The Supply Chain Control Tower: Myth and reality
The vision of achieving 360-degree visibility in the supply chain, though promising, is hindered by the complexity and multifaceted nature of global supply chain management, resulting in incomplete data and inefficiencies despite advances in AI-based solutions.
Once upon a time, as all good stories begin, there was a vision. It called for the achievement of 360-degree visibility for product in the supply chain.
There were variations on how broad or narrow that scope should be: Should it cover just the product shipment from birth to death? Or, should it go further up the chain to the actual beginning, such as design, prototyping, order entry, production and then shipment through to delivery to the end-user?
The notion of some version of what is commonly called a Supply Chain Control Tower or Command Center dates back to sometime in the last century. Like many new concepts, there was no “ah ha” moment when it emerged from the primordial ooze into prime time.
The company i2 (remember them?) developed a conceptually brilliant software solution called “Global Logistics Manager” that was designed to keep track of both the physical flow of goods and the documents related to moving those goods.
So, what could possibly go wrong?
Well, for one, supply chain management isn’t a monolithic series of events, but rather a complicated and multi-faceted set of activities engaged in by a large number of trading partners. Depending on what source you use, Walmart, for example, lists approximately 400 million SKUs.
And while not every SKU has its own distinct supply chain, the grouping of supply chain-distinct SKUs is extremely large. This means one product with multiple colors and sizes does not necessarily mean individual supply chains to manage, but rather one for the group (e.g., Jimmy Choo patent leather pumps made in a single city in Asia would likely have a unified supply chain, regardless of the number of styles and colors).
However, this raises the conundrum of obtaining the requisite data that’s timely and accurate across the entire spectrum of players, from producers to various transportation service providers and modes, 3PLs, warehouse and cross-dock operators, along with documentation necessary for shipment and customs requirements.
Failing to achieve 100% of the requisite data leaves an incomplete view—black holes—in the supply chain visibility, with the resulting lack of trust and reliance on the system, making the it largely ineffective.
Complex global networks can have a long list of trading partners and activities for moving such products once the shipment leaves the manufacturer’s dock:
- loading in an export container;
- trucking to the port;
- loading on container ship (or cargo plane) and moved across the Pacific;
- off-loading in LA/Long Beach;
- loading on a container chassis;
- trucking to a transload facility in the LA Basin;
- transloading into a domestic container;
- trucking to the railhead;
- railing on stack train to Chicago (BNSF or UP);
- railing beyond Chicago to New York (NS or CSX);
- off-loading in North Jersey;
- loading on another container chassis;
- transporting to DC for unloading;
- staging product on DC racks;
- responding to customer order (picked, packed, and shipped); and
- transporting product to store or end-use customer.
AI-based solutions will undoubtedly help solidify a more stable and accurate supply chain from a data management, data-integrity and visibility standpoint, but it won’t be easy, nor will it be fast.
To date, Command Center/Control Tower concepts have not delivered on the promise of their vision. In fact, we’re planning a deeper dive in the near future, delving into the problems, issues and potential solutions for this critical aspect of supply chain. More to come.
