Share via

Best approach to normalize supplier quotations in construction projects before automation?

Venu Madhav 20 Reputation points
2026-05-05T10:28:55.3633333+00:00

I’m working on a procurement/process improvement project for a construction business where we receive quotations from multiple suppliers for items such as windows, joinery, marble, lifts, roofing, bathroom fittings, etc.

The challenge is:

Every supplier sends quotations in different formats (PDF, Word, Excel)

Some suppliers bundle multiple items together (example: shelf + drawer as one line item), while others quote separately

Some include exclusions (installation, drawings, delivery, fittings), while others include them

Quantities, dimensions, and specifications may vary across suppliers

Employees are currently manually normalizing and comparing this data in Excel, which is time-consuming and inconsistent

Before jumping into OCR or automation, we’re trying to define:

What parameters should be normalized first?

(Example: scope coverage, bundles, exclusions, quantity mismatches, pricing anomalies, delivery timelines, etc.)

What KPIs do procurement/construction teams typically use before comparing price?

Are companies using Excel-based frameworks, custom apps, or procurement platforms for this type of supplier comparison?

Has anyone successfully used AI tools like Claude, ChatGPT, OCR, or document intelligence to extract and normalize supplier quotations?

I’m looking for practical advice from professionals who have implemented similar supplier quote normalization or bid comparison systems in construction environments.

Microsoft 365 and Office | Excel | For business | Other
0 comments

Answer accepted by question author

Michelle-N 16,635 Reputation points Microsoft External Staff Moderator
2026-05-05T12:05:41.5433333+00:00

Hi @Venu Madhav

Based on the information provided, I understand that you are struggling with inconsistent quotation formats, bundled line items, and varying exclusions, leading to time-consuming manual normalization in Excel.

  1. Parameters to Normalize (Priority Order)

Before automation, you need a "Bid Leveling" structure. I recommend normalizing in this order:

  • Scope Coverage (The "Plug" Method): Compare each quote against your project’s BOQ (Bill of Quantities). Explicitly list what is "Included" and "Excluded." For exclusions, add a "Plug Number" (based on internal estimates or market rates) to create an Adjusted Price. Treat surplus items (not in the original spec) as separate add-ons.
  • Line Item Unbundling: Force suppliers to quote according to your BOQ detail level via an RFQ Template. If they bundle items (e.g., shelf + drawer), require a breakdown or manually split them based on a standardized line-item list.
  • Quantities, Units, and Specs: Convert all measurements to the project’s standard units. Highlight any quantity mismatches or deviations from the technical specifications.
  • Price Structure & Anomalies: Separate unit rates, lump sums, overhead & profit (OH&P), and mark-ups. Flag "low-ball" or excessively high prices for clarification. Ensure currency and price validity periods are aligned.
  • Commercial Terms & Lead Times: Normalize payment terms, delivery timelines, and warranties. Calculate the Total Cost of Ownership (TCO), including early payment discounts or potential delay liquidated damages.

Note: Always maintain a "Normalization Log" to track every adjustment made to a supplier's original figure.

  1. KPIs Before Price Comparison

Leading procurement teams use a Weighted Scorecard where non-price criteria often account for 30–50% of the total score. Common KPIs include:

-Technical Compliance

-Supplier Capability

-Schedule Reliability

-Risk & Quality

  1. Current Tools & Industry Trends

Most companies use advanced Bid Tabulation or Bid Leveling templates. The key is sending these exact Excel templates as part of the RFQ, forcing suppliers to provide data in your required structure.

  1. Practical Implementation Advice

According to professionals who have successfully implemented these systems, the most effective roadmap is:

 Fix the RFQ First > Standardize the Playbook > Perform a Technical Review before the Commercial Review > Automation Strategy: Only automate the data entry into your proven manual framework. Use tool to "read" the PDF, but use your logic to "compare" the values.

I hope these insights from the field help you structure your project.

If the answer is helpful, please click "Accept Answer" and kindly upvote it. If you have extra questions about this answer, please click "Comment".

Note: Please follow the steps in our documentation to enable e-mail notifications if you want to receive the related email notification for this thread.

Was this answer helpful?

1 person found this answer helpful.

1 additional answer

Sort by: Most helpful
Most helpful Newest Oldest

  1. AI answer

    2026-05-05T10:29:20+00:00

    For construction procurement, a practical approach is to combine a structured Excel-based comparison model with Microsoft 365/Power Platform automation and document intelligence.

    1. Parameters to normalize first

    Normalize the information that directly affects scope, risk, and comparability before looking at unit price:

