Setting the Scene

Our customer is active within the food processing industry and operates a broad diversity of activities to deliver ‘nutritional solutions’ to their customers. As such they are continuously looking for ways to improve on cost-time1-quality to ensure the best product and service to their customers without compromising on (a healthy) margin.

Situation

One of our customers wanted to see if a further optimization of their packaging lines was possible and requested us to see what could be done, using the data from the machines. A manual maintenance log in excel was kept by the operators when there were issues on the production line. These logs indicated that the machine was running quite well but if one looked at the throughput of the line this did not match.

We supported the customer in gathering and selecting the appropriate data from the machine, in this case we placed a temporary sensor, and imported this into our cloud tooling. By creating dashboards and smart aggregations of data it was immediately evident that the manual logs were capturing 10% less machine downtime then what we saw on our dashboards.

By co creating with the line manager(s), the discrepancy soon became apparent. The packaging machine’s throughput was increased but the labeler after the packaging line was not upgraded. This led to small downtimes in which new label cartridges were inserted which was not logged as downtime.

Comparing the cost of a new labeler versus the increased throughput led to a positive return on investment calculation.

Expanding the data capture towards multiple lines and integrating with work planning and order management systems allowed to use intelligent algorithms to plan the workforce and use of the lines in an efficient manner.

By combining the data from the machines, orders, returns and complaints it was possible to identify the root process parameters that impacted quality.

Our Task

Reduce the downtime on one of the food manufacturing lines, without compromising on product quality.

Activities performed by C4I

Help select the already available appropriate data from the various sources.

Identify where and how additional necessary / relevant data can be captured.

Bringing together all (newly) available and relevant data (OT data AND IT data) for the challenge. (machine data, System DB data and manually entered XLS data

Identify, implement and support a custom toolset that is fit for purpose, scalable and cost-efficient to rapidly and flexibly create asset insights to enable further optimizations

• Data ingestion
• Data preperation
• Data Aggregation
• Data Analysis
• Data visualization

Bring asset insights on a user-friendly way to enable faster ROI positive decisions

• Dashboarding (Real time asset insights, Trending, Alerting, Management Summary)

• Perform Route Cause Analysis. Enriching the results from the datasets with (human) context by performing user interviews to bring additional qualitative context to the data for thorough route cause analysis

• Build a solution that makes business sense (dream big, start small, scale smart): jointly elaborating the business case (ROI driven), build the solution for one line (pilot), expand the solution to multiple lines (scale) and realize bi-directional knowledge transfer over the entire project

• Mature the solution and the organisation to bring self-service analytics to the OT and IT departments of the customer. (stabilize)

Results

• Increased Productivity
• Reduced Downtime– more throughput without loss of quality
• Bigger capacity to handle (more) orders on the line.
• Integration realised with work planning and order management systems to enable optimized workforce planning on the selected lines.
• Overall ROI on one machine after 1,2 years (On the entire line: 0,8 year, on two lines: six months, ...)
• New insights to work on and create further value… (by adding SCADA data for example)