Building Enterprise Resilience From QRM Signals

By Irwin Hirsh, Q-Specialists AB

Most biopharma organizations can point to enterprise resource management (ERM) activity: a risk register, periodic workshops, board updates, and corporate KPIs intended to provide assurance. Yet surprises still occur — quality events that escalate into supply disruption, delayed milestones, or regulatory exposure. The issue is rarely competence or intent.

The issue is that risk is not consistently operating as a decision system.

In practice, many companies run two parallel worlds:

• The enterprise risk world, where risks are described in broad outcomes (supply continuity, compliance, cash runway, launch timing).
• The operational quality world, where the earliest evidence is generated (deviations and investigations, CAPA recurrence, change control integrity, continued process verification (CPV) drift, supplier performance, complaint signals, audit outcomes).

When these worlds are not designed to connect, they meet late. What leadership experiences as a “sudden enterprise risk event” is often the end stage of a gradual slide that was visible in weak signals — just not translated into decisions early enough.

This gap matters even more in small and outsourced biopharma.

The core claim of this article is simple: many enterprise risks begin as quality weak signals.

They show up as small degradations that are easy to normalize:

• Investigation aging becomes routine.
• CAPAs close, but recurrence returns.
• Supplier performance slips in small increments.
• Change controls proceed, but downstream stability erodes.
• CPV trends drift before anyone calls it loss of control.

These are not merely “quality inconveniences.” They are early evidence that control is weakening, and in an outsourced model, they may be the only early evidence possible.

This is where the hierarchy of metrics as presented in Part 1 of this series  becomes a very practical model. The point is not more measurement; it is line of sight. Connect the enterprise outcomes leadership cares about to a small set of decision-driving signals, backed by diagnostics that explain why they moved.

One way to tell whether that system exists is signal latency, the elapsed time from first detection of drift to an accountable decision and verified action.

To quickly assess whether your risk management is operating as a system: When a risk signal changes, do you already know who reviews it, where it is reviewed, and what decision or action follows?

If your honest answer is “it depends,” then escalation is still personality-driven — and risk will be managed late, by design.

Let us now explore the decision pathway that makes the answer “yes” consistently.

The Decision Pathway: From QRM Signals To Enterprise Decisions

Enterprise “surprises” persist when quality weak signals are visible, but the route from signal to decision is ill-defined. The requirement for quality process maturity is therefore clear: escalation must function as a controlled pathway, not an informal communication pattern.

Risk governance is an engineered decision system: when a defined signal crosses a threshold, a named forum makes a timebound decision, assigns ownership, and verifies whether exposure reduced.

What escalation means in practice

Escalation is a designed control pathway, not a social pattern. It exists only when a defined signal crosses a threshold and triggers a timebound decision in a named forum, with explicit ownership and verification. Note, if in your system “yellow” changes nothing, no assigned action, no due date, no documented decision, no follow-up evidence, then escalation is not happening. The organization is observing drift until it becomes urgent.

A practical approach is in place when simple questions are clearly answered:

If you want to see whether escalation is truly designed, ask whether you can answer the questions below without caveats. If any answer is unclear, you do not have an escalation route: you have a hope that someone will notice and act.

Control pathway question	What you must define	Practical output
What signal matters?	A decision-driving indicator (leading where possible)	Indicator definition + source
What change triggers action?	Threshold(s) and trigger rules	Trigger rule + timebox
Where is it decided?	The named forum and cadence	Forum agenda item
Who owns the response?	Accountability and decision rights	Owner + decision log
How do we verify it worked?	Effectiveness criteria and follow-up cadence	Verification evidence + date

 

This is how QRM becomes the starting engine of ERM: signals are wired to decisions and verification.

The signal-to-decision mechanism (minimum viable design)

Keep the mechanism minimal and explicit.

You are designing a short chain from operational signals to decisions and verification for control of enterprise outcomes.

Design step	Purpose	Keep it minimal by ...
Anchor the enterprise outcome	Ensure signals matter to leadership	One or two must-not-fail outcomes
Pick decision-driving signals	Detect drift early enough to act	Three to five indicators per outcome
Add diagnostics	Explain “why it moved” quickly	One to three diagnostics per signal
Set thresholds	Force review and action when needed	Triggers force forum + owner
Define ownership & verification	Convert action into risk reduction	Decision rights + effectiveness check

 

Can you quickly and clearly explain your enterprise escalation pathway:

outcome → signal → trigger → forum → owner → verification?

