The term control tower has been grossly overused in the domain of supply chain management. It’s used to describe practically anything from basic visibility to network-wide fully autonomous solutions. This makes it nearly impossible for anyone looking for a capable solution to effectively compare options side by side.
This post will break down the four main types of supply chain control towers, ranging from those that offer basic visibility and analytics, to those that let you act on exceptions in real time, and even go as far as autonomous execution.
What is a supply chain control tower?
Supply chain control towers were initially envisaged as a command center, a physical site that brought together analysts and data from various systems and trading partners. They were an attempt to assemble data and intelligence from across the supply chain via point solutions, data integrations and swivel-chair processes.
As a result, the supply chain operations team gained tremendous visibility, compared to earlier methods where teams operated in near isolation; and crucial information was collected to aid in decision-making and supply chain coordination.
The modern-day equivalent of a control tower is a fully integrated supply chain management system that provides end-to-end visibility, decision-making support and fully autonomous execution capabilities. It enables all supply chain ecosystem participants to collaborate on the same data and provides views and actions on orders and shipments down to individual items.
Siloed vs. end-to-end control tower solutions
Before diving into the definitions of various control tower types, it is important to note that there are significant functional gaps among the control towers available in the market. Buyers should be aware that solutions differ in terms of whether their visibility and control span the entire supply chain or only focus on a specific function, such as Transportation Management, Demand or Supply Planning, or Warehouse Management.
As explained in Nucleus Research’s report, Supply Chain Control Tower Value Matrix:
“[W]ith siloed Control Towers, planners and transportation analysts often find themselves bogged down with swivel chair operations that necessitate emails and last-minute meetings. This methodology proves too slow to adjust to large volumes of supply chain exceptions where planners lack confidence in their plans’ efficacy from a logistics perspective, and transportation users do not have visibility of their adjustments’ impact on inventory and capacity.”
Level 1: visibility without actionability is not control
The fundamental premise of any control tower is that you have visibility into all the transactions, events and milestones you want to track. Bringing together relevant data from all parties, facilities, inventory and transportation into a single view provides visibility into all supply chain milestones and events.
However, it is critical to understand and remember that some technology vendors refer to their analytics systems as control towers, even though they lack control. While these systems do compile and present copious amounts of data from various data sources and partners, users are unable to act on what they see.
Although visibility and analytics are advantageous, a true control tower must eventually allow you to act on the data it provides.
Level 2: actionability and collaboration
To be considered a true control tower it must, at a minimum, offer users both visibility and actionability. Beyond the basic ability to recognize and analyze events, it must also allow users to implement resolutions without disconnected systems like phones or email.
To successfully address difficulties that emerge throughout the execution process, several partners are frequently required, which is why a comprehensive set of collaboration and case management tools are often required.
Traditional supply chain and transportation management systems, while capable of seeing and resolving issues, frequently fall short of the capabilities of a network-enabled control tower. Ecosystem partners must be able to collaborate in real time on time-sensitive issues based on a single version of the truth.
Level 3: decision support and scenario analysis
The ability to observe and act swiftly does not automatically result in the greatest resolution for your business. Expediting a shipment to avoid a projected out-of-stock is ineffective if the expedited products will not arrive before the next regular shipment.
With artificial intelligence (AI) and machine learning (ML) becoming more integrated into various software systems, some control towers also provide users with decision support based on trends in historical data. This enables users to simulate scenarios before implementing a solution. However, if the algorithms are limited to operating entirely on stale data or making assumptions about, say, lead times, the recommended solution is unlikely to be particularly accurate.
