The WMS Data Dilemma

Questions on Data and the Future of WMS

The 2023 Warehouse Management Software (WMS) conference circuit in the US is now a wrap. It was possessed of several key trends, all worth exploring and of value to the broader supply chain community. The most abundant refrain, and arguably the most interesting, was that of data unification across a singular and connected platform spanning the supply chain. Each of the three marquee providers, Manhattan Associates, Blue Yonder, and Korber, have their sights firmly set on one of the largest distributed data problems inside the enterprise – The disjointed supply chain software stack, and its disparate data.

Most domains across the enterprise are captured and platformed on a singular application and data model. ERP has SAP, CRM has Salesforce, and HR has Workday, to name a few. Yet the supply chain is rife with best-of-breed point solutions, along with their concomitant unique data models, purpose built for a specific specialized need. This results in a functioning supply chain at any node or within a particular discipline, but limited visibility and systemic coordination across a collection of nodes and disciplines without bespoke integration and translation built by a team comprised of cross-functional experts. This problem, while durably long-standing, gained visibility with the supply chain focus in the boardroom post pandemic. Few would disagree that it needs be solved, but at a moment of new tech-fueled transformation across Planning, Transportation Management (TMS), WMS, and other disciplines, an industry pivot to a unified and canonical model that displaces the point solution incumbents is no small task.

The ultimate goal of this platform, to be examined at length in a subsequent post, is the ability to harness the cross-functional data in a location that AI/ML can access, and most importantly interpret and rationalize, to drive transformational efficiencies. Today data is housed in separate domiciles and is not canonical in nature. A simple and easily resolved example is the disparity in how different systems classify SKU data. It is commonplace for a single enterprise to use item number in their WMS, part number in their planning software, catalog number at the point of sale, and the list continues. Rationalizing, cataloging, and storing this historically disparate data in a singular location with consistent naming and descriptors is the path to unlock much of the technological advancement available today, and that promised in the future. A noble cause, but a large problem.

Manhattan Associates

Manhattan Associates continues to build out its Active platform. The framework for this platform has been completely rebuilt from the ground up on a cloud native, SaaS architecture. It spans their suite of offerings – Planning, Order Management (OMS), TMS, WMS, and POS. Manhattan was the first to aggressively lean into this journey and as a result, is ahead of the competition. However, their new Active WMS solution is very new to market as a result of the complete rebuild. With a limited client base on the new platform, but many in the queue, it is still relatively unproven across their client base.

Korber (HighJump)

Korber opted to take a measured approach with incremental change infused into their software and platform with each new release. Their goal was to ostensibly limit the disruption to their clients with strategic and thoughtful change that never forced a complete re-write. Thus, protecting them from the harrowing and career killing full-platform upgrade event. Expect their migration to the unified platform to stretch out over the coming three to five years.

Blue Yonder

Blue Yonder, whom arguably has the most comprehensive suite of supply chain software capabilities, has embarked on a journey to re-platform their entire suite of solutions on top of Snowflake. Snowflake is a comprehensive data platform and data science company housing data across industry and enterprise with the intent to become the largest harmonized accessible data repository. The elegance of this vision is that the accessible data pool available to Blue Yonder and Blue Yonder’s clients grows exponentially as organizations migrate their data onto Snowflake. I view this as a particularly compelling vision, led by their charismatic and luminary CEO, Duncan Angove. The Snowflake journey is now underway and over the coming years their various solutions, including WMS, will make voyage onto Snowflake.

Which WMS Will Win the Future?

Does Manhattan’s first mover advantage suggest their victory is preordained? Will Blue Yonder’s Snowflake bet pay off in spades? Is Korber’s incremental approach comprised of Goldilocks levels of velocity and risk mitigation? It is far too early to tell in my opinion. Success is comprised of a broad set of ingredients that spans technology decisions, technology partner alignment, solution functionality, solution stability, ability to harness current and future transformative capability, and ultimately market acceptance. Only time will tell, but there is plenty of room in the supply chain space success across several market leading firms.