Bakers always say baking is about precision: the mix of ingredients, the handling, the timing, and the temperature. It may take someone years to master the skill and know intuitively the optimum combination of all the variables. Imagine expanding the scale to an industrial level, producing a thousand kinds of baked goods on multiple production lines? When you are running a business of this scale, intuition is simply not enough, and the business won’t have years to get things right.
Fortunately, that’s where machine learning and optimization technologies come in handy. For businesses such as food manufacturing plants that routinely produce a high volume of food items and transport them to different locations within their supply chain, they are uncovering the power of analytics to optimize their production procedures and transportation schemes. Thanks to the volume of data that these plants generate and own, with the right optimization applications, data analysts can design, test, and optimize their analytical models to uncover efficiencies.
The food manufacturing industry is highly competitive with razor-thin margins. Retailers are constantly looking to squeeze an extra penny from food suppliers to cushion their bottom line. Consumers are also increasingly fickle with basic food items, selecting the best bargain without sacrificing on quality.
Harry-Brot Improves Industrial Baking using Advanced Optimization Technology
A German baker, Harry-Brot, has found new ways to minimize these pressures by using FICO optimization technologies. Harry-Brot produces more than 1,000 varieties of baked goods on 73 different production lines to fulfill the everyday demands of more than 9,000 stores.
Cutting corners that may hamper quality would be neither acceptable nor wise. Instead, the food manufacturer examined its existing approaches to production and transportation. It then leveraged the large amounts of data from its operations to set variables, modify constraints and explore trade-offs to optimize its strategy for baking production and distribution. These insights allowed Harry-Brot to test different scenarios to determine the most profitable options.
As a result, Harry-Brot achieved overall cost reductions of 3.57 percent and 1.81 percent for production and transportation costs, respectively. For its optimization use and results, achieved with the help of optimization firm Asolvo and FICO Xpress Optimization, Harry-Brot won the 2017 FICO Decisions Award for AI, Machine Learning and Optimization.
When it comes to baking at the industrial level, artificial intelligence is the new secret ingredient that can help food manufacturers rise above their competition.
