Top 5 under-leveraged OMS Capabilities, and how to use them!
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Top 5 under-leveraged OMS Capabilities, and how to use them!

My twenties were a whirlwind of meetings, hotel stays, and airport dinners -- Jumping from one retail client to the next designing order management solutions that replaced decades old mainframe technology. It often felt like I was a mechanic in a garage, helping retailers build futuristic "cars" to drive, but where some of the tooling to build the car was as new as the more modern 'car' itself.

Looking back, those sessions revealed more than just choosing the right tools. They offered insights into organizational culture, decision-making processes, and the ability to adopt new functionalities quickly without stalling projects.

In reflecting on my 15 years of designing commerce solutions through various OMS platforms, such as Manhattan Associates Active Omni, IBM Sterling Commerce, Aptos Retail Enterprise Order Management, and leading the product/GTM for enVista / Körber Supply Chain , I want to highlight some key aspects of the OMS landscape that are either overlooked or misunderstood.


Inventory Alerting:

With the rise of ship-from-store and pickup-in-store options in the mid-2010s, having a centralized view of inventory in the OMS became crucial. Retailers couldn't rely on outdated mainframe solutions that updated inventory from ERP/Store systems to the website only once a day. Real-time updates throughout the day were necessary to avoid over-promising available store inventory.

However, many OMS designs failed to fully realize the value of this functionality, leading to frustrated customers when orders couldn't be fulfilled as promised. There was also a missed opportunity to indicate limited quantities available on product display pages, rather than using generic sales tactics. As the technology has advanced here it grew in adoption of the years, but I still feel it's under-utilized and brushed over during design sessions.

Inventory Protection / Watermarking:

Inventory protection involves showing only a portion or percentage of actual physical quantities available for sale on the website. Applying a blanket protection rule that restricts the availability of all items is a common mistake. This strategy might work for high-velocity, low-inventory products like cosmetics, but it's not suitable for industries like footwear, where specific sizes are essential.

Failure to understand these considerations can impact customer satisfaction and sales as the retailers either under-show the available inventory, or over-protect the inventory picture and lose sales. Finding that happy-medium in the configuration is critical, and it's in the bucket of "used, but misunderstood".

Order-Routing Algorithm (vs. IF/THEN logic):

Order routing is often underutilized, especially when it comes to leveraging algorithmic approaches instead of relying solely on IF/THEN logic. Traditionally, the logic was centered around prioritizing distribution centers and larger stores. However, as OMS solutions evolved, incorporating store labor, shipping costs, and requested delivery dates became essential.

Despite the availability of advanced functionality, many retailers still prefer static routing strategies, overlooking the dynamic nature of inventory statuses, store allocations, labor changes, and cost factors. I bucket this one as "not used, by most, but should be."

Batch Picking & Capacity Management:

A recent and relevant capability in store fulfillment is batch picking, which requires understanding available human labor capacity. Two commonly overlooked areas are store labor capacity settings and store picking frequency (batch) settings, which are the fourth and fifth capability.

Store Labor:

Setting the same labor capacity for all stores every day of the week is a critical mistake. Foot traffic and order volumes fluctuate, particularly on weekends, and adjusting labor capacity accordingly is crucial. Retailers should consider creating an "Omni Operations" team responsible for optimizing store labor capacity. Leveraging analytics to understand needed staffing levels and predicting them three weeks out (when the store schedule is set) could be a huge catalyst for a more efficient order routing decision. Often the issue is that the right-inventory at the right-store is available, but the labor at that very moment is not, thus driving up additional fulfillment cost on shipping side and potentially delaying the customer's orders. This falls into "used, but under-leveraged"

Batch Picking Frequency:

The frequency of picking batches in store fulfillment is often suboptimal. Rather than picking orders in a single batch in the morning, it's better to spread allocations across multiple stores to balance the workload. Understanding order allocation patterns, labor models, and order profiles can help optimize batch creation and pick sequence logic. Similar to the above, this is "used, but under-leveraged" and in some cases "mis-understood" because the number of batches picked during the day, the types of items in those batches and also the number of pickers per batch are often not intentionally and strategically set.

Moving towards a better understanding

In summary, there are several aspects of the OMS landscape that are either overlooked or misunderstood. Inventory alerting, inventory protection, order-routing algorithms, batch picking, and capacity management all play crucial roles in optimizing the fulfillment process. By leveraging these functionalities effectively, retailers can improve customer satisfaction, sales, and overall operational efficiency.

Did you enjoy reading this? Please like, leave a comment and reshare to your network! Thank, Zach.

Don Vangeloff

Proven leader, helping companies grow their business the right way

1y

Nice write-up Zach Z. For Retailers currently shipping from multiple nodes, Order Routing Algorithm should be a slam-dunk with solid ROI and fairly minimally-invasive deployment (to current nodes), right? So what, in your view, needs to be done so Order Routing Algo becomes "used by most" (or at least "sought after by a lot more")? Besides enterprise inertia (set and forget), what is holding back these decision-makers?

Very well-written article Zach Z. and must-know info for those decision makers and leaders of OMS.

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Ashwin Kumar🪁

Helping Shopify Merchants to Sell More📈, Spend Less💰& Run Faster⏱️

1y

Zach, each of your content are best learning for retailers, who wish to implement an OMS. Great going,👏 kudos to you!. Thanks 🙏

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Zach Z.

Transforming Commerce with Amazing & Profitable, Order Experiences

1y

Alejandro Fernández Alderete, what's SalesForce's take on the above?

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Vikas Aron

Reimagining Omnichannel Supply Chain

1y

Largely because there is no easy way to determine the right strategy at the right time, and hence most end up choosing the ‘safe’ strategy, i.e. go with the least common denominator that works for all stakeholders.

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