In manufacturing systems, direct labor cost may constitute less than 10% of total production cost. Manufacturers often incur high overhead costs, accounting for around 30% of total production costs. QRM helps minimize overhead costs and compensate for the negative aspects of tariffs.
There are four Stages of QRM Implementation:
- Digital twin with uncertainty modeling for the QRM feasibility analysis.
- Reducing setup times and batch sizes.
- Developing a capacity management strategy.
- Implementation with “POLCA—Paired-Cell Overlapping Loops of cards with Authorization.”
Improving the responsiveness of supply chains through QRM requires a systematic approach. First, manufacturers should determine the right level of responsiveness so that they do not overinvest and waste their limited cash resources. If a manufacturer has certain market power such that their customers are willing to wait for two weeks to receive their orders, for example, it will not be feasible to overinvest and cut the delivery window to one week. Digital twin would be immensely helpful in analyzing different production models and identifying the right level of responsiveness. Here, an ideal analytical application must rely on uncertainty modeling to avoid costly mistakes. Most analytical applications convert transactional data to aggregate information and use the aggregate information in supply chain models. Although such a conversion simplifies the model development, it causes a loss of information and suboptimal decisions. Uncertainty modeling corrects this poor practice by employing the fast Fourier transformation.
Second, manufacturers must reduce setup times and batch sizes simultaneously. When setup times are long, switching production from one product to another one causes inefficiency problems, leading to low utilization of resources. This forces decision makers to produce each product in large volumes and limit the number of product switches. Then, long production cycles lead to excessive inventory along supply chains. To avoid such circumstances, manufacturers must invest in reducing setup times. Toward this end, they may install quick changeover systems. After reducing setup times, manufacturers can reduce batch sizes and have better control over inventory accumulating along the supply chain. The scenario analysis in the first stage helps manufacturers determine how much to reduce the setup time and batch sizes without overinvesting in the responsiveness.
Third, manufacturers must develop a capacity-rationing policy such that limited resources are allocated to various products according to the developed policy. If a shared resource is used to produce both make-to-stock and make-to-order items, the resource must first be utilized to produce the make-to-order items. After the production of make-to-order items is completed, it can be used to produce make-to-stock items. This method is referred to as the “spackling” method. If a shared resource is used to produce only make-to-stock items, those with low demand uncertainty must be produced during the off-season period. Here, the precious, peak-season capacity must be reserved for the items with high demand uncertainty. This method is known as the "accurate-response" system. Finally, if a shared resource is used only for make-to-order products, those with high profit margins must be prioritized over the low-margin ones. In sum, precious capacity of limited resources (during peak periods) must be utilized in the following order:
- Make-to-order products from high margin to low margin,
- Make-to-stock products from high uncertainty to low uncertainty.
Fourth, the production activities must be maintained according to the "POLCA" method, which was developed by Rajan Suri. The POLCA method is an alternative to the Kanban system of lean manufacturing, addressing some shortcomings of the latter. In the Kanban system, Kanban cards are detached from workstations as products move forward along the supply chain. The detached cards automatically initiate production orders to replenish the inventory. This approach causes some problems when manufacturers have diverse products. The POLCA method has an additional authorization step such that production orders cannot be placed independently from the capacity management policy. Thus, it ensures a smooth control and implementation of QRM.
Once manufacturers complete these steps, they can benefit from QRM and reduce production costs, thereby compensating for the negative aspects of tariffs.
Author: Isik Bicer, PhD