We previously published a letter to client, because the letter included some good advice with broad application to supply chain managers. Here is another you might find interesting.
We frequently have the illusion of control when we have access to lots of data. However, the real user experience is that the more data there is to be consumed, the more attention is diffused—rather than being focused.
Consider the complexity of the discussion you and your team held around this screen (as an example):
[Identifying product information redacted]
In real life, after a protracted discussion over all of the detail that is available in such a system, it will not be unusual (I would guess) for five persons involved in the discussion to come away with two or three different conclusions as to the best action to take—for that one SKU! Now, multiply that by having to look at a dozen or more items that may be considered “critical” at any one meeting.
Think about this: Nothing on the screen above provides a single, clear, unambiguous metric that tells your team whether action on this SKU should be prioritized higher or lower than action on some other SKU needing attention.
Compare that to the methodology and simplicity found in demand-driven approaches:
While the above figure references the priority for a given purchase or make orders, the same exact principle applies across the entire gamut of decision-making in the demand-driven approach. There are two unambiguous signals:
- Color – Red, yellow and green; where RED is always prioritize over YELLOW, and YELLOW is always prioritize over GREEN. This color designation is determined by BUFFER PENETRATION (whether that buffer is a stock, time or capacity buffer).
- Buffer % - Percent of buffer remaining (the inverse of BUFFER PENETRATION—that is, a 97 percent buffer penetration is a 3 percent remaining buffer value); where the lower the BUFFER %, the higher the priority for action.
Remember also, that this simple, yet elegant, signal is really virtual buffer status, already taking into account factors such as
- Replenishment supply on the way (e.g., open PO lines, open Transfer lines)
- Demand spikes within the replenishment horizon
- Other factors (where applicable)
After looking at a short list of items in the RED ZONE, or low in the YELLOW ZONE, any cross-functional team of five or six will almost always depart company having a single and collaborative view of priorities and actions.
When considering a demand-driven POOGI (process of on-going improvement), think also of the relative simplicity of the following types of metrics:
- Unacceptable Service Level Performance
- Cumulative days in Critical Red Zone of the last 180 days
- Cumulative days in stock-out (Black Zone) of the last 180 days
- Cumulative days in stock-out (Black Zone) with demand (SOWD) of the last 180 days
- Unacceptable Flow Performance
- Cumulative days in Green Zone of the last 180 days (> 15 days)
- Cumulative days Over Top of Green (OTOG) of the last 180 days (<= 15 days)
- Cumulative days OTOG of the last 180 days (> 15 days)
A monthly cross-functional POOGI meeting that analyzes the (hopefully) few items that appear on lists based on these kinds of metrics can readily Pareto the determined causes and innovate to reduce occurrences in the future using the 80/20 principle.
I think, perhaps, you can see why Einstein wisely observed, “Everything should be made as simple as possible, but not simpler.” Effectiveness is found in simplicity, because it also allows managers to use their limited time to FOCUS on the truly important matters that need attention for improvement tomorrow.
This is by no means an intent to cast aspersions on [product name redacted] as a solution. Almost all of the solutions on the market today are too complex. Sage 500’s Inventory Replenishment and MRP (materials requirements planning) are too complex. Both processes provide calculations on what should be done for replenishment, but they do not supply unambiguous signals for prioritizing action nor focus management attention. SAP’s traditional methods are too complex. All of the traditional methods provide complexity because of the false assumption that more data means better management.
Also, developers get paid for creating complexity; and—for better or for worse—complexity demonstrates well as “bells and whistles.” People are impressed when they see lots of data and lots of complexity. Here again, this is largely due to the fact that people actually believe that more data leads to better management.
It is simply a false assumption. If more data led to better management, then virtually every firm in the U.S. would be managed hundreds of time more effectively today than it was being managed in 1980. But, we all know that isn’t true. The data has changed, and access to data has changed, but management methods are largely the same; management thinking is largely the same; and the attention of management is diffused while trying to digest the volume of data now available to them. They are generally unable to distinguish the relevant information from the plethora of irrelevant information.
Just some thoughts for you and your team to consider triggered by today’s conversation. I hope you find them worthwhile.
Questions? Ask us, or leave your comments below. We would surely like to hear from you.