Reporting And Intelligence Guide

This guide explains what reporting data Excelsus IQ is meant to give a print company, how the reporting tools actually work, and what actions managers, estimators, customer service, production, purchasing, and finance should take from that data.

The goal is not to create more dashboards for their own sake. The goal is to help the shop use one clean operating record set for decisions.

What Reporting Is Supposed To Do

In this system, reporting should help the shop answer questions like:

• what is due and at risk right now
• what is profitable and what only looks busy
• where we are leaking time, waste, or margin
• which customers and product types convert well
• which quotes need action today
• which vendors are helping or hurting execution
• how much cash is still open

If reports are not tied to real actions, the team will stop trusting them.

Source-Of-Truth Rule

The reports only stay useful if the operating data is clean.

That means:

• quotes should be built from real customer, catalog, and pricing defaults
• approved quotes should become orders instead of being retyped elsewhere
• production should clock time against the real scheduled tasks
• purchase orders and receiving should be recorded in the system
• invoices and payments should be recorded against the correct customer and job
• quote outcomes should be captured after the sale is won, lost, expired, or abandoned

Bad source data creates misleading reports. Reporting is not the place to fix operating discipline after the fact.

Reports Center Structure

The reports area is split into several access-controlled sections.

Use the simplest section that answers the question.

• Use Standard Reports when the answer already exists.
• Use Report Builder when you need your own column mix.
• Use Dashboards when you need trends, patterns, or decision support.
• Use Accounting Reports and Invoice Reports when money movement is the real question.

Standard Reports

Standard reports are the fastest path for recurring operational questions.

They are meant for things like:

• job pipeline review
• customer tax-default review
• quote register review
• purchase order review
• vendor directory review
• invoice operations shortcuts

Use these when the team needs repeatable answers without redesigning a report every time.

Report Builder

Report Builder is the ad hoc reporting tool. It is built for users who know the question they want answered and need to choose the exact fields and output order.

The builder supports these current report bases:

How To Use Report Builder Correctly

Build reports from the question backward.

Good examples:

• which rush jobs due this week are still not shipped
• which jobs for one customer still have open balance
• which quotes over a certain amount expired this month
• which PO items were received late from one vendor

Bad examples:

• adding every possible column because it might be useful later
• saving reports with no naming standard
• using the builder when a standard dashboard already answers the question

Recommended Builder Workflow

1. choose the right report base first
2. add only the fields needed to answer the question
3. set output order so the most important columns come first
4. save runtime filters if the report will be reused by date, customer, PO number, status, or similar prompts
5. save the report with a clear business name

Good saved report names:

• Open Rush Jobs By CSR
• Uninvoiced Shipped Orders
• Declined Quotes Over 2500
• POs Due This Week

Bad saved report names:

• Test
• Report 2
• Custom Export

Runtime Filters

Saved builder reports can preserve runtime filter prompts such as:

• date
• date range
• customer
• status
• PO number
• text contains search

That matters because the saved report should preserve the structure of the question without freezing the answer to one day forever.

What To Do With Builder Output

Use builder output for:

• management reviews
• export-to-CSV work
• one-off audits
• customer-specific or rep-specific operating lists
• exception reports

Do not use builder exports as a permanent shadow system. If a saved report becomes mission-critical, standardize the naming, owner, and review cadence.

Dashboards

Dashboards are where the system moves beyond list views and starts showing patterns.

The dashboards section includes:

• financial and KPI snapshots
• win / loss review
• quote intelligence
• promise date accuracy
• efficiency reporting
• constraint dashboard
• vendor scorecards
• vendor pricing changes

These are not all for the same audience.

Dashboard Use Rule

Every dashboard should answer one of these:

• where do we need to act today
• what pattern is improving or worsening
• which team or process needs coaching
• where are we taking risk without realizing it

If the team cannot name the action, the dashboard is just decoration.

Quote Intelligence

Quote Intelligence exists in two forms:

• a per-quote intelligence panel on quote detail
• a dashboard report showing pattern trends across quote history

Both are advisory only. They are meant to improve follow-up, pricing discipline, and understanding of what the shop wins well.

What The Per-Quote Intelligence Panel Shows

On the quote itself, the panel shows:

• win probability
• confidence level
• recommended action
• resolved history count
• explanation text
• top drivers
• similar historical quotes
• outcome feedback controls

How The Quote Score Works

The current engine is rules-based. It does not make up an answer from nothing.

It starts from historical resolved quote outcomes and adjusts the score using factors such as:

• customer history
• product history
• sales rep history
• margin band
• quote amount band
• quantity band
• revision count
• first response versus revised quote
• competitive pricing pressure
• seasonality
• turnaround pressure
• similar historical quotes

The system then assigns:

• a win probability percent
• a confidence label
• a recommended action

Recommended Actions

The current action categories are:

Confidence

Confidence tells you how much clean history the score had to work with.

• High confidence means there is enough history and enough matching factors to lean on the result more heavily.
• Medium confidence means useful signal exists, but judgment still matters.
• Low confidence means the system is showing a directional clue, not a strong prediction.

Do not confuse a high score with high confidence. A quote can score well with thin data and still deserve human caution.

