Enabling cost-effective reformulations for bio-based laundry detergents
Speed up the development process of high quality detergents with an affordable price.
Constant improvement and development of detergents are essential to meeting the changing needs of consumers and in keeping a competitive edge in the market. But as is the case for many home care products, the general R&D lifecycle is long and expensive.
The process requires domain expertise and a deep understanding of the product. Detergents are very complex formulations with many different components. Recipes need to perform well in different situations, e.g. on different types of stains, with different water properties, washing cycles.
Lowering the cost of active components without compromising on performance is the #1 goal of our client, a large European laundry care manufacturer. Also, continuous consumer demand to improve performance, address regulatory compliance pressure and reduce time-to-market are equally important.
Augmented Analysis from DataStories allowed the customer to use their existing product performance data to understand hidden relationships between numerous detergent components and the final performance.
Our client's value engineering team was then able to build reliable models with only a handful of driving components. As a result they could estimate the effects of the drivers on the desired properties of the final product. These insights are useful to further quickly investigate cost/quality tradeoffs to find the best formulations.
Because the predictive models quantified the effects of adding more ingredients on performance, engineers identified detergency plateaus. This discovery led to substantial opportunities to reduce the volume of active components without losing performance.
Once our client discovered cost reduction opportunities, DataStories helped identify which experiments to run exactly to further optimize the end result.
Those recommended formulations have been successfully validated by the customer.
For example, the interactive What-Ifs plot above show that if component A is cut to 90% and Component B is cut to 20%, we lose less than 3% detergency and if component A is cut to 60% and component B is cut to 20% we loose 10% detergency. For real data, the customer will be able to determine the best trade-off.
The impact we created
Lowered cost by 10% while maintaining the overall quality of the product.
We created a strong advantage in a very competitive market. Our insights and predictive models had a clear return on investment. Within a span of 30 days we created the means to continuously improve detergent compositions, both in cost reduction and in becoming more resource efficient.