Contract Logistics Solution Design Engine

From RFQ chaos to governed logistics decisions.

Consodes

Transform incomplete RFQ data into structured assumptions, cost-driver scenarios, benchmark comparisons and proposal-ready outputs — with every assumption visible and every risk flagged.

The Problem

Tender teams price million-dollar operations on incomplete data.

RFQs arrive incomplete

Missing SKU dimensions, undefined peak factors, unclear temperature bands. Teams either chase clarifications for weeks or fill the gaps with silent guesses.

Assumptions live in spreadsheets

Cost models are built by one engineer, in one file, with assumptions no one else can see or challenge. Pricing consistency across tenders suffers.

Margins leak quietly

Underestimated returns labour, cold-chain zoning and VAS productivity surface only after go-live — as losses, not as line items in the proposal.

How Consodes Works

A governed path from raw RFQ to proposal-ready output.

1 — Intake & data-gap analysis

Upload RFQ documents, volume files and SLA material. Consodes scores completeness, flags critical gaps and generates customer-ready clarification questions.

2 — Structured assumption building

Every operational assumption carries a source (RFQ, benchmark or manual), a confidence score and a risk flag — reviewable and editable by the whole team.

3 — Activity hours, resources & cost drivers

Volumes convert into activity hours, headcount, equipment and a full monthly cost model across labour, warehouse, IT, packaging and other cost lines.

4 — Scenarios, benchmarks & proposal output

Compare pricing scenarios with visible margins and risk, validate against historical projects, and export proposal-ready content in minutes.

Product Modules

Everything a solution design team needs, in one workflow.

RFQ Intake

Structured upload of RFQ documents, volume files, SKU masters, SLAs and benchmark files with an intake checklist.

Data-Gap Report

Completeness scoring, pricing-impact analysis and auto-generated clarification questions per data area.

Assumption Builder

Storage, inbound, outbound, returns and VAS assumptions with source, confidence and risk governance.

Cost Model

Activity hours, resource requirements and full monthly operating cost across five cost families.

Pricing Scenarios

Competitive, balanced and service-protection scenarios with visible cost drivers — no black-box price.

Benchmarks & Proposal Output

Similarity-scored historical comparisons plus one-click export of proposal sections, cost models and clarification questions.

Use Cases

Built for the people who own the tender.

Head of Solution Design

Standardise how the team converts RFQs into operational models. One assumption library, one methodology.

Tender / Bid Manager

Cut first-draft response time and keep every open clarification visible until it is closed.

Commercial Director

Approve pricing with full sight of margin, risk markers and the assumptions behind every number.

Contract Logistics Director

Protect the P&L from assumptions that were never validated before contract signature.

Logistics Engineering Manager

Turn productivity benchmarks into governed, reusable calculation inputs across every tender.

Operations Excellence Manager

Feed post-go-live lessons learned back into the benchmark library so the next bid starts smarter.

Pricing

Simple plans that scale with your tender pipeline.

Starter
USD 500/month
  • 3 users
  • 5 active RFQs
  • Data-gap analysis
  • Assumption builder
  • Standard cost model
Professional
USD 2,000/month
  • 15 users
  • Unlimited RFQs
  • Pricing scenarios & benchmarks
  • Proposal output exports
  • Approval workflow
Enterprise
Custom
  • Unlimited users & workspaces
  • Private assumption libraries
  • SSO / SAML & audit logs
  • API & ERP integrations
  • Dedicated onboarding
Security

Enterprise-grade controls for commercially sensitive data.

Data isolation

Workspace-level isolation for tender data, with role-based access across solution design, commercial and operations teams.

Governance & audit

Every assumption edit, margin change and approval is logged. Decisions stay traceable long after the tender closes.

Retention control

Configurable data retention aligned to customer NDAs and internal information-security policy.

See your next RFQ in Consodes.

Book a 30-minute demo with a real tender from your pipeline.

From RFQ chaos to governed logistics decisions.

← Back to consodes.com

Demo Workspace / Dashboard
RFQ-2026-014 open IU

Dashboard

RFQ pipeline and tender workload across the workspace.

Active RFQs
12
4 due within 14 days
Awaiting data clarification
5
31 open questions
Avg. first-draft response
2.8 days
was 9.5 days pre-Consodes
Pricing leakage avoided
$184,000
estimated, trailing 12 months
Proposal readiness
76%
Active RFQs
RFQ NameCustomerSectorRegionStatusRisk LevelProposal ReadinessDue DateOwner
Pharma Cold Chain Distribution RFQGlobal PharmaCoHealthcareUAE Assumption ReviewHigh
68%
 12 Jul 2026Idil
E-commerce Fulfilment TenderRetailMaxE-commerceUK Cost ScenarioMedium
81%
 18 Jul 2026James
Spare Parts Warehouse RFQAutoMotionAutomotiveGermany Data Gap ReviewHigh
55%
 09 Jul 2026Sarah

RFQ Intake

Upload RFQ material for Pharma Cold Chain Distribution RFQ — Global PharmaCo.

