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Operational analytics and process intelligence

Operational analytics that give the COO and operations managers current throughput, capacity and service-level numbers, built on governed data from your ERP and operational systems.

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Line 2 is today's bottleneckHourly throughput shows Line 2 falling from 190 to 156 units an hour after 09:00 while Lines 1 and 3 stay near their targets of about 190 units.Line 2 is today's bottleneckUnits per hour against target, 06:00 to 14:00Demo dataOutput vs plan-6%Line 2 now156 units/hOrders at risk47140160180200220Line target 190 units/hLine 1 182Line 3 201Line 2 156Down 34 units/h since 09:0006:0008:0010:0012:0014:00Line 2 is today's bottleneckHourly throughput shows Line 2 falling from 190 to 156 units an hour after 09:00 while Lines 1 and 3 stay near their targets of about 190 units.Demo dataLine 2 is today's bottleneckUnits per hour against target, 06:00 to 14:00Output vs plan-6%Line 2 now156 units/hOrders at risk47Line target 190 units/h220200180160140Line 3 201Line 1 182Line 2 15606:0010:0014:00Down 34 units/h since 09:00

Our operational analytics work starts from a simple problem: operations teams make decisions every hour, but their reports often arrive weekly, built by hand from ERP exports and spreadsheets. By the time a bottleneck shows up in a report, the cost has already been paid in overtime, late orders or idle capacity.

We build operational analytics on the systems you already run, such as NetSuite, Odoo, Zoho and your field or project tools. We define the KPIs with your operations leads, connect the data and deliver an operations dashboard that refreshes through the day.

Supervisors, planners and the COO then act on the same current numbers.

What it is

Operational analytics manages today's work, not last month's

Definition

Operational analytics is the use of current data to manage day-to-day operations: production, fulfillment, service delivery, field work and back-office processing. It answers questions like what is late now, where capacity is short this week and which step is slowing throughput.

  1. Where traditional reporting looks back at last month, operations analytics runs at the pace of the work. Data refreshes frequently, KPIs are tied to targets and dashboards are built for the frontline teams who can act, from shift supervisors to the COO.

  2. It depends on two foundations: clean, connected data from operational systems, and clear metric definitions agreed with the teams who use them. Without both, an operational dashboard shows numbers nobody trusts.

  3. Operational analytics also differs from analytics for go-to-market teams. Our focus is physical and service operations, such as supply chain, production, field service and facilities, where throughput, capacity and on-time delivery drive cost.

Comparison

Operational analytics and traditional BI answer different questions

Both matter. They answer different questions at different speeds.

Ops analyticsTraditional BI
Main questionWhat needs attention now?How did we perform last period?
UsersSupervisors, planners and the COOExecutives, finance and analysts
RefreshHourly or daily, with real-time data where a decision needs itWeekly or monthly
Typical outputOperational dashboard with alerts and targetsManagement report and trend analysis
ActionReassign capacity, expedite orders, fix a process stepSet budgets, targets and strategy

Traditional BI tells you how last month went. Operational analytics tells you what to fix today. Operations usually need both traditional analytics and operational analytics, built on the same data model so the numbers agree. The rows below show where each approach fits. Both rely on one model.

Benefits

Operations analytics gives the COO control of throughput, capacity and cost

Operations analytics gives the COO control over the levers that drive cost and service.

Overtime and rework explain nearly 80% of the cost gapDemo cost per unit runs $14.20 against a $12.80 target; overtime adds $0.62, rework and scrap $0.48 and idle capacity $0.30.Overtime and rework explain nearly 80% of the cost gapCost per unit, target against actual, Q3 2026Demo data$12.00$12.50$13.00$13.50$14.00$12.80Target+$0.62Overtime+$0.48Rework and scrap+$0.30Idle capacity$14.20Actual$1.10 of the $1.40 gapOvertime and rework explain nearly 80% of the cost gapDemo cost per unit runs $14.20 against a $12.80 target; overtime adds $0.62, rework and scrap $0.48 and idle capacity $0.30.Overtime and rework explainnearly 80% of the cost gapCost per unit, target vs actual, Q3 2026Demo dataTarget$12.80Overtime+$0.62Rework and scrap+$0.48Idle capacity+$0.30Actual$14.20Overtime and rework: $1.10 of $1.40Bars start at $12.00 to show the gap.

