Q&A Case studyQuality 4.0
A Quality 4.0 Analytics Layer Above Legacy LIMS and MES
Dr. Reddy's knitted six site-level LIMS instances and two MES vintages into a single quality analytics layer that surfaces process drift before it becomes a deviation.
Fewer trend-triggered investigations year on year
47%
The practitioner
Priya Nair
Chief Quality Officer, Dr. Reddy's Laboratories
Interview by Mrudula Kulkarni
Managing Editor - Pharma Now
15 Jul 2026 · 8 min read
The challenge
Across six manufacturing sites, quality signals lived in siloed LIMS instances and two generations of MES.
The approach
Rather than a rip-and-replace, the team built an analytics layer that read from the source systems in near real time and normalised results…
The result
The layer went live at all six sites within nine months.
The challenge
Across six manufacturing sites, quality signals lived in siloed LIMS instances and two generations of MES. Trend analysis was largely manual and reactive, and the corporate quality council was consistently seeing signals in the monthly review that a shift-level operator could have caught days earlier.
The approach
Rather than a rip-and-replace, the team built an analytics layer that read from the source systems in near real time and normalised results into a common process performance model. Statistical process control charts were wired to shop-floor dashboards, and a lightweight signal-triage workflow was co-designed with QA leads at each site.
The result
The layer went live at all six sites within nine months. Deviations trending toward investigation dropped notably, average time-to-signal shortened from days to hours, and the corporate council refocused monthly reviews on systemic issues rather than site-level firefighting.
Q
Why not just consolidate the LIMS instances?
We priced that out first. Consolidating six LIMS to one would have been a three-year programme with a nine-figure budget, and would have frozen the release process during migration.
The analytics layer gave us most of the value — cross-site visibility, faster triage, systemic trending — without touching the source systems. Consolidation can still happen later; we just moved the value forward.
Q
How did you standardise data across two MES generations?
We built a canonical process performance model that lived above both MES vintages. Each source system had a thin adapter that mapped its native tags to the canonical model.
When we onboarded a new site, the adapter was the only thing we wrote — the analytics layer above stayed unchanged.
Q
How did the site QA leads react to shop-floor SPC charts?
Cautiously at first. There was a real fear that signals on a dashboard would lead to over-reaction and unnecessary investigations.
We co-designed a lightweight signal-triage workflow with the QA leads themselves — three tiers of severity, clear escalation criteria, and a "watch" state that didn't auto-generate a deviation. That got them onside.
We stopped trying to replace the source systems. We started asking better questions of them.
Priya Nair, Chief Quality Officer
Measured impact
47%
Fewer trend-triggered investigations year on year
4.2 h
Median time to signal
6
Sites live within nine months
The method, in brief
1
Inventory every quality data source across six sites — Cataloged every LIMS instance, MES vintage, and standalone quality database. Identified the ~40 signal streams that mattered for cross-site trending.
2
Build a canonical process performance model — Defined the common data model above the source systems. Every downstream chart, alert, and dashboard reads from the canonical model, not the source.
3
Write thin adapters per source system — One adapter per LIMS instance and MES vintage, mapping native tags to the canonical model. Adapters are the only per-site code — everything above is shared.
4
Co-design the signal-triage workflow with site QA — Sat with QA leads at each site to define severity tiers and escalation criteria. The "watch" state without auto-generated deviations was the unlock.
5
Roll out one site per month — Sequenced the rollout to give each site a full month of adoption before adding the next. All six sites live within nine months.
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