According to McKinsey (2020), digitalization in manufacturing industries can increase production efficiency by up to 25%. In the industrial context, this efficiency can be translated in several ways, ranging from machine availability to worker training. Cost reduction due to the optimized use of ingredients is part of this scope of possibilities. This type of monitoring results in several improvements for the industry, and can be developed in several ways:
- Comparison between actual and theoretical consumption: by comparing the actual consumption of ingredients with the ideal consumption (what should be consumed), it is possible to identify discrepancies and possible extra expenses;
- Identification of inefficiencies: Cost analysis also indicates inefficient steps in the process. For example, some processes result in excessive leftovers or use more ingredients than necessary.
- Implementation of improvements: Based on the data collected, it is possible to implement improvements such as adjustments in the amount of ingredients used or even review storage strategies;
- Quality control: Cost and quantity analysis also helps maintain strict quality control, ensuring that ingredients are used efficiently and meet the desired standards.
Data science is an ally in this process, mainly because it allows real-time monitoring of the stages of the production process.
This case is about how a multinational manufacturing industry in the food sector used the ST-One™ Solution to monitor the use of ingredients individually in their production lines. Through the analysis of production cost, the factory discovered inconsistencies that directly affected its profitability. Learn more about us.
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