Top Drivers

Top drivers explain why the score moved.

Examples:

• customer history is strong
• comparable product types close well
• multiple revisions are hurting odds
• rush pressure is making the deal riskier
• pricing-sensitive notes or loss history suggest competitive pressure

This is the part managers and estimators should actually read.

The right use is:

• if price pressure is negative, review pricing strategy
• if revision pressure is negative, tighten scope and get decision-makers aligned
• if customer and product history are strong, prioritize follow-up and speed

Similar Historical Quotes

The quote panel shows similar historical quotes, not similar completed jobs.

Those matches are driven by items such as:

• same customer
• shared product type
• same sales rep
• similar quote amount band
• similar quantity band
• similar turnaround pressure

Use similar historical quotes to answer:

• have we won this kind of quote before
• is this customer usually a closer or a shopper
• are we seeing the same kind of quote come back repeatedly

Do not use similar historical quotes as a substitute for actual cost history.

Similar Historical Quotes Versus Similar Jobs

This distinction matters.

• Quote Intelligence uses historical quote outcomes.
• The quote builder's similar-jobs view uses completed jobs with actual cost data.

Use quote intelligence when the question is:

• are we likely to win this
• what is making the deal stronger or weaker

Use similar jobs when the question is:

• how did close-to-this work actually cost out in production
• what margin did we really earn on similar specs

Outcome Feedback

Outcome feedback is the training and analytics label for quote history.

Capture:

• won
• lost
• no decision
• expired

When a quote is lost, also capture:

• reason lost
• competitor
• pricing notes
• freeform notes

This does not automatically change the entire operational quote status model by itself. It labels the quote for future analysis and intelligence.

If the team never records outcomes, quote intelligence becomes thin and unreliable.

Quote Intelligence Dashboard

The Quote Intelligence dashboard is the cross-quote pattern view.

It tells you things like:

• resolved quote count
• won quote count
• overall close rate
• recommendation mix
• win rate by estimator
• win rate by customer
• win rate by product type
• margin bands versus close rate
• turnaround pressure versus close rate
• quote amount bands versus close rate

How To Use Quote Intelligence Data

Use win rate by estimator to:

• spot coaching needs
• see who is qualifying well
• identify quoting teams that need pricing or scope discipline

Use win rate by customer to:

• identify strategic accounts
• identify chronic shoppers
• decide where relationship effort pays back

Use win rate by product type to:

• see what the shop wins best
• identify offerings that may be overquoted or poorly positioned
• decide where to focus sales energy

Use margin bands versus close rate to:

• test whether the shop is underpricing to win
• find where margin is healthy without collapsing close rate
• separate real pricing pressure from estimator fear

Use turnaround pressure versus close rate to:

• see whether rushed work is actually worth chasing
• coach staff on when rush promises reduce close quality or execution quality

Use amount bands versus close rate to:

• understand which deal sizes convert best
• see whether small quotes are consuming too much effort
• identify strategic thresholds where management review should be required

What Quote Intelligence Should Not Be Used For

Do not use it for:

• automatic discounting
• approving bad-margin work just to increase close rate
• blaming one rep from small sample sizes
• treating one dashboard period as permanent truth

It is meant to improve judgment, not replace it.

Win / Loss Reporting

Win / Loss reporting is the more direct sales-outcome view.

It is built for:

• total quoted dollars
• won dollars
• win rate
• average margin on won jobs
• loss reasons
• rep rows
• quote detail rows

Use it when management wants direct sales-performance review instead of predictive pattern review.

How To Act On Win / Loss Data

• If loss reasons cluster around price, review estimating strategy and competitor pressure.
• If loss reasons cluster around timeline, review promise-date discipline and shop capacity.
• If one rep wins at low margin, coach qualification and pricing.
• If won dollars are high but average margin is weak, revenue quality needs attention.

Promise Date Accuracy

Promise Date Accuracy is a scheduling-intelligence tool, not just a report.

It tells the team:

• how many jobs were predicted
• how many shipped
• on-time rate
• override rate
• forecast accuracy distribution
• override outcomes
• departments causing promise risk
• on-time performance mix

How To Use Promise Date Data

Customer service and production should use it to answer:

• are we overpromising
• are managers overriding the recommended date too often
• which departments are creating backlog risk
• whether override behavior is helping or hurting on-time delivery

Good actions from this dashboard:

• adjust commitment discipline
• escalate staffing or machine bottlenecks
• stop promising dates earlier than the shop can hold

Efficiency Reporting

Efficiency reporting answers whether production is running close to plan.

Current areas include:

• waste reporting
• operator efficiency
• makeready reporting

Waste Reporting

Use waste reporting to review:

• spoilage cost
• waste percent
• weekly waste trends

Act on it by:

• finding repeat spoilage causes
• revising setup assumptions
• retraining process steps
• tightening quality gates

Operator Efficiency

Use operator efficiency to compare estimated versus actual hours.

Act on it by:

• finding consistent overruns
• coaching on setup discipline
• checking whether estimates are unrealistic
• separating a training issue from a planning issue

Makeready Reporting

Use makeready reporting to review setup time, run time, and trend direction by machine.