Higher throughput. Current visibility of queues and bottlenecks lets teams rebalance work before delays build up.

Better use of capacity. Utilization by line, crew or team shows where capacity is idle and where it is overloaded, which supports staffing and scheduling decisions.

Reliable service levels. On-time delivery and turnaround tracked against target, with exceptions flagged early, protect customer commitments.

Lower cost per unit. Linking operational data to cost shows where waste, rework and overtime erode margin, which also gives the CFO a clearer view of operating cost.

One version of the truth. Supervisors, planners and leadership use the same definitions, so meetings focus on actions instead of debating whose numbers are right.

Faster improvement cycles. Because every process change is measured against a baseline, operations leaders can see whether a change worked, and scale it or reverse it with evidence rather than opinion. Each of these gains is tracked on the same dashboards, so the COO can report progress to the board in measured terms.

Components

A reliable operational analytics stack has five layers, none tied to one vendor

A dependable stack has five layers. We build each one with tools that fit your size and budget, without lock-in to a single vendor.

Five layers move operations data from capture to action

Select a stage, use Next or the arrow keys

Source systems. ERP, inventory, production, field service, project and ticketing tools capture work as it happens.

Step 1 of 5

A common mistake is buying a dashboard tool and expecting it to solve everything. The tool is only the top layer.

Reliable operational analytics needs every layer below it to work: capture, integration, modeling and governance. We build each one to fit your size and budget. Each layer below names the outcome it delivers, then how. Skip one, and the dashboard at the top shows numbers nobody trusts. We size each layer to your volumes and budget, and reuse what you already own. Each layer is documented for your team.

Component 1

Capture work as it happens

Source systems: ERP, inventory, production, field service, project and ticketing tools.

Component 2

Move data without manual exports

Integration: scheduled or event-driven pipelines through APIs, Zapier, Make or n8n.

Component 3

Calculate every KPI the same way

Data warehouse and model: a central store with documented KPI logic.

Component 4

Alert the right person in time

Dashboards and alerts: Power BI or your BI tool, with thresholds that notify owners. Where useful, reverse ETL writes KPIs back into frontline tools.

Component 5

Keep numbers trustworthy as operations change

Governance: owners, quality checks, access controls and a metric dictionary.

ERP platforms
How we work

Operational analytics runs as a loop from data to daily decisions

Operational analytics runs as a loop, not a one-off project.

The five steps at a glance

Select a stage, use Next or the arrow keys

Capture. Fix data at the source. Systems record orders, jobs and tasks, and we close capture gaps at the source.

Step 1 of 5

The difference from traditional reporting is the loop. Data does not end in a report. It ends in an action, and the result of that action becomes new data.

Each step below names the outcome first. Together they turn operations data into a daily habit rather than a monthly review. The loop starts small, with advice on one process and one dashboard, and we set it up once your data and team are ready. Once it runs reliably, we extend it to the next process. Each turn of the loop is usually faster, because the data model and the habits are already in place. Supervisors and the COO see the same numbers at every turn. Each step names its outcome first, then the work.

business process automation
Sample views

Operations dashboard examples that flag overbooked crews early

Each operations dashboard example here is built on demo data, not client data. The chart shows the capacity view: planned hours as a share of available hours, by crew, for the next four weeks. Crew B is planned at 121%, 118% and 114% for the next three weeks, while Crew C runs at about 70%, so the planner knows which crew to rebalance.

