End-to-end supply chain visibility (E2ESCV) seems like a great idea! Even I have been an advocate of the concept.SupplyChainDataComplexity.png

 

Clearly, there are significant advantages in E2ESCV for today’s extended and geographically-distributed supply chains. E2ESCV dramatically improves the opportunities for collaboration between the trading partners, while reducing risk and, hopefully, increasing the flow of relevant materials across the supply chain.

 

But, let’s get real—shall we?

When supply chain thought-leaders talk about E2ESCV, and then create lists (like the following) of the data that should be a part of E2ESCV, it seems like an insurmountable hurdle:

  1. STRUCTURED DATA
    1. Materials
      1. Products (finished goods)
      2. Components (raw materials and subcomponents)
    2. Forecasts
    3. Orders
      1. Purchase orders
      2. Production orders
      3. Sales orders
    4. Inventories
    5. Shipments
      1. Inbound
      2. Outbound
    6. Trade documents
    7. Assets
    8. Quality
    9. Waste
  2. UNSTRUCTURED DATA
    1. Social media data
      1. Images
      2. Video
      3. Text
      4. Other content
    2. Public data
      1. Weather
      2. Traffic
      3. Political affairs
      4. Statistical
      5. Other

It’s no wonder that most (virtually, all) of the small to mid-sized business enterprises (SMEs) with whom we work have no road map for moving to the next level.

 

A high mountain in strange territory

If our SME customers give any thought at all to E2ESCV—as a “dream,” perhaps—it probably seems to them like a high mountain in strange territory. They probably consider their likelihood of achieving E2ESCV about the same as their going to Nepal and conquering Mount Everest.

 

First of all, they do not believe it is possible to create a trust relationship strong enough with most of their trading partners—on either end of their supply chains—to enable the open sharing of that much data.DDMRP BufferStatusBoard.png

 

Second, the resources and costs that might be involved in creating (or, acquiring) the IT infrastructure to securely manage the exchange of such a huge volume of structured data are simply out-of-sight for the vast majority of SMEs.

 

Third, if they were to give to, and receive from, their supply chain trading partners such a tremendous volume of data, they are not even sure what they would, or could, do with it. They fear that it simply lead to information overload. (Most of them already have so much data available internally that they are already having trouble distinguishing what is relevant information from what is irrelevant.

 

Given just these three factors (above), the whole concept of E2ESCV seems so far out of reach that even giving much time to contemplate it seems wasteful.

 

But, we believe there is hope!

 

Inherent simplicity to the rescue

Suppose—just suppose—that supply chain trading partners could go a long, long ways toward E2ESCV by trading just a very small data set. A data set that would not, generally, be create a huge trust barrier to its exchange with supply chain trading partners.

 

Suppose that this small data set would provide all of the essentials necessary for the upstream or downstream trading partners to make decisions and set timely and accurate priorities for actions to sustain the flow of relevant materials across the supply chain.

 

We believe this is possible—especially as a first step that is easily within reach of most SME supply chain participants.

 

Four core data points

We envision the exchange of four (4) core data points between trading partners:

  1. SKU-Location (SKUL, or stock-keeping unit by location)
  2. Current Buffer Status Percent (percent of buffer remaining)
  3. Current Buffer Status Zone (a simple RED/YELLOW/GREEN color code)
  4. Current Calculated Replenishment Quantity

 

These data points would already incorporate (from the sender) the following factors:

  • Open replenishment orders
  • Open demand orders due now or past-due
  • Open demand spikes (open orders that are considered “spikes” by rule and are within the “spike horizon”)*
  • Planned Adjustment Factors (for seasonal, promotional, or other identified and anticipated changes in demand)

 

These are all relevant data points, and can be employed by the recipient trading partner (let us assume, in this example, that the recipient is the supplier of these SKULs) to calculate their own “buffer status” for the SKUs they are to supply.

 

Even if the currently calculated replenishment quantity is not yet a firm order, the supplier can use that number to determine whether or not that quantity—if confirmed as an order today—would be a “spike,” or if it can be satisfied within the normal lead-time arrangements with the customer.

 

This seems within reach

The exchange of four simple data points as the beginning of E2ESCV suddenly seems to be within reach!

 

This isn’t Mount Everest! This is the hill at the edge of town! This doesn’t require an exotic trip to Nepal! This requires knocking on a few doors to talk with the CEOs at a few key vendors and customers!

 

We believe that SMEs—like our customers—can start building on this inherently simple data framework. This framework requires a minimum of data visibility to be effective, and a trust level between trading partners that is within reach, as well.

 

 

How are you moving toward E2ESCV? Have you been stymied by what seemed like Mount Everest and a trip to Nepal? Let us know.

 

Leave your comments below, or feel free to contact us directly with your questions or comments.

 

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NOTES:

* For more information on “spike” demand and how to use the calculation effectively, please contact us.

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