ditched his in the Hollywood classic It Happened One Night?

Trends like these can happen abruptly, triggered both by major events and seemingly innocuous ones. For retailers, and for procurement and inventory managers, in particular, this can be nothing short of catastrophic. If the backend of your retail operation fails to keep up with changes in the market, you’ll end up with tons of useless inventory or shortages that cede profitable opportunities to your rivals. 

In such a competitive market, even a slight miscalculation can lead you to overpay, overstock, or mistime your orders. If that happens, even a great sales streak may struggle to dig you out of the hole into which you’ve dug yourself. 

Challenges in inventory management

As we’ve seen, retail markets can be volatile. Customers can be fickle. Supply chains are fraught with risks that can blindside you with serious delays and shortfalls if you aren’t vigilant. On one hand, you need to track shopper preferences, previous sales figures, historic trends, and even what people are saying online. On the other, you need to constantly order products ahead of time, taking a chance that you’re making the right call. If you get it wrong, you risk being stuck with inventory you can’t move, wasting money on parts, products, transport, and warehousing. 

This makes trying to build inventory models manually incredibly difficult. You need to stay on top of myriad, often conflicting factors, figuring out how to interpret them and what to do with this information. Common pitfalls retailers face in their standard inventory management include:

• You only have internal sources to work with
• It takes too long to build a system
• It uses the wrong data
• It’s not always accurate 
• It’s prone to bias

Let’s take a look at these problems in more detail — and how machine learning in retail helps to tackle them.

Using machine learning in retail 

Machine learning models streamline the analysis part of inventory management, making this faster and more precise. This gives you far more exact, actionable insights than you could hope to get from a manual system, and means you get these insights in a short enough time frame that they’re still valuable. In turn, by putting these insights into practice, you keep costs under control and make your inventory management more efficient. 

Some of the major benefits of using machine learning in retail include:

• You capture a wider array of data

Historical sales figures and patterns only tell you so much. You may think you know how well a product will sell next quarter, but throw in a usually cold summer, a celebrity wearing a bold new style, or even a global pandemic, and suddenly all bets are off. Unless, of course, you have access to exactly the right sources of data to answer these unexpected questions or alert you to massive disruptions in the market. 

When you’re running a machine learning project that combines external data sources, you aren’t restricted to the information you have in-house. You might look at more nuanced purchasing patterns, based on factors like weather conditions, region, and location. You might decide to contextualize demand by looking at annual earnings or other demographic data. You could analyze mentions of products and styles on social media. Or look at vendor purchasing habits based on major internal or external events. 

If you’re using a platform that automates seamless connections to pre-vetted external sources, you can pivot to the most relevant information in no time at all. Using augmented data discovery tools, you can automate your search for the most up-to-date data sources to fill in the gaps, ensuring your models always deliver relevant, accurate insights. 

• You understand your data better

Chances are, your marketing team collects masses of data from a broad range of sources, including site data and social media. But are you using this data to its full potential, or are you drowning in it? 

A carefully conceived machine learning project will help you organize that data, make astute connections, reveal important patterns in customer behavior, and predict how the market — or an individual buyer — is likely to behave next. All of which translates into more confident forecasting and a better-managed inventory.

• You get results faster

Using the right tools for machine learning in retail means you can automate a lot of the heavy lifting, particularly in the data preparation stage. This means you get your models into production faster and can incorporate new external data sources with fewer delays. All of which mean you get the answers you need to manage your inventory now… not in a month when the industry has already moved on. 

• You reduce bias

If you create your forecasts manually, you are likely focusing on average values and point predictions, which don’t take uncertainty into account. It’s also likely that the system you use has a tendency to slightly over or under-forecast, which isn’t particularly helpful, especially if your team is aware of this and starts overcompensating in the opposite direction. 

On the other hand, when you’re constantly feeding new information from different sources into your models, you move away from over-reliance on any one system. This means the model is always adapting to new information, correcting any systemic biases that can arise when you focus too much on one dataset or line of inquiry.  

• You lower your inventory and ordering costs

Fluctuations in the market and customer preferences can create serious — and expensive — headaches for procurement. By incorporating dynamic data streams that reflect these real-time changes into your machine learning models, you can respond much faster, ensuring you’re only ordering what you need when you need it. For example, by developing a forecasting model that was driven by machine learning and incorporated a broad range of external data sources, one of our clients was able to shift over to just-in-time shipping and reduced inventory costs by 17%.  

Final thoughts: machine learning to move with the times

Machine learning in retail gives you the ability to track moods and trends and respond to them at lightning speed, translating these insights into inventory management that cuts cost while maximizing sales opportunities. The potential of machine learning to make you more efficient and competitive is enormous, but you do also need to make sure you’re using the right technology to make it happen. 

Whatever systems, platforms, and tools you use for your data science projects, make sure that you can connect to the data sources you need, when you need them, without delay. That applies to external data as well as internal. You never know where the next disruption will come from. The key is to create machine learning models that can adapt to new, up-to-date data sources so that you never miss a beat.