In pharma, it matters not only that a product was made, but whether you can prove it at any moment. Yet in many plants the batch record still lives in binders, and the path of a drug from maker to patient is a map full of blank spots. For a production director and a supply chain director that means one thing: paper batch records and an opaque pharma supply chain are a quality, regulatory and financial risk that grows with every lot.
In our experience, the hard part is not choosing the technology but deploying it under pharma rigour, with people who understand both the software and the realities of GMP. We do not have a ready case study to show here, and we say so plainly. What we do build are teams that actually run this kind of digitisation, and our experience in industrial and Industry 4.0 projects (automation, embedded, data integration) carries straight over into drug manufacturing.
Why are paper batch records and an opaque chain a risk?
Because paper and scattered spreadsheets cannot keep up with quality and audit demands:
- batch release takes longer, because data has to be gathered, checked and signed by hand,
- errors surface too late - often only at review, not during the process,
- audits and inspections cost more when evidence must be reconstructed from many sources,
- the supply chain is blind - you cannot see in real time where a lot is or under what conditions it travelled.
This is not a “someday” problem. Every lot without a digital trail is open risk that is hard to close after the fact.
What does digitising batch records (electronic batch records) involve?
Electronic batch records turn a paper batch report into a live, controlled process. Instead of filling in a form after the fact, the operator keeps the record in a system that enforces sequence and completeness. In these solutions we focus on what truly shortens time and raises quality:
- a digital recipe and step sequence instead of loose forms,
- in-line validation - the system will not pass an unfinished or incorrect step,
- a full audit trail (who, what, when) as the foundation of compliance,
- integration with MES, LIMS and ERP, so data is not re-keyed by hand.
The outcome we aim for is review by exception: you review the deviations, not the whole stack of paper, so the batch leaves the line faster.
How should you approach serialisation and track-and-trace in pharma?
Here we are honest. Serialisation and track-and-trace is a narrowly regulated domain (the anti-counterfeiting directive, reporting to verification systems, and more), where the devil lives in the details of integration and compliance. That is why we approach it cautiously and confirm competencies individually, against a specific scope, before we declare readiness.
What we can say plainly:
- we understand the architecture of such solutions (code assignment and aggregation, communication with hubs, line integration),
- we treat it as a project at the meeting point of software, data and process, not just a label on the pack,
- we do not promise readiness we have not verified - if we lack a specific competency, we say so and bring in people who match the requirement.
This is an area where an honest conversation about scope matters more than a pretty slide.
How do you monitor the cold chain with IoT?
For a temperature-sensitive product, cold chain monitoring decides whether a lot is fit for release at all. We build the layer that turns sensor readings into decisions:
- telemetry from IoT sensors (temperature, humidity, time) across warehouse and transport,
- thresholds and alerts triggered before an excursion happens, not after,
- a record of conditions for each lot as proof the cold chain held,
- integration with transport and warehouse, so data flows from the whole route, not a single point.
With that, the quality team reacts to a deviation in time, instead of learning about it at goods receipt.
How do you build supply chain visibility and ERP integration?
Individual systems are not enough - value appears when supply chain visibility ties production, warehouse and logistics into one picture. So we join the data end to end:
- ERP integration (stock, orders, lots) as the backbone of the information,
- a shared view of lot status from raw material to shipment,
- data exchange with partners (3PL, distribution) via APIs,
- one view for planning and quality, instead of several truths in several spreadsheets.
We design it so you can add new sources without rewriting the whole thing, because a supply chain changes faster than annual plans.
Which skills do we provide for a pharma project?
We shape the team around the specific scope, not “just in case”. On production and chain digitisation projects we most often staff:
- an architect and backend developer wiring the integrations (MES/LIMS/ERP, IoT),
- a data engineer building the flow and quality of data from the line and sensors,
- an integration / API developer connecting systems and partners,
- a DevOps specialist owning deployments, security and maintenance.
Where narrowly regulated areas come in, we verify competencies against the requirement and bring in people with the right experience, rather than overstating readiness.
Where do AI and analytics fit in (optional)?
Once the production and chain data is in order, an optional AI and analytics layer opens up. Our competence in AI and data is real (across the RITS Group we develop AI/LLM/data projects), though not inside a drug plant - so we talk about competence, not a production deployment. Sensible directions include:
- quality and trend analytics on batch data,
- deviation prediction in the process and the cold chain,
- assistants for documentation and search across records (with validation rigour kept intact).
We treat this as a layer on top of sound data, not a shortcut around getting the basics right.
How fast can a team join a project?
Speed matters, but so does fit. Our recruitment usually closes in about three weeks, stretching to two months only for non-standard, narrowly regulated requirements we want to confirm properly first. As a nearshore partner from Poland we work in a close time zone, so the team joins your working rhythm, reviews and quality meetings without a long ramp-up. We most often start with team augmentation or a dedicated team, and we covered the differences between models in our article on IT engagement models.
How do we do it at RITS?
We do it as a nearshore partner from Poland, with experience in industrial and Industry 4.0 projects:
- we staff the roles needed to digitise batch records, cold chain and supply visibility,
- we join the data from MES, LIMS, ERP and IoT sensors into one maintainable picture,
- on serialisation and track-and-trace we are straight with you: it is a narrowly regulated domain, so we confirm competencies individually,
- we verify skills before the start and shape the team around the project.
We do not sell a case study we do not have. We sell the people and the approach that actually run this kind of digitisation.
Where to start with digitising pharma production and the chain?
Start from the single most painful point - usually batch release time or a blind spot in the cold chain. Name the data you must be certain of and where it is missing today. That defines the scope of the first stage, the integrations and the roles you need. The rest is best built incrementally, rather than rewriting everything at once.
Digitising batch records, the cold chain or supply visibility in pharma? Let’s talk - we’ll match a team and a scope to your quality rigour.