    1. Scope and coverage
    • Map each quote line to a standard internal “item” or work package (for example, Window Type A, Bathroom Pack B, Lift Package C).
    • Normalize whether the quote covers:
      • Supply only vs supply + install
      • Design/drawings/shop drawings
      • Delivery (to site, to floor, offloading, crane, etc.)
      • Testing/commissioning (for lifts, MEP, etc.)
    • Use a consistent yes/no/partial flag for each scope element per supplier.
    1. Bundles vs separate items
    • Define a standard breakdown for each package (for example, shelf, drawer, runners, handles as separate cost buckets).
    • In Excel, create columns for each component and allocate the supplier’s bundled price across them (even if proportionally) so all suppliers can be compared at the same component level.
    1. Exclusions and assumptions
    • Create a structured exclusions/assumptions matrix:
      • Installation
      • Drawings
      • Delivery
      • Fixings/fittings
      • Waste removal
      • Warranty/maintenance
    • Normalize these into standard columns (Included/Excluded/Clarify) instead of leaving them as free text.
    1. Quantities, dimensions, and specification alignment
    • Define a “tender baseline” for each item:
      • Standard quantity (e.g., 120 m² marble, 40 windows)
      • Standard dimensions/spec (e.g., 1.2m x 1.5m, U-value, finish, brand/grade)
    • For each supplier, add:
      • Quoted quantity vs baseline quantity
      • Quoted spec vs baseline spec (Match / Better / Worse / Different)
    • Add a normalized “adjusted price to baseline quantity” column so all quotes are on the same quantity basis.
    1. Commercial terms Normalize key commercial parameters:
    • Lead time / delivery timeline
    • Payment terms (advance %, retention, days to pay)
    • Warranty period and coverage
    • Validity of offer
    1. Price structure
    • Normalize into:
      • Base material price
      • Labor/installation
      • Delivery/logistics
      • Design/engineering
      • Contingencies/allowances
    • Then calculate:
      • Unit price per baseline unit (m², item, set)
      • Total price per package
      • Adjusted price after scope/quantity normalization
    1. KPIs before comparing price

    Before focusing on “lowest price,” procurement and construction teams typically look at:

    • Scope completeness index
      • % of required scope covered (by value or by items).
      • Number of exclusions/clarifications.
    • Compliance to specification
      • % of items fully compliant with baseline spec.
      • Count of deviations requiring design/consultant approval.
    • Risk and reliability
      • Lead time vs project program (on-time/at risk/late).
      • Warranty length and service coverage.
    • Commercial quality
      • Payment terms score (more favorable terms score higher).
      • Price stability (validity period, escalation clauses).
    • Total cost of ownership view
      • Not just initial price, but expected maintenance, replacement, and energy performance (for lifts, windows, roofing, etc.).

    These KPIs can be tracked in a source-to-pay process using Dynamics 365 and supplier evaluation capabilities, where supplier performance and compliance are evaluated using consistent criteria and KPIs to reduce risk and improve efficiency.

    1. Tools typically used (Excel vs apps vs platforms)

    In practice, organizations use a mix:

    • Excel-based frameworks
      • Common for early-stage or smaller organizations.
      • Structured comparison templates with standardized columns for scope, exclusions, quantities, and KPIs.
      • Often combined with manual data entry from PDFs/Word/Excel quotes.
    • Procurement/ERP platforms
      • Dynamics 365 Supply Chain Management and Finance support:
        • Source-to-pay processes including RFQs, purchase orders, and vendor performance tracking.
        • Supplier selection and portal capabilities so suppliers can respond to RFQs in a more structured way instead of ad-hoc documents.
      • The Supplier Engagement app and supplier portal can help standardize how suppliers submit bids, reducing format variation and improving collaboration across RFQs, purchase orders, and invoicing.
    • Custom apps on Power Platform
      • Power Apps for structured quote capture and internal comparison forms.
      • Dataverse or SharePoint as a central repository for quote data.
      • Power Automate to orchestrate ingestion, validation, and routing of quotes.
    1. Using AI/OCR/document intelligence to extract and normalize quotes

    AI and document processing are effective once a clear data model and comparison template exist.

    • Document processing and extraction
      • Microsoft 365 document processing scenarios show how attachments from incoming emails can be extracted to SharePoint and processed with models that identify and extract key fields (names, dates, amounts, etc.).
      • Similar models can be trained to extract:
        • Supplier name, quote date, validity
        • Item descriptions, quantities, unit prices
        • Exclusions and inclusions
    • Invoice/quote-like processing patterns
      • Power Automate and AI Builder can be used to ingest structured documents (like invoices) into Dynamics 365, applying validation and normalization before creating records.
      • The same pattern can be adapted for quotations: monitor mailboxes, extract attachments, parse and normalize data, validate against expected schema, and store in a central system for comparison.
    • Benefits once implemented
      • Reduced manual data entry and fewer errors.
      • Consistent application of business rules and local requirements.
      • Better audit trail and long-term retention of original documents alongside structured data.
    1. Practical implementation path
    1. Define a standard comparison template in Excel
    • Create a master comparison sheet with:
      • Standard item list/work packages.
      • Columns for scope coverage, exclusions, quantities, specs, commercial terms, and normalized prices.
      • KPI calculations (scope completeness, spec compliance, risk scores).
    1. Use this template as the “data model” for any automation
    • Map all AI/OCR extraction outputs into this structure.
    • Ensure every supplier quote can be transformed into this normalized format.
    1. Gradually introduce automation
    • Start with:
      • Email → SharePoint/OneDrive storage of quotes.
      • Document processing models to extract key fields into SharePoint lists or Dataverse.
    • Then:
      • Power Automate flows to populate the comparison template or a Power App.
    1. Evolve toward structured RFQs
    • Use Dynamics 365 RFQ and supplier portal capabilities so suppliers respond in a structured way, reducing the variability of formats and simplifying normalization.

    This combination—clear normalization rules, KPI-driven comparison, an Excel-based baseline model, and gradual adoption of Power Platform and Dynamics 365 capabilities—provides a practical and scalable approach for construction quote normalization and bid comparison.

    References:

    AI-generated content may be incorrect. Read our transparency notes for more information.

    Was this answer helpful?

Your answer

Answers can be marked as 'Accepted' by the question author and 'Recommended' by moderators, which helps users know the answer solved the author's problem.