If not, you cannot say you have a good ERM system. The design is too complex to run consistently or may not even be described.

What makes a metric a governance metric

A governance metric is not a KPI you admire on a dashboard. It is a signal that triggers a decision pathway. If it does not change a decision, it is reporting.

Governance property	Plain-English meaning	Typical failure mode
Decision-linked	Constrains/triggers choice	Dashboard creation is the goal; no actions taken
Owned and placed	Someone is accountable; reviewed in a named forum	“Everyone owns it,” so no one does
Thresholds	Triggers are defined and nonnegotiable	Yellow becomes a parking lot; drift normalizes until red.
Verified	Actions are checked for effectiveness	Activity without control, e.g., CAPA, lack of predefined effectiveness goals; “no recurrence” alone is insufficient.

 

This is why many ERM efforts disappoint: they produce an inventory of risks and metrics but do not specify the operating rules that turn movement into decisions and verified actions.

A practical distinction helps keep the work honest: a risk register is an inventory of concerns; an ERM operating system is a closed loop. The loop changes decisions, assigns ownership, and verifies reduced exposure.

The operating model: a three-tier governance loop

The simplest way to operationalize the signal-to-decision mechanism is a three-tier loop. The tiers are not hierarchy for its own sake; they match decisions to the right forum and time horizon.

A useful design principle is: escalate only when the decision changes.
If Tier 1 can contain and verify, keep it there. If Tier 2 can resolve within agreed risk limits, Tier 3 should be informed, not asked to decide. This keeps the system fast without creating meeting overload.

Tier	Purpose	Typical inputs	Typical decisions	Expected outputs
Tier 1: Operational	Detect/contain drift	deviations, investigations, early warnings	containment, characterization, escalation recommendation	updated risk statement, containment plan, trigger assessment
Tier 2: Cross-functional	Resolve trade-offs	recurring signals, cross-vendor issues	systemic CAPA, vendor intervention, control redesign, prioritization	funded plan, owners, timelines, verification plan
Tier 3: Executive	Accept/mitigate enterprise exposure	material risk, supply/timeline impact	risk acceptance, resource moves, strategy adjustments	explicit decision record, constraints, accountability

 

Decision rights and verification (the part most companies skip).

• Tier 1 owns containment and immediate characterization.
• Tier 2 owns cross-functional trade-offs and systemic fixes within agreed limits.
• Tier 3 owns the decisions that change enterprise commitments: accepting material residual risk, reallocating budget or headcount, pausing activities, switching suppliers, or adjusting timelines.

Verification is what prevents “activity” from being mistaken for “control.” Each Tier 2 or Tier 3 decision should include a simple effectiveness check: what evidence will demonstrate that risk exposure has reduced (or that residual risk is understood and accepted), and when that evidence will be reviewed.

Where the hierarchy of metrics fits

The hierarchy is the line of sight that keeps the loop focused: In earlier articles in this series, the hierarchy of metrics spans strategic goals, KPIs/OKRs, critical success factors, and diagnostics. For risk governance, the hierarchy should be: outcomes, to decision-driving signals, to diagnostics, because those layers directly drive thresholds, escalation, and verification.

• Outcome metrics sit at the top (what leadership is protecting).
• Governance signals sit in the middle (the few indicators that change decisions early).
• Diagnostics sit below (the short list that explains the signal and guides action).

This keeps Tier 2 and Tier 3 discussions anchored in the same logic: not “how do we feel about risk,” but “what moved, why, what do we do, and did it work?”

Minimum viable standard documents (keep it light)

To run governance consistently without bureaucracy, you need only three standard documents:

1. Risk Indicator Specification Sheet (one page)
   Outcome protected; signal + source; thresholds; owner + forum; diagnostics; expected actions by tier; verification method.
2. Decision Log (lightweight)
   What changed; decision (who/where); actions and owners; due dates; verification evidence; residual risk statement (as needed).
3. Threshold Playbook (short)
   What yellow/red means in actions (not adjectives); time expectations; escalation rules if actions stall.

This operating model does not replace QRM documents. It connects them and creates line of sight from operational signals to the decisions that determine enterprise exposure.

Guardrails to prevent sophistication from becoming over-engineering

I have found that teams often over-design risk models before they have a working decision pathway. The table below lists common traps and the practical guardrails that keep the system practical.

