Estimation process feedback loop
We designed and delivered a system whose one of the modules enables estimating the order and monitoring its progress.
Our client is a production company operating on individual orders (more demanding, non-mass production). They prepare monthly hundreds of valuations on which the functioning of the company depends – its profitability and the company’s attractiveness on the market.
The customer gets an order to produce a certain amount of their product. To estimate the costs, they need among others, to specify the amount of material that will be used for production and the time that is needed to complete the order. It is problematic to estimate orders that are not repeatable (non-mass production) cause each one is different from the previous one. Orders like that are difficult to implement without the appropriate data for analysis.
The biggest problem arises when, after production, it turns out that the distribution has been badly estimated and therefore priced – it’s a big loss for the manufacturer. Some orders should be estimated as unprofitable at the outset. On the other hand, providing a too-high initial price may result in the loss of the order.
The estimation of both the costs and the time remains as a time-consuming and affected by a large margin of error.
We designed and delivered a system whose one of the modules enables estimating the order and monitoring its progress. It works as a feedback loop of the estimation process.
Before we collected the production data, we had assumed 50% of the reference speed of the machine – it turned out not to be so and it allowed us to improve our estimates. The actual average speed for all orders (without setup time) is about 40% of the reference machine speed during operation. The parameter increases from month to month because we modify production processes to see how changes affect production efficiency. New parameter values are taken into account in subsequent estimates.
The current difference between the performance of individual employees is about 30% (from -20 to +10 standards). This parameter is considered for better estimation when it comes to introducing a fair motivation & reward system. It also reduces the performance gap and increases productivity.
After using preliminary estimates, it turned out that most orders are underestimated (in the smallest orders the cost of the enterprise’s work was even 250% of the estimated cost).
Our adjustment of the algorithm and input parameters led us to improve estimates and eliminate unprofitable orders.
In addition, we discovered that an increase in the material assumed by the client could be up to 40% in the event of problems with setting the machine. On this basis, we can take risks into account in our calculations, dividing them into all completed orders.
Reduction of time consumption
Shortening the time needed to prepare an average valuation.
Increasing the accuracy of calculations
Good-quality estimations based on all available information.
Increasing company profitability
Based on accurate estimates, the client can now make the right decisions and modify the strategy to increase profitability (like detecting the least profitable orders, setting an attractive market margin).
Fair incentives and reward system for employees
Thanks to a better estimation of their work time.