Crew B is overbooked for the next three weeksCrew B is planned at 121%, 118% and 114% of available hours for the next three weeks, while Crew C runs at about 70%.Crew B is overbooked for the next three weeksPlanned hours as a share of available hours, by crew, next four weeksDemo dataWeek 1Week 2Week 3Week 4Crew A88%86%84%83%Crew B121%118%114%97%Crew C74%71%69%72%Crew D92%94%95%90%Above 110%for 3 weeks<80%80-89%90-100%>100%Crew B is overbooked for the next three weeksCrew B is planned at 121%, 118% and 114% of available hours for the next three weeks, while Crew C runs at about 70%.Crew B is overbooked for thenext three weeksPlanned as % of available hours, 4 weeksDemo dataW1W2W3W4Crew A88%86%84%83%Crew B121%118%114%97%Crew C74%71%69%72%Crew D92%94%95%90%<80%80-89%90-100%>100%
1

Throughput: find today's bottleneck

Output per hour, shift and day against target, with work in progress by stage.

2

Capacity: balance the load weeks ahead

Planned against available capacity, utilization by resource and overtime trends.

3

Service levels: save orders at risk

On-time delivery, turnaround and backlog age, with at-risk orders listed.

4

Executive view: compare sites on one page

Throughput, capacity, service and cost per unit across sites, with trends.

Each operations dashboard answers one question for one role. Supervisors ask what needs attention this shift. Planners ask where capacity will run short. The COO asks how every site is performing.

The four views listed below follow that split. Each one runs on the same data model, so the numbers agree from the shop floor to the boardroom.

Every chart title states its insight, so a reader knows the point before reading the axis. In an engagement, we adapt each operational dashboard to your lines, crews, sites and targets, and keep the same insight-first titles and drill paths so users find the cause behind each number quickly. Each view also links to the metric dictionary, so anyone can check how a figure was calculated before acting on it in a shift meeting or a board review.

KPIs we track

Six operational analytics KPIs show whether operations are on track

Every KPI has a written definition, a target and an owner.

GoalDeliver on time, at target cost and quality
  1. Equipment
    • OEE
  2. Output
    • Throughput
    • Cycle time
  3. People
    • Utilization
  4. CustomerReview first
    • On-time delivery
  5. Quality
    • First-pass yield
Goal, drivers, metrics. The highlighted driver is the one to review first.

OEE: get more from equipment

Demo

Availability multiplied by performance and quality.

Throughput: meet output targets

Demo

Units, orders or jobs completed per period, by line, team or site.

Utilization: staff to real demand

Demo

Hours used as a share of hours available, by resource.

On-time delivery: keep commitments

Demo

Share of orders delivered by the committed date.

Cycle time: shorten lead times

Demo

Elapsed time from start to completion of an order or request.

First-pass yield: cut rework

Demo

Share of units or jobs completed correctly the first time.

Too many KPIs hide the one that matters. Each operational dashboard KPI should lead to an action. We keep the list short and tie each KPI to a target and an owner.

The decision each one supports is named in the title. The definitions are agreed with operations and finance before any build, so the numbers mean the same thing in every meeting. Each KPI below names the decision it supports, then its definition. Targets are set with the owner of each metric, and alerts fire when a value moves outside its range. Together they show whether operations deliver on time, at target cost and quality. Each KPI is shown by line, team, site and period, against its target.

Use cases

Operational analytics puts live data behind one decision in every operations function

Use cases vary by function. Common ones include the following.

  1. Supply chain

    ResultAvoid shortages and excess stock

    What we build

    Stock cover, supplier lead times and fill rate, linked to demand and open orders.

  2. Capacity planning

    ResultDecide when to hire or subcontract

    What we build

    Demand against capacity by team, line or crew, weeks ahead.

  3. Manufacturing

    ResultOne set of production numbers

    What we build

    OEE, throughput, scrap and downtime by line, connected to the ERP.

