Agrifood 4.0 : improve its performance through data analysis and digitalisation
Industrial performance issues: material yields, productivity and food safety
Our agrifood customers operate in a highly competitive environment with, in particular, major issues relating to material losses. Responding to this challenge requires tackling the subject from all angles: reducing losses in washing, reducing the volume of non-conforming products due to the variability of raw materials or a lack of process control, etc.
At the same time, when production lines are saturated, working on productivity becomes a priority in order to meet demand while avoiding investments that would penalise cash flow and profitability. It is also a task made indispensable by the increasingly diversified demands of customers, who require an agile production tool.
Finally, sanitary requirements demand the ability to ensure fine traceability of production, but also to be able to investigate to resolve a crisis and answer questions from the authorities, all within a very short timeframe.
Enabling and simplifying access to the information teams need
To meet these challenges, one of the challenges for the agrifood is data collection and data analysis. In many plants, data is still tracked on paper, and any traceability work requires pulling the input documents out of their archive box.
Despite the widespread automation of production lines, data rarely leaves the automatons or the supervisors. And if it does, data recovery is often laborious.
For our customers, our tools allow them to collect data directly from their industrial information systems. This eliminates the need for a lot of paper-based data entry, which corresponds to the copying of measured data online. If necessary, our online data entry tools can replace the remaining paper entries. In parallel, they also allow the collection of traceability and quality control data from their management systems (PGI, LIMS, etc.), which can thus be combined together.
In this way, our clients can:
- combine their data to transform it into relevant information (e.g. sterilisation schedule for a production batch automatically calculated from timing data and on-line temperature measurements; calculation of yields per batch, per product, etc.),
- use their data to control their process parameters with statistical control charts and thus control the quality of the finished product,
- improve their productivity by monitoring the OEE of their lines by capturing a maximum amount of information on the equipment to automatically qualify the causes of stoppages, under-cadence, etc.
- set up monitoring summaries for each production batch with all the critical parameters, and a validation process for simplified traceability. Thus, the treatment of a non-conformity or an incident is drastically accelerated by having all the necessary information on the life of the batch at hand.
From the reception of raw materials to the packaging of finished products, including the transformation processes (mixing, standardisation, heat treatment, separation, fermentation, cooking, etc.), our solutions make it possible to respond to the various challenges of the plant by simplifying the use of data.
Enable to go further by using the approaches provided by Data Analytics and Machine Learning in the plant
To go further, our clients can take the next step thanks to our solutions with an approach that combines the flexibility and agility of a field approach while capitalising on the company’s business, process and data experts:
- A field approach that provides process engineers with powerful but simple tools that enable them to optimise the parameters of their production processes to determine the ideal operating ranges according to their objectives (material yield, quality, EPC, etc.) but also to better understand the parameters that impact their processes or to identify the causes of anomalies. This “observational” approach is all the more relevant in the agrifood industry as the raw materials are highly variable and the processes may involve living matter (fermentation).
- A “data” approach that allows data scientists and data analysts to save time both in the constitution of their datasets, to make their studies and their models, but also to be able to rapidly deploy their models thanks to the Python code execution capabilities of our solutions.
An opportunity for agrifood to make its digital transformation
The food industry is a sector that is lagging behind in the field of digital transformation compared to sectors such as chemicals or metallurgy, but it is now an opportunity.
A vertical business approach, as provided by our solutions, enables the rapid implementation of a coherent, structured industrial data information system at low cost and with immediate business uses, without the complexity of the systems that existed until now. Thus, our clients not only catch up, but also bring themselves up to the state of the art to enable their teams to work more efficiently and in better conditions for the performance of the factory.