Common trap	Why it happens	What to do instead
Metric inflation	Fear of missing a signal	Fewer signals; stronger diagnostics
Lagging-only dashboards	Outcomes are easier to measure	Add leading indicators
False precision	Scoring feels rigorous	Require decision/resource impact
Yellow means nothing	Conflict avoidance	Define action/timebox or remove yellow

 

The goal is not analytical elegance. The goal is predictable decision-making under real-world constraints. With the decision pathway in place, QRM becomes the starting engine of ERM: early/weak signals trigger timely decisions, ownership is explicit, and actions are verified.

Two Examples: How The Decision Pathway Prevents Late Surprises

The examples below focus on supply continuity and process capability because they are easy to visualize. The same signal-to-decision pattern applies to compliance-facing risks as well — for example, investigation health, CAPA effectiveness, and data integrity signals — where thresholds and verification matter at least as much as the metric itself.

Each case follows the same pattern:

enterprise outcome → weak signal → threshold → tiered decisions → verification.

The point is not the specific metrics. The point is the decision wiring.

Example 1: Supplier instability - A partner-network and supply continuity risk

Element	Content
Enterprise outcome protected	Uninterrupted supply for clinical/commercial demand
Critical success factor (CSF)	Critical material availability is maintained within qualified supply lanes (timeliness and incoming quality), and partner responsiveness (investigation and change notifications) remains within expectations.
Owner + forum cadence	Owner: Supply/partner oversight lead (quality + supply chain) Forum/cadence: Tier 1: weekly supplier signal review Tier 2: monthly cross-functional supply/quality governance Tier 3: enterprise review ad hoc when red or exposure is material
Decision-driving signal(s)	Supplier performance degradation for a critical material (e.g., on-time-in-full trend); rising incoming quality-related deviations; delayed/low-quality investigations at CMO
Diagnostics "why it moved"	Identify the driver (material/lot family), failure mode pattern, partner response cycle time, and any recent supplier/CMO changes.
Trigger / threshold	On-time in-full (OTIF) below target for two consecutive periods and/or repeat deviation pattern tied to the same supplier/material class within a defined window
Tier 1 operational control	Contain and characterize: temporary increased testing/controls (as needed), quarantine rules where appropriate, confirm signal validity, assess immediate lot/batch exposure, escalate if trigger is met
Tier 2 cross-functional resolution	Vendor intervention and systemic fix: joint RCA, tighten controls (specs/sampling/incoming inspection/notifications), prioritize audit/assessment, agree on corrective actions and timelines, evaluate secondary sourcing feasibility and lead times
Tier 3 trigger for executive decision	Enterprise trade-off: accept temporary exposure with explicit constraints, fund/accelerate dual-sourcing, renegotiate commitments, adjust supply/launch plans, or change strategic supplier posture
Verification (evidence of control)	OTIF recovery trend: reduced incoming-related deviations; investigation cycle time restored; confirmed effectiveness of supplier/CMO controls over a defined verification period
Typical failure mode to avoid	“Keep watching” without a trigger: performance slips are normalized until a missed delivery or batch impact forces crisis sourcing with fewer options and higher cost.

 