  4. Use case 4

    ResultField service and construction: keep jobs on budget

    What we build

    Completion, crew utilization, first-time fix rate and project cost against budget.

  5. Facilities

    ResultCut the backlog across sites

    What we build

    Occupancy, maintenance backlog, work order turnaround and cost per square foot.

The problem is similar in every function. Decisions are made daily, but data arrives weekly.

The insight is that each function has one or two decisions where current data changes the outcome. Planners decide what to order. Leaders decide when to hire. Supervisors decide where to send a crew.

We build operational analytics around those decisions first. The aim is fewer shortages, fewer late orders and less idle capacity. Each card below names the decision, then the data behind it. Each use case reuses the same data model and the same KPI definitions. That means a second function builds on the work done for the first, and leaders see one consistent view of operations across every function and site. Supply chain or capacity planning is a common starting point, because those decisions are often the most costly to make on old data. Others follow once the operational analytics model holds up. Each card below names the decision first, then the data that supports it in practice.

supply chain analyticscapacity planningmanufacturingconstructionreal estate
Common challenges

Five obstacles stall operational analytics, and each has a known fix

Operational analytics projects tend to hit the same obstacles. None of them is unusual, and each one has a known fix.

  1. 1

    Systems stop conflicting

    We connect ERP, field and project tools and set one system of record per data object.

  2. 2

    Data arrives on time and complete

    We fix capture at the source with forms, validation rules and automated syncs.

  3. 3

    Every team reads KPIs the same way

    We agree definitions with operations and finance before any build.

  4. 4

    Dashboards become part of daily work

    Each view serves one role and one decision, with training and a review cadence.

  5. 5

    Capacity gaps stop blocking progress

    We can stay on as a fractional data team.

Services

Four operational analytics services share one connected data model

Each service solves one operational question. All four share the same data model, so the work done for one usually makes the next faster and cheaper. A common starting point is the service closest to the biggest operational pain, with the others added as the model grows and the first dashboard shows its value. Each card names the outcome first, then the service behind it. All four are documented and owned by you.

Four services share one operations data modelSupply chain, capacity planning, process automation and data modernization all run on one shared operations data model, so each service makes the next faster.Supply chainanalyticsStock cover, lead timesCapacityplanningDemand against capacityProcessautomationClean capture, workflowsDatamodernizationWarehouse, pipelinesShared data modelFour services share one operations data modelSupply chain, capacity planning, process automation and data modernization all run on one shared operations data model, so each service makes the next faster.Shared data modelSupply chainanalyticsStock cover, leadtimesCapacityplanningDemand againstcapacityProcessautomationClean capture,workflowsDatamodernizationWarehouse, pipelines
Supply chain analyticsCapacity planningProcess automationData modernizationsolutions

Name one hard operational decision, and we will scope the data to support it

Tell us which operational decision is hardest to make with your current reports, and which systems hold the data.

We will outline the KPIs, data model and operational dashboard needed, and the engagement that fits: advisory first, training where your team needs it, then a defined project or a fractional data team once you are ready.

If you are not sure where to start, our AI and data readiness assessment maps your data maturity first.

Before the first session, it helps to share a recent operations report, a list of the systems involved and the KPIs your team reviews today. We will come back with a first view of the data model and a realistic scope for a first operational dashboard.

AI and data readiness assessmentstrategic planning advisory
See how we work
Share a report, leave with a scoped first dashboardThree steps to a first operations dashboard: share a report, join a working session, receive a scoped first dashboard.1 Share a reportA weekly ERP export2 Working sessionDecision, data, KPIs3 First dashboardScoped and estimatedLine output todayShare a report, leave with a scoped first dashboardThree steps to a first operations dashboard: share a report, join a working session, receive a scoped first dashboard.Share a reportA weekly ERP exportWorking sessionDecision, data, KPIsFirst dashboardScoped and estimated123Line output today
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