Towards the end of last year, I wrote about some of the key growth drivers for omni-channel order management systems, including changing customer expectations, the rise of B2B use cases, and the shorter lifecycle of a SKU. As I’ve continued to have conversations with OMS suppliers, I wanted to revisit the growth drivers and offer a few more perspectives. As I wrote earlier, the order management system is the backbone for omni-channel operations. The reason for this is that the OMS allows an organization to capture all information in the order management process across all relevant channels. This includes the entry of the order, sourcing, payments, and fulfillment. It also spans all channels of sales operations.
While adoption rates, according to my latest research, have remained somewhat flat, a lot of that is due to the integration issues that often arise, which can make companies hesitant to invest big money in the solution. However, as the use cases for omni-channel success continue to mount, the future looks bright for OMS. According to my latest survey, 58 percent of respondents are currently using a distributed order management system. However, another 28 percent plan to implement an OMS in the next 12 to 18 months.
One of the key growth drivers for OMS is cloud-offerings. More often than not, when people think cloud, they simply think of a cheaper alternative to an on-premise version, with the main benefit being less IT staff required to maintain the system. While this may be true, the benefit truly lies in the ability to update quickly. I spoke with Adam Davies, Senior Product Manager at SAP Hybris. According to Adam, “Cloud is making implementations easier, as updates can be automated and the customer can transition to the new version a lot quicker and easier. The initial resistance came from the omni-channel shift – inventory and location information into the cloud was seen as too much data to not have “on-hand.” But, customers have seen the benefits and this is no longer an obstacle.”
As Adam mentioned, with a cloud application, there is no longer a need to run manual updates on the OMS, as it is all pushed from cloud to the application. This basically allows customers to turn on new features and functions as they see it necessary without having to spend time refactoring legacy code for minor improvements.
Ship from Store
Another key growth driver for OMS adoption is the rise of ship from store. According to my latest research, 65 percent of respondents are picking items and shipping them from the store. According to Jennifer Sherman, Senior Vice President, Product & Strategy at Kibo Commerce, “the rise of ship from store options has made an omni-channel OMS more important. The size or format of the store drives a different user experience. For example, fulfillment in a mall boutique looks different from fulfillment in a department store.”
In the past, the store has been seen as the weak link in omni-channel operations, especially on the fulfillment side. However, retailers have been pushing to make the store more efficient. A lot of planning goes into ship from store; this includes a rules-based approach to deciding which orders should be shipped from a store rather than a DC. According to my research, the number one criteria used to determine when to fulfill e-commerce orders from the store is distance to the customer delivery location (55 percent of respondents), followed by inventory constraints at the DC (50 percent of respondents.) This is in line with what Chris Shaw, Director of Product Marketing at Manhattan Associates is seeing as well. According to Shaw, “in-store fulfillment is becoming less about just overstocks and markdowns for the walk-in customer; and becoming more about finding the most profitable sourcing option possible to deliver on our promises for every customer.”
A third growth driver that came up in conversations with suppliers was around the use of artificial intelligence and network optimization. The branch of AI known as machine learning is getting hot in the supply chain world. Suppliers are using machines to analyze an output, update their models based on the accuracy of these outputs, and then creating a better model. Optimization is a critical piece of the machine learning aspect within OMS. According to Chris Shaw, “The need for inventory sourcing optimization is pushing the OMS market. We must rely on machines today to understand everything that is key to making decisions – supply, price, historical success of location on fulfillment, labor within the store, and costs. Ideally, you can run simultaneous evaluations, which analyzes every factor that plays into the decision concurrently. Then your OMS will make the most cost-efficient decisions automatically.”
Retailers are using machines to learn more about every aspect of the order management process. This in turn is helping to make better informed decisions on how to fulfill an order that will not only satisfy the customer, but also be done in the most cost-efficient manner. Adam Davies echoed that thought. “A big trend is opportunities that come from artificial intelligence. Today you try to optimize where to fulfill an order from using simple algorithms, but with machine learning, the optimization becomes quite complex and moves beyond the fulfillment side and into the sales cycle. For example, does shipping faster to a certain area make people in that region order more?”
The use of omni-channel order management systems is a critical component of an omni-channel strategy. The rise of cloud offerings, machine learning, and the store’s role in the overall commerce landscape are major growth drivers for this technology. Retailers are poised to continue to utilize OMS in order to meet the changing nature of their customer’s expectations while ensuring the most cost-effective and timely fulfillment options for the bottom line.