Example 2: Process performance drift becomes batch disposition and supply risk

Element	Content
Enterprise outcome protected	Stable process performance and reliable release outcomes (minimized risk of batch failure, shortage, or regulatory concern)
Critical success factor (CSF)	The process remains within the defined control strategy with stable variability, disciplined change control, and predictable batch disposition performance.
Owner + forum (cadence)	Tier 1: routine process/quality review (e.g., weekly) Tier 2: cross-functional performance review (e.g., monthly; QA/tech ops/MSAT/supply) Tier 3: executive trade-off forum if thresholds indicate material exposure (e.g., quarterly or ad hoc)
Decision-driving signal(s)	Early drift in process performance: widening variability in critical parameters or outcomes; recurring minor deviations concentrated in one step; yield erosion; increasing rework or repeat testing; emerging OOT patterns (often visible in CPV/continued verification systems where used).
Diagnostics “why it moved”	Identify where the drift concentrates (step/shift/line/site; lot range). Correlate to recent changes (raw materials, equipment state, utilities, procedures, staffing/practices, supplier inputs). Distinguish special cause vs. gradual drift; review capability indicators for the most critical parameters.
Trigger/threshold	Sustained drift beyond agreed trend limits and/or repeated deviations indicating loss of control in a critical step; sustained variability increase over a defined number of lots; yield/throughput erosion beyond an agreed threshold that threatens disposition predictability.
Tier 1 operational control	Contain and assess: intensified monitoring, immediate risk evaluation for impacted lots, confirm signal vs. noise, implement short-term controls to protect batch disposition while diagnostics are gathered.
Tier 2 cross-functional resolution	Systemic intervention: tighten parameter controls, verify equipment/qualification state, harmonize operator practices, investigate raw material/supplier contribution, strengthen change control governance for the affected step, implement targeted process improvements with a defined verification plan.
Tier 3 executive decision when triggered	Enterprise trade-off: fund major remediation, pause or sequence tech transfer/scale-up activity, adjust supply commitments, authorize strategic changes (e.g., supplier switch or process redesign), and define restart criteria and acceptable residual risk.
Verification evidence of control	Restored stability and capability: reduced deviation recurrence in the critical step; sustained performance over a defined verification window; improved predictability of yield and batch disposition cycle time.
Typical failure mode to avoid	Drift is normalized as “expected variability” until an OOS/OOT or missed supply commitment forces reactive changes with fewer options, higher validation/regulatory burden, and greater schedule impact.

 

You will notice the pattern in these examples: the enterprise outcome remains stable, but the operational signals and diagnostics differ by context. That is the practical benefit of a hierarchy of metrics. It lets leadership ask one consistent question — “are we still on track to protect the outcome?” — while Tier 1 and Tier 2 use the diagnostics to determine what is actually drifting and what to do next.

If you can run these patterns reliably for a small number of outcomes, you have effectively created the core of an ERM operating system: not a register but a repeatable route from signal movement to enterprise decisions and verified control.

Your Implementation Playbook: Start Narrow, Prove Value, Scale

Treat this as an operating upgrade, not a transformation program. The goal is to install a runnable loop, prove it works, then expand.

1. Choose one or two must-not-fail enterprise outcomes. If the outcome is not important enough to move resources, governance discipline will not sustain.
2. Select three to five decision-driving signals per outcome. Prefer leading indicators. Pair each with one to three diagnostics to accelerate root-cause thinking.
3. Define thresholds that force review. Avoid “watch closely.” If yellow has no action, redefine it or remove it.
4. Install the three-tier loop with a short cadence. Consistency beats sophistication (weekly Tier 1; biweekly/monthly Tier 2; monthly/quarterly Tier 3, depending on tempo).
5. Verify effectiveness before expanding scope. Scale because you have proof: faster containment, reduced recurrence, improved supplier performance, stabilized trends, fewer late escalations.

A practical scaling rule: expand by cloning the operating model (signals, thresholds, forums, verification), not by adding more and more metrics.

What Changes When It Works

When the system is working, the change is visible in behavior, not dashboards:

• Escalation becomes predictable, not personality-driven.
• Leaders receive fewer surprises because weak signals trigger decisions while options still exist.
• Teams spend less time debating whether something is “bad” and more time deciding what to do, because thresholds and diagnostics structure the discussion.
• Risk registers become more credible because they are fed by operational intelligence that is already being governed.

Summary of the essential points

1. Enterprise risk often begins as quality weak signals. If you wait for outcomes, you are already late.
2. Risk governance is a decision system. Communication is not an escalation pathway.
3. Use a hierarchy of metrics for line of sight: outcomes → signals → thresholds → actions → verification.
4. Keep it minimal and runnable: few indicators, clear triggers, defined forums, accountable owners, verified effectiveness.
5. Start with QRM as the engine. The signals and discipline already exist, your job is to connect them to enterprise decisions.

About The Author:

Irwin Hirsh has 30 years of pharma experience with a background in CMC encompassing discovery, development, manufacturing, quality systems, QRM, and process validation. In 2008, Irwin joined Novo Nordisk, focusing on quality roles and spearheading initiatives related to QRM and life cycle approaches to validation. Subsequently, he transitioned to the Merck (DE) Healthcare division, where he held director roles within the biosimilars and biopharma business units. In 2018, he became a consultant concentrating on enhancing business efficiency and effectiveness. His primary focus involves building process-oriented systems within CMC and quality departments along with implementing digital tools for knowledge management and sharing.