Review of A Theory of Constraints Replenishment Solution, Part 3
I began a discussion on the Theory of Constraints (TOC) distribution and replenishment model and explained how it works in Part 3 of this series. In that post, I demonstrated how buffers are positioned at points of potential high demand variation and stocked and restocked at levels determined by stock on hand, demand rate, and vendor replenishment lead time. Order frequency is increased and order quantity is decreased to maintain buffers at optimum levels so as to avoid stock-out conditions. Stock-outs cause interruptions to the flow of parts.
The figure below depicts the TOC-based model.
In the final installment of this series, I will complete our discussion on the TOC distribution and replenishment model and present results that you can expect from using this superior method.
Aggregation smooths demand in the TOC system
The TOC distribution and replenishment system relies on aggregation to smooth demand. Demand at regional warehouses is much smoother or consistent than demand at retail locations. This is because higher demand at some retail locations is offset by lower demand at other locations. Demand at the factory warehouse is even smoother than demand at both the regional warehouses and retail locations. Goods produced by the factory are stored in a nearby warehouse until they are needed to replenish goods consumed by sales. The factory bases its production runs on depletion of warehouse buffers.
Since sales of SKUs occur daily, shipments also occur daily and the quantities shipped are just sufficient to replace goods that have been sold. Your first inclination might be to think that this process increases shipping costs over what could be achieved by shipping large batches less frequently. In actuality, the net effect on total shipping costs is a significant decrease in shipping costs. This is because the system significantly reduces shipments of things like obsolete goods and re-shipments of misallocated goods. Increased shipping frequency more than compensates for increased costs created by smaller shipments of saleable goods.
When you think about it, the ability to capture sales that would otherwise be lost due to insufficient inventory makes the TOC solution a much better alternative. In this system, replenishment is driven by actual consumption, not a sales forecast.
Here is how the process works in sequence:
- As sales are made, the buffer levels at retail locations drop.
- Dropping buffer levels trigger orders from regional warehouses.
- Regional warehouse orders trigger replenishment from the factory warehouse.
- Factory warehouse replenishment ultimately triggers a manufacturing order to resupply the appropriate buffer before it runs out.
Buffer sizing is based on two basic factors: variability and the time it takes a vendor to replenish a SKU. For example, the more variable the consumption, the larger the buffer must be to cover the variability. Correspondingly, the longer it takes to resupply the SKU, the larger the buffer needs to be in order to cover the demand during the resupply waiting times.
Expected results from switching to the TOC replenishment solution
The benefits of the TOC’s replenishment solution can be very striking. For example, a company switching from the min/max system that is currently 75- to 85-percent reliable can reasonably expect to increase its reliability to 99 percent or better while cutting inventory by at least half. In addition, the average time to resupply retail locations characteristically drops from weeks or months to one or two days.
One of the central benefits of the TOC’s replenishment solution is that it transforms distribution from a push system to a pull system. That is, nothing gets distributed unless there are sales. Market pull is the external constraint which optimizes distribution while minimizing inventory.
The min/max system is a model of inefficiency:
The TOC model stabilizes inventory and completely avoids stock-outs:
Inventory levels decrease significantly while the incidence of part stock-outs drops to virtually zero! Now which condition would you like?
Coming in the next post
In my next post, I will begin a new discussion on a different improvement subject.
Until next time,
Epiphanized—Integrating Theory of Constraints, Lean and Six Sigma (TLS), Bob Sproull and Bruce Nelson, North River Press, 2012
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