Formula manufacturers need to keep a keen eye on the batch yields they experience by formula. Aggregate batch yields easily lull you to sleep over time. A company can make or lose money quickly based on batch yields.
If a company experiences high yields for some products, it is hard to see that certain products may be achieving far less than standard. Total production averages mask these differences – total usage vs. total production. Therefore, finding the formulas with lower yields requires analysis by batch.
Over time, identifying batch yields by formula provides the following benefits:
- Efficient use of R&D: Allows an R&D department to focus on those few formulas that experience a lower than normal batch yield on a regular basis.
- Reduce Inventory: Permits the reduction in finished goods inventory by reducing the likelihood of finished good shortages because the scheduler is not being caught off guard by not achieving expected results. Many companies hold excess finished goods inventory to buffer from these variations
- Improve Costing Accuracy: Provides accurate costing data to calculate finished goods selling prices. If a formula batch yield drops from 90% to 80%, that has the effect of reducing the selling price up to 10%.
In order to address this issue, a formula manufacturer should routinely compare actual production batch yields (raw material input vs. finished goods produced) at batch levels. If the company runs a manual batch management system, they can accomplish this on a sample basis.
Newer manufacturing systems can notify specified users when any formulas experience lower than anticipated batch yields. This allows you to take immediate action if necessary.
In summary, potential profits are easily disguised by neglecting to observe yields at a formula level. By only relying on total production yield calculations, the averages lose sight of the details.
Take a look today at some of your highest moving formulas. Are the batch yields what you expect? Could the batch yields be better? The time spent may dramatically impact your bottom line.
We originally published this post in November 2011 and have updated it for freshness, accuracy, and comprehensiveness in 2016.