Act on it by:

• seeing where setup is drifting longer
• deciding when routing or batching needs to change
• validating machine standards against real time

Constraint Dashboard

The Constraint Dashboard shows queue dwell and bottleneck behavior between departments.

Use it when the shop feels busy but cannot clearly identify where work is sticking.

It helps answer:

• where jobs are waiting longest
• which departments are driving downstream delay
• which specific jobs are sitting in the bottleneck

This is for production leadership and operations review, not estimator guesswork.

Vendor Scorecards And Vendor Pricing Changes

Purchasing dashboards help the shop manage supplier quality and cost movement.

Vendor scorecards show measures like:

• on-time rate
• incidents
• spend
• receipt price drift or variance

Use them to:

• move work away from unreliable vendors
• support better vendor review conversations
• avoid paying higher prices without noticing

Vendor pricing changes help the shop review submitted supplier cost updates and maintain approval history.

Use that data to:

• control cost updates before they silently change estimating quality
• document who approved price changes
• keep catalog costing current

Accounting Reports

Accounting reports answer financial questions rather than pure operating questions.

They cover areas like:

• A/R aging
• revenue
• job profitability
• commissions
• overhead recovery
• invoice operations

How To Use Accounting Data

Use A/R aging to:

• prioritize collections
• spot chronic late payers
• decide whether new work needs tighter terms

Use revenue reports to:

• review monthly billing trends
• compare expected sales performance against actual invoiced work

Use job profitability to:

• compare estimate versus actual
• find jobs that looked profitable but were not
• improve future pricing and production standards

Use commissions to:

• validate payout logic
• catch disputes before payroll closes

Use overhead recovery to:

• see whether the shop is covering its fixed operating burden through real work volume

Invoice Reports

Invoice Reports are the operational billing workspace.

Use them for:

• invoice review
• payment allocation
• identifying open balances
• seeing ready-to-invoice jobs

This is where finance, admin, or CSR staff should work when they are trying to complete the billing cycle instead of just reviewing a KPI.

Role-Based Use Of Reporting

Owner Or GM

Use reporting to manage:

• revenue quality
• margin health
• close rate trends
• overdue receivables
• vendor performance
• bottlenecks

Main screens:

• Dashboards
• Accounting Reports
• Quote Intelligence
• Job Profitability
• Promise Date Accuracy

Estimating Lead

Use reporting to manage:

• close rate by product type
• margin bands versus close rate
• similar jobs with actuals
• NCR and waste feedback loops

Main screens:

• Quote Intelligence
• Win / Loss
• Job Profitability
• similar-jobs view in quote builder

CSR Or Sales Management

Use reporting to manage:

• follow-up priorities
• customer behavior
• rep performance
• due-date risk

Main screens:

• Quote Intelligence
• Win / Loss
• Job Pipeline
• Promise Date Accuracy

Production Manager

Use reporting to manage:

• queue risk
• operator overrun
• waste
• setup drift
• due-order risk

Main screens:

• Constraint Dashboard
• Efficiency reports
• Promise Date Accuracy
• daily requirements and due-order views

Purchasing

Use reporting to manage:

• PO status
• received value
• supplier performance
• price changes

Main screens:

• Purchase Order reports
• Vendor Scorecards
• Vendor Pricing Changes

Finance

Use reporting to manage:

• A/R
• revenue
• payment allocation
• profitability
• overhead recovery

Main screens:

• Accounting Reports
• Invoice Reports
• Financials dashboard

Data Hygiene Checklist For Reliable Reporting

If the shop wants trustworthy data, confirm these are true:

• quote outcomes are captured
• loss reasons are not left blank on lost work
• quote statuses are not left stale
• customer defaults are maintained on the customer record
• sales reps and CSRs are assigned correctly
• tax settings are correct
• approved quotes become orders instead of being recreated manually
• tasks are clocked in and out accurately
• quantity actuals are recorded honestly
• waste and NCR events are captured when they matter
• POs are received in the system
• invoices are created from the real work
• payments are allocated to the correct invoice

Common Reporting Mistakes

Avoid these:

• judging a rep, customer, or product type from too little history
• reading a low-confidence quote score as a final answer
• using revenue without also checking profitability
• using close rate without checking margin quality
• using overdue counts without checking dollar exposure
• saving builder reports with unclear names and no owner
• exporting to spreadsheets and then trusting the spreadsheet more than the MIS

Daily, Weekly, And Monthly Cadence

Daily

Review:

• dashboard summary cards
• due and at-risk work
• promise-date risk
• invoice and ready-to-bill queue

Weekly

Review:

• win / loss
• quote intelligence patterns
• waste and operator efficiency
• vendor issues
• open A/R exceptions

Monthly

Review:

• revenue
• profitability
• overhead recovery
• customer and product-type close-rate trends
• estimator coaching needs
• vendor performance drift

Final Rule

The value of reporting is not that the system can calculate a number.

The value is that the team uses that number to:

• change priorities
• correct setup
• coach behavior
• improve pricing
• protect margin
• improve delivery performance

If the data never changes behavior, the reporting stack is unfinished even if every chart loads.