Theory of Constraints Handbook - James Cox Iii [684]
Figure 35-7a depicts a simple routing of tasks required to build a product in a manufacturing process in a Type 1 environment. The routing is built in isolation and is subsequently added to the master schedule, which is used for scheduling many other products. In conventional scheduling algorithms, the paradigm is one of loading the master schedule until every resource is fully utilized. However, the DBR approach schedules the constrained resource to no more than 85 to 90 percent (in simplified DBR), which provides a time buffer for protecting the constrained resource against variability. (See Chapter 9.)
The capacity constrained resource (CCR)—a resource that if not managed effectively will become the constraint—in this case X, (see Fig. 35-7b) has less capacity than the other resources in this process. This means that the resource determines how much can be produced. Therefore, this scheduling less than constraint capacity also means that all of the other resources by definition have additional sprint (protective) capacity to respond whenever variability is causing disruptions. A CCR time buffer is placed in front of the constrained resource, which means the resources in front can start to work and deliver their output to the CCR before they are needed. This tying the rope from the CCR to the gating operation allows delaying release of the work order to the floor until a buffer time ahead of when needed by the CCR. It is common for WIP to accumulate in front of the CCR so that when variability impacts a resource, the CCR will be protected from the disruption. Whenever the disruption is resolved, the resources use their sprint capacity to catch up until the flow is back to normal. By monitoring the control limits of the buffers, management knows when and where to take action before the effects of the disruption impact delivery dates.
FIGURE 35-8 Typical material flow in a manufacturing operation.
This approach significantly reduces the WIP inventory, which reduces the resource queue, a prerequisite for reducing cycle time. It follows if we reduce the cycle time without hiring additional personnel, then we increase the company’s Throughput.
Material Management and Inventory Control
TOC Replenishment8 is when stock levels are based on dynamic buffer stock levels that are much more agile and responsive to changing demand patterns than conventional min-max methodology.
The predominant conventional inventory control algorithm is based on determining the maximum amount of inventory carried for an item in the various stocking points in the company as depicted in Fig. 35-8. These stocking points can occur anywhere needed in the production flow as required to protect Throughput. This algorithm is based on determining at what quantity level you reorder (min), triggering an order to get back to the maximum inventory level. The min level is the minimum quantity level that triggers the reordering procedure to get back to the maximum level. The min level is based on the average demand during replenishment lead time and the amount of safety stock.
The TOC replenishment approach is based on dynamic stock buffers, which are based like all TOC algorithms on managing time. This causes the inventory levels to increase or decrease in real time based on the fluctuations of market demand. Now to be clear, the stock buffers are physical material for supporting the manufacturing operations or finished product in a make-to-stock environment. The greatest source of variability is due to ever-changing market requirements for the company’s products. So it may not appear that the replenishment solution is managing time; therefore, an explanation is in order. The objective is to maintain inventory levels that provide materials in a timely manner to support the manufacturing schedules. Therefore, the focus is on ensuring that as customer requirements change, the inventory levels of the needed material will be available. This change in focus, unlike the min-max methodology, allows for more frequent