6 Proven Ways AI Will Improve Your OMS

delivery-ecommerce-bannerAs we progress into the next fiscal year and roll through the daily motions required to meet a new set of annual objectives, retailers like you know — better than anyone — how fleeting the status quo in the retail world can be. Fulfillment models have evolved tremendously over the past decade, yet many of the systems merchants rely on to meet new delivery expectations have not. In today’s retail environment, the point of purchase can be wherever the consumer wants to be, adding a new level of complexity to the standard, rules-based order management system (OMS) running every e-commerce transaction coming through.

That being said, to be efficient, profitable, and fast for online delivery requires an ability to adapt to consumer’s ever-changing needs in advance while leveraging your stores. This means arming systems with predictive insights to anticipate demand and understand what/when/where your next customer will buy.

So, the question stands, how exactly have retailers tackled the challenges online fulfillment presents? Here are six proven ways AI will significantly improve your existing OMS:  

1. Predict localized demand accurately

Spoiler alert: predicting demand is much more accurate when you have artificial intelligence running the equation. A large driver behind AI adoption is rooted in its ability to learn and make sense of a multitude of data, which we all know you have lots of.

The reinforcement of data drives machine learning models to improve predictions over time, allowing retailers to forecast product performance and customer demand with extreme accuracy. In the case of fulfillment, AI enables your OMS to look at a customers’ propensity to buy a particular product at a specific location – and account for this demand when fulfilling online orders.  

2. Optimize transactions in real-time

One of the biggest feats for many legacy retailers out there today? Speed.

Speed, in today's see-now-buy-now environment, relies on efficiently managing your inventory. Real-time analytics will ensure the flow of inventory is productive and agile as possible. With advanced analytics driving predictive insights into your OMS, the (milli) second someone makes a purchase online, an optimized fulfillment decision executes instantaneously – ensuring the constant movement of inventory at all times. 

3. Automate to break data silos

Automation and real-time capabilities are in many ways two sides of the same coin. As a crucial component to enabling real-time capabilities, automation is expected to boost revenue growth by 10% each year. With automated systems in place, retailers can break down existing data silos to provide a more accurate picture of potential demand. Advanced analytics platforms, like Celect, are able to ingest large amounts of data from multiple systems to provide a much more informed fulfillment decision:

“Celect connects our Order Management System, and it also receives data inputs from our Business Intelligence systems. It’s a behind the scenes technology though, which is great because it requires very little monitoring. Once the integration and the automated feeds are in place, the system optimizes on its own and only requires monitoring to “keep the lights on” as well as periodic analysis on the results and identify future opportunities.” – Marc Chretien, Sr. Director of eCommerce Operations at the ALDO Group 

4. Balance multiple, competing objectives simultaneously

It’s time to toss rules-based “optimizations” out the window.

The reoccurring problem with OMS rules-based logic is, oftentimes, one objective will contradict another. As an example, if your goal is to improve delivery speed, reduce split shipments, and decrease shipping costs — the rules set will prioritize, and often sacrifice, one goal for another. If delivery speed improves by fulfilling orders from stores closer to the customer, you may experience more split shipments and increased shipping costs (due to multiple shipments). 

Advanced analytics will enable your OMS to get as close as possible to the optimal value on each separate objective by taking into account the bigger picture at hand. It’s going to allow your fulfillment decisions to look at the long run margin gains, where perhaps fulfilling from a store close to the customer isn’t the right play.

5. Factor in potential markdowns

Did you know retailers are losing $123 billion in revenue from excess inventory each year? It wasn’t too long ago the media had a frenzy over H&M’s $4B worth of unsold inventory. While this all-too-common inventory pile-up continues to garner headlines, retailers can incorporate AI into their fulfillment decisions to be proactive and prevent overstocks from eating margins.

For example, leveraging AI for fulfillment helped Lucky Brand reduce inventory overall and “direct e-commerce fulfillment based on store inventory so that orders ship out from stores that see less foot traffic.

6. Sacrifice now for future gain – the big picture

The main point to take away is this: advanced analytics will enable your OMS to sacrifice now for future gain by taking the whole picture into account – including potential split shipments, shipping costs, and delays against margin benefits. 

It’s the bottom line that really matters. If you were at NRF earlier this month, then you already know how the industry’s biggest players are embracing tech to support their long-term vision of success. Retailers must learn to anticipate rapid changes in consumer behavior, which only continues to intensify as an increase in choice, technology, and competition persistently place pressure on the status quo. To learn more about how retailers are adding AI to their OMS, take a look at our case study with ALDO Group or calculate your own ROI below. 👇

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Topics: ship-from-store, artificial intelligence, ecommerce, order delivery, brick-and-mortar retail, technology adoption, order fulfillment, OMS, order management systems

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