Canah chooses Bento for food production process optimization
Canah chooses Bento for food production process optimization
Project Description
This project concerned the development of a customized software solution to integrate the tools specific to the food production process and to automate specific flows. The solution provides specific post-calculation and pre-calculation analyses, as well as “What if” analyses for cost optimisation, through a series of scenarios taking into consideration internal or external factors. Expanding the solution to a mobile application allows management to view all performance indicators in real time and to reduce response time in the decision-making process.
The Challenge
Canah is the largest hemp processor in Romania, with a turnover of almost EUR 6 million in 2016 and 35 employees. The company sells hemp-based products in Romania and in the European Union. Accelerated business growth led to the need to automate internal processes, to manage company resources more efficiently, and to integrate all specific instruments (scales, packaging and labelling systems) into an integrated IT solution that simplifies the production process. The management decided to select a provider to enable automatic data collection and real-time data operation by connecting to production equipment.
The Solution
To carry out this project, Canah chose Bento – a company specializing in turnkey solutions for the food production industry. Following the analysis of the client’s needs and workflows, Bento and the client’s team agreed on performance indicators and delivery timeframes. The proposed solution integrated the equipment used by Canah and included extensions for mobile devices, to enable the management to access relevant indicators in real time.
The Results
Currently, Canah has all required information concerning the amount of raw materials and semi-finished products used, the number of finished products, working time, wait time, failure time or inventory status. All this information is available without the help of any human operator, significantly reducing production tracking phases and eliminating human errors. The solution allows adding any type of sensors required to optimize the quality of the products – such as humidity, temperature, or automatic analysis of deviation from quality standards.
