TL;DR:
- Historically, omnichannel retailers struggled to optimize order fulfillment, leading to missed profit opportunities.
- Advanced software solutions from companies like Manhattan Associates and Blue Yonder are now introducing optimization capabilities into omnichannel systems.
- Constraint logic, demand forecasting, and machine learning play pivotal roles in optimizing order promising.
- These solutions focus on providing accurate promise dates to customers while maximizing profitability.
- Inaccurate inventory counts in physical stores remain a challenge, but RFID and cycle counting offer potential solutions.
- The integration of optimization and machine learning is reshaping the future of omnichannel retail, offering untapped profit potential and improved customer satisfaction.
Main AI News:
In the realm of omnichannel retail, optimization has become the new cornerstone of success. Historically, omnichannel software solutions fell short in this area, leaving retailers unable to maximize their profitability. However, this is swiftly changing.
Omnichannel, at its core, empowers consumers to access products through various means. Shoppers can make purchases in physical stores, order online for home delivery, or opt for the convenience of online ordering and in-store pickup. The array of fulfillment and product return options continues to expand, offering flexibility and convenience to customers.
For retailers, the key to meeting ever-growing customer expectations lies in the adoption of an omnichannel order management system (OOM). This system streamlines order fulfillment across all channels and fulfillment pathways, ensuring a seamless customer experience. Amy Tennent, Senior Director of Product Management for Manhattan Active Omni at Manhattan Associates, highlights that retailers are now striving to provide detailed delivery and pickup information right on the product details page of their e-commerce site, even before customers add items to their carts. This proactive approach minimizes cart abandonments and customer frustration.
Traditionally, OOMs relied on predefined rules to allocate inventory for fulfilling orders. For instance, an online order with a 24-hour delivery requirement might dictate that the goods should ship from the nearest location. These rules could be organized in hierarchical structures, prioritizing the closest store location but shifting to the e-commerce warehouse if necessary. As retailers’ operations grew, the complexity of these rules also increased.
However, the challenge with such rule-based systems is that they often leave untapped profit potential on the table. The hierarchy of rules, while functional, falls short of fully optimizing profitability. Enter a new era where software companies like Manhattan Associates and Blue Yonder are introducing optimization capabilities into their omnichannel solutions. These advanced solutions leverage network inventory visibility, demand forecasting models, and constraint logic.
Constraint logic, in particular, takes into account the physical constraints of a supply chain. For instance, the Manhattan Active Omni solution assesses whether a warehouse has the necessary labor to fulfill an order by a specific cutoff time. Similarly, if a product requires monogramming, the system calculates the extra time needed for this process, along with the specific warehouse capable of handling it.
Notably, both Manhattan Associates and Blue Yonder, pioneers in omnichannel optimization, also offer supply chain planning solutions that have long incorporated optimization algorithms.
Optimization for Order Promising
When it comes to optimizing order promising, the focus is twofold: providing customers with accurate promise dates and maximizing profitability by selecting the optimal fulfillment location. Blue Yonder’s microservice, Commits and Fulfillment Optimization, exemplifies this approach. While common sense might suggest shipping items from a more distant warehouse when local stock is insufficient, the reality can be more nuanced. By incorporating demand models and markdown optimization into the equation, retailers may choose to ship from a closer store, even if it incurs extra shipping costs, to clear out end-of-life fashion items.
Manhattan Associates employs a similar strategy by optimizing order promising through demand forecasting. However, it recognizes the importance of tailoring algorithms to suit different business models and needs. Flexibility and adaptability are crucial in the ever-evolving landscape of inventory management.
Machine Learning and Accurate Promising
Retailers walk a fine line when making promises to customers. To avoid over-promising and under-delivering, they often employ conservative parameters, considering factors such as carrier pickup times, transit times, and warehouse picking rates. While this cautious approach ensures reliability, it can also lead to unnecessary delays and cart abandonments.
To address this issue, Manhattan Associates harnesses the power of machine learning to fine-tune order promises continually. By analyzing historical data related to carrier performance, warehouse efficiency, and other variables, the system can dynamically configure accurate promise dates for earlier deliveries, striking a balance between reliability and speed.
Nonetheless, the accuracy of promises hinges on the precision of inventory counts, particularly in physical stores. Many retailers struggle with maintaining accurate in-store inventory, leading to discrepancies between what the system shows as available and the actual stock on shelves. This can result in disappointed customers when items promised as in-stock are not available.
Blue Yonder points out that even with in-store scanning to improve inventory accuracy, it still falls short of warehouse-level precision. Stores lack visibility into items in customers’ shopping carts and face higher theft rates. To mitigate this, retailers often limit the online availability of in-store inventory. Determining the correct inventory promising number remains a challenge, one that could benefit from machine learning solutions.
Manhattan Associates, however, suggests that RFID technology and cycle counting can significantly enhance inventory accuracy in stores, paralleling the improvements achieved in warehouses.
Conclusion:
Over the past two decades, the landscape of order management has evolved significantly. The integration of optimization and machine learning into omnichannel order management systems marks a transformative and promising shift for retailers. With the potential to unlock hidden profit opportunities and enhance customer satisfaction, these advancements are reshaping the future of omnichannel retail.