PDF
Customer RFQ document
GlobalPharmaCo_RFQ_v3.pdf · 4.2 MB
Uploaded
XLS
Excel volume file
Monthly_Volumes_2025.xlsx · 812 KB
Uploaded
XLS
SKU master
Drop file or browse
Missing
CSV
Order profile
Order_Profile_H1.csv · 1.1 MB
Uploaded
PDF
SLA document
Service_Level_Agreement_draft.pdf
Uploaded
DOC
Storage requirements
Partial — temperature bands unclear
Incomplete
XLS
Value-added services file
VAS_Requirements.xlsx · 240 KB
Uploaded
XLS
Historical benchmark file
Linked from workspace benchmark library
Linked
RFQ Intake Checklist
  • Customer profile uploaded
  • SKU master missing
  • SLA document uploaded
  • Temperature requirements unclear
  • Peak volumes missing
  • Returns process not defined

Data-Gap Report

Completeness and pricing-impact analysis for RFQ-2026-014.

Data completeness score
64%
Critical missing fields
7
blocking pricing confidence
Medium-risk assumptions
11
benchmark-substituted
Clarification questions
18
generated, ready to export
Pricing confidence
Low
resolve critical gaps to raise
Detected issues by data area
Data AreaIssue DetectedImpact on PricingRisk LevelSuggested Clarification QuestionOwner
SKU profileSKU dimensions missingImpacts storage and picking assumptionsHigh“Please provide SKU dimensions, weight and carton configuration.”Idil
Peak volumeNo seasonal peak factor providedImpacts labour and space calculationHigh“Please confirm monthly peak factor and expected campaign periods.”Idil
Temperature controlCold chain requirement unclearImpacts facility zoning and compliance costHigh“Please confirm storage temperature bands and monitoring requirements.”Sarah
ReturnsReturns process not definedImpacts labour, space and quality workflowMedium“Please confirm monthly return volume and inspection requirements.”James
VASLabelling and kitting requirements incompleteImpacts labour productivity and equipmentMedium“Please provide expected VAS order volume and process steps.”James

Assumption Builder

Every assumption carries a source, a confidence score and a risk flag. Confidence bands: High 85%+ Medium 70–84% Low <70%

A · Storage Assumptions 5 assumptions
AssumptionValueSourceConfidenceRisk
Average inventory18,000 palletsRFQ
92%
Low
Cold chain pallets1,500 palletsManual
62%
High
Ambient pallets16,500 palletsRFQ
90%
Low
Warehouse utilisation88%Benchmark
78%
Medium
Peak factor1.35Benchmark
66%
High
B · Inbound Assumptions 3 assumptions
AssumptionValueSourceConfidenceRisk
Inbound pallets / month2,500RFQ
91%
Low
Receiving productivity18 pallets/hourBenchmark
82%
Medium
Putaway productivity22 pallets/hourBenchmark
81%
Medium
C · Outbound Assumptions 4 assumptions
AssumptionValueSourceConfidenceRisk
Orders / month42,000RFQ
93%
Low
Order lines / month118,000RFQ
89%
Low
Picking productivity125 lines/hourBenchmark
80%
Medium
Packing productivity55 orders/hourBenchmark
79%
Medium
D · Returns & VAS 4 assumptions
AssumptionValueSourceConfidenceRisk
Returns / month4,500Manual
64%
High
VAS orders / month3,000RFQ
76%
Medium
Returns productivity22 returns/hourBenchmark
74%
Medium
VAS productivity45 orders/hourBenchmark
68%
High

Activity Hours & Resource Calculation

How monthly operational volumes convert into required hours and resources.

Activity hours
ActivityCalculationHours / month
Receiving2,500 ÷ 18139 h
Putaway2,500 ÷ 22114 h
Picking118,000 ÷ 125944 h
Packing42,000 ÷ 55764 h
Returns4,500 ÷ 22205 h
VAS3,000 ÷ 4567 h
Replenishmentderived158 h
Total operational hours2,391 h
Resource requirement
ResourceRequiredResourceRequired
Warehouse Operators15Forklifts6
Team Leaders2Reach Trucks4
Shift Manager1RF Devices22
Warehouse Manager1Packing Stations12
Key Operational Risks
  • Peak factor based on benchmark, not customer-confirmed data
  • Returns workflow requires clarification
  • Cold chain monitoring cost excluded from customer data
  • VAS productivity assumption requires validation
Risks are carried forward into the proposal as commercial risk markers.

Cost Calculation

Monthly operating cost model for RFQ-2026-014, built from governed assumptions.

A · Labour Cost
OperatorsUSD 32,000
Team LeadersUSD 7,000
Shift ManagerUSD 3,500
Warehouse ManagerUSD 5,000
Social CostsUSD 9,200
Total LabourUSD 56,700
B · Warehouse Cost
Warehouse RentUSD 38,000
UtilitiesUSD 7,500
MHE LeaseUSD 9,500
MaintenanceUSD 3,000
CleaningUSD 2,400
SecurityUSD