A small custom manufacturing company used a simple pricing model that charged a higher percentage markup on direct costs for complex and longer jobs than it did for simpler, shorter production processes. This appeared to be rational but did not fully account for all the differences in production jobs. For example, when some simpler, shorter jobs that involved high-cost materials drove up the markup in dollars, this made the margin seem too high, even at the lower percentage of direct costs. Conversely, the company also felt that some jobs were so complex and involved so many setups that even the higher percentage markups were not high enough. The company decided they needed a better pricing model.
Company leadership decided to bring in a consultant, now a NextLevel team member, who was experienced in this type of analysis to develop a new pricing model. The consultant took a more comprehensive approach to all the resources involved in the company’s manufacturing jobs and sought to devise a better way to price for resource consumption of each job. For example, he thought there must clearly be a better way to assign value to the manufacturing complexity of the work, separate from the cost of the materials.
The plant was operating at or near capacity 100 percent of the time, so there was a fixed number of hours of manufacturing equipment and labor resource available each day. Therefore, if the company recovered its costs for materials and labor and added an established contribution for each hour of plant use it would receive adequate revenue. Using basic arithmetic, the consultant derived the following pricing equation: Job Price = Material Cost + Plant Hours X Required Contribution per Plant Hour.
In the first year of implementing this model, the company realized a 50 percent increase in EBITDA on revenue that was about 10 percent higher than the previous year. The throughput of the plant was optimized for the maximum value of markup that could flow through the manufacturing process as opposed to series of jobs marked up by percentages that were not effectively linked to their complexity.