Seeq and Cognizant are combining industrial analytics software with implementation and consulting services to help pharmaceutical and life sciences manufacturers improve production performance across multiple lines and sites. The collaboration initially focuses on overall equipment effectiveness, continued process verification and the analysis of manufacturing conditions, with Cognizant supporting the deployment and organisational changes needed to use the resulting information.
Seeq supplies the software used to connect and examine operational data, with Cognizant contributing consulting, implementation and change management expertise. Applying a calculation across a manufacturing network requires both elements: the underlying process information must be accessible, and each factory must understand how the resulting measure is defined and used. The arrangement initially concentrates on pharmaceutical and life sciences operations, with customer deployments and measured results still to be announced.
A pharmaceutical batch can generate temperature, pressure and agitation records in a process historian while a manufacturing execution system tracks material additions, equipment status and the sequence of manufacturing activities. Laboratory results may be held elsewhere. Investigating a deviation requires these records to be associated with the same batch and process stage, otherwise apparent differences can arise simply because measurements were taken at different points in production.
Once the records have been aligned, engineers can compare heating, mixing or transfer stages across successive batches and examine deviations against accepted operating conditions. A pressure variation during material transfer may require a different interpretation from the same reading during a controlled hold period. The analytical process therefore depends on identifying the operating event and product being made as well as retrieving the sensor value.
Production records can also be used to calculate overall equipment effectiveness, or OEE, which combines availability, operating performance and quality into an assessment of usable output. Unplanned stoppages affect availability, slower running reduces performance, and rejected material reduces quality. For comparisons across manufacturing lines to be meaningful, each plant needs consistent definitions for downtime, scheduled production and acceptable product.
Seeq’s Operational Efficiency and OEE Industry Solution is intended to bring equipment, process and scheduling information into a common analytical view. A recorded loss of production may stem from a machine fault, missing materials, upstream congestion or an activity incorrectly classified as downtime. Identifying its cause requires the analysis to connect the measured interruption with production records and the surrounding process conditions.
Shared equipment makes these distinctions more demanding in pharmaceutical plants because vessels and filling lines often move between different products. Cleaning, changeover, qualification checks and scheduled maintenance may all precede the next campaign. Recording those activities as separate operating states allows the same OEE method to distinguish a planned preparation period from an unexpected stoppage, with subsequent investigations directed towards the process actually responsible.
Continued process verification draws on related data but focuses on the consistency of the manufacturing process and its quality attributes during routine production. Statistical analysis can reveal changes in a parameter over successive batches, although the significance of a trend depends on the approved process, measurement uncertainty and the product specification. Engineers and quality personnel must establish whether the observation reflects a process change before deciding on corrective action.
Within the manufacturer’s regulated quality system, the analytical configuration and use of its results also require appropriate control. Seeq for Pharma provides functionality associated with auditability, data integrity and controlled changes relevant to requirements including US 21 CFR Part 11. A particular installation still has to be assessed and validated for its intended role in production and quality decisions, using the manufacturer’s own procedures and records.
When the same approach is introduced at another factory, Cognizant’s implementation work includes addressing differences in data systems, equipment naming and operating practices. Two plants can use different status codes for the same physical condition, or perform a comparable production stage at different points in their recipes. Reconciling those differences is necessary before a dashboard or calculation developed at one location can be relied upon at another.
A common data model can reveal an apparent relationship between a temperature excursion and a subsequent product quality result, but the observation still requires investigation against the physical process. Variations in feedstock, instrument behaviour or another operating parameter can produce the same pattern. Engineers and quality specialists must examine the surrounding evidence before changing operating conditions or documented procedures.
Analytical models also require maintenance as manufacturers replace sensors, alter recipes or modify equipment. Such changes may affect the interpretation of historical measurements or the thresholds built into a calculation. Version control, periodic review and documentation of local operating differences help preserve comparability as the methods are extended across facilities.
Seeq has previously supported pharmaceutical batch comparison, deviation investigation and process monitoring, giving the partnership established software applications on which to build. Cognizant’s role is to help implement comparable methods within customers’ operational and organisational arrangements. Although the companies identify other industries as possible areas of future cooperation, their initial stated focus remains pharmaceutical and life sciences manufacturing.
The collaboration’s performance will become measurable through customer projects that connect reliable operational data, establish usable production metrics and integrate analytical results with existing engineering and quality procedures. Consistent application across different sites will depend on maintaining those connections and definitions as equipment and products change. No jointly delivered customer installation or quantified productivity improvement has yet been reported under this arrangement.
