MEISSNER: CASE STUDY
- 5 hours ago
- 6 min read
FORECASTING, CAPACITY PLANNING & DATA VISUALISATION
Medical devices • Biopharma manufacturing
Meissner makes the single-use assemblies and filtration systems that biopharma companies rely on to manufacture biologics, vaccines and advanced therapies. At its Castlebar site in the west of Ireland, production planning sits at the heart of the operation: it determines how labour, components and sterilisation capacity line up against a constantly shifting order book, and whether critical products reach customers on time.
For years that planning depended on a series of spreadsheets and the expertise of the people who maintained them. Meissner has now replaced the spreadsheets with a first-of-its-kind, visual, data-driven planning platform that forecasts using estimated labour and a demonstrated performance feedback loop, predicts the cost of assemblies it has never made before, and gives planners hours back each week with numbers they can confidently stand behind. Built in close partnership with Brightbeam, it is already the engine of a company-wide global planning and efficiency programme.
Key Results:
2–4 hrs: manual prep replaced each week
100–200: planner hours recovered a year
Any part: costed on demand, even never-made parts
17 weeks: concept to go-live
The challenge
Castlebar's weekly capacity plan ran on manual worksheets that took up to four hours to prepare. As the system was built around originally estimated labour hours, without consideration of demonstrated output, it provided a non-visual overview of the week that was cumbersome and not reliable for decision-making. The plan was built around the final assembly area only, and did not factor in the departments producing sub-assemblies.
Preparing the plan meant cross-referencing several reports by hand, manually removing known incorrect data, creating tables and updating notes. A small slip, an Excel filter left on, a figure pulled from the wrong column, would often surface only five or six steps later, forcing the planner to retrace and rework. Ensuring full confidence in the weekly figures required a thorough and meticulous review process.
The knowledge to run it sat largely with a small cohort of people. A twenty-page standard operating procedure was created, but the process was simply too intricate to document cleanly, which made it difficult to train anyone else or cover absence. The team was candid that the workbook had reached its limits and was no longer a foundation worth building on.
There was a strategic risk in this too, and in a biopharma supply chain it carries real weight. Manual forecasting offers little forward view of capacity, and a labour or capacity gap that is spotted too late can force orders elsewhere, delay critical components and put pressure on a site's growth case. Meissner wanted a forecast that surfaces those pressures weeks ahead, while there is still time to act.
The costing process also operated without a feedback loop. Where an assembly required more labour on the production floor than its original estimate had assumed, there was no mechanism to communicate this back to the costing team, meaning estimates had the potential to drift progressively further from the true effort a job actually required.
The solution
Working in partnership with Brightbeam, Meissner created a visual, browser-based planning platform, by department, owned and run by its own teams, that brings five capabilities into one place. It is built on labour hours, draws on Meissner's own historical production data, and refreshes from a single automated weekly upload rather than hours of manual preparation.
Five capabilities, one prediction engine
Forward Planner. A visual labour-based forecast vs available labour, by department, of the weeks ahead, with past-due work carried forward. The output is instantly sortable to reveal which jobs will drive a busy week, so resourcing is planned in advance rather than reacted to, and specific labour-intensive jobs are flagged before they reach the floor.
Past Performance. Actual labour booked to jobs compared against predicted, by department, and against available labour capacity, exposing variances worth investigating. Hindsight becomes a continuous improvement signal.
Material Availability. A forward, colour-coded readiness view showing which jobs are ready to start, which ones are waiting on a sub-assembly to be manufactured, and which ones are missing components, so shortages are chased weeks early instead of discovered on the day of build.
Sterilisation Forecast. Projects pallet demand at the external sterilisation provider against contracted capacity, enabling proactive planning of the final step before product ships.
Predictor. Generates labour and material cost estimates for any assembly at any quantity, including assemblies the site has never made before, with a transparent breakdown by department and bill of materials.
The data behind much of this already existed in Meissner's systems. What did not exist was the engine that turns it into forward-looking predictions of time and cost: it learns from years of production records, borrows intelligently from similar parts when a part is new, strips out statistical outliers, and fits a curve to the real economics of production. The same engine drives every chart and the on-demand predictor, so the numbers are consistent everywhere and fully explainable to leadership.
All data for this project remains within Meissner's own secure Azure environment at all times, ensuring sensitive information stays under the company's control. Access is managed through single sign-on via Azure Entra ID, with role-based, site-scoped permissions that also protect the integrity of the cost calculations. The platform was built from day one for two sites, Castlebar and Camarillo, and is configurable to each site's real working calendar, from headcount and bank holidays to special job keywords.
How Brightbeam built it
As a Meissner technology partner, our way of working shaped the result as much as the technology did. Rather than start from a fixed specification, we started on the factory floor, sitting with the Castlebar planners to understand exactly how the work was really done. That discovery gave the evidence to make informed decisions about what to build, and what to leave out, before a line of code was written.
And, as per our standard method, delivery was deliberately iterative. Working software was put in front of users at alpha and beta and refined through user acceptance testing with the final stakeholders themselves, so progress was shown rather than described, and the tool improved with every sprint. Stakeholders across operations, IT and Finance stayed close throughout, shaping the solution as it took shape rather than reviewing it at the end.
Above all, the work stayed fixed on the outcome rather than the technology. When it came to the prediction model, we tested the options and chose based on the evidence: large language models were considered but proved less accurate than the purpose-built data model, so the data model was chosen because it was the best fit for the problem.
That adaptability defined the whole engagement. The team stayed focused on solving the real use cases rather than the letter of a statement of work, flexing as understanding grew rather than holding to a rigid project plan, and going beyond the original brief to deliver a product better than the one first envisioned, exactly to the agreed timeline, with no extensions and no missed deadlines. In a field where software projects so often carry risk, that reliability matters. The platform is owned and run by Meissner's own teams, built to be understood, trusted and extended in-house, and the collaboration has already sparked new ideas, extensions and further phases, the start of a long-term partnership rather than a one-off project.
The results
The wins compound across the platform. Replacing the weekly manual build with an automated upload returns two to four hours a week, an estimated 100 to 200 hours a year, to the planner role. The forecast is now labour-based, model-predicted and cleaned of outliers, so the numbers can be put confidently in front of senior leadership, and comparing predicted against actual turns every week into a signal for continuous improvement.
For the first time the team sees labour, material readiness and sterilisation pressure together, weeks ahead rather than only the current week, detailed by department where before it reflected final assembly only. Material shortages are chased early. Demand on the outsourced steriliser, the last step before product can ship, is now forecast against contracted capacity rather than discovered late. And emerging labour gaps are caught while there is still time to act, helping protect on-time delivery and the case for site growth.
The Predictor adds an edge the business did not have before: in seconds it can cost any order, show how unit cost falls with volume, sense-check existing standards, and quote work for parts never made, with full visibility of the data behind every figure. Because the platform spans Castlebar and Camarillo from the outset, both sites are viewed through the same lens, creating a foundation for decisions across the network.
Already shaping the future
This is not a tool waiting to prove itself. Meissner has launched a global planning and efficiency programme built around the platform, using it to identify inefficiencies and sharpen planning and costing across the business. Planners, supervisors and finance teams across Castlebar and Camarillo are using it day to day, and the insight it surfaces is actively shaping the future direction of the company, from a platform that went from concept to production in just 17 weeks.
Why it matters
Reliable medtech and biopharma supply chains depend on manufacturing sites that can plan with confidence. By bringing advanced data modelling into the planning room, Meissner has made an established medical device operation measurably more resilient, scalable and competitive, protecting the supply of components that life-changing therapies are built on. Delivered through the kind of collaboration that has made Ireland a global medtech hub, it is a practical model of data-driven process innovation, proven on the factory floor and already scaling internationally from an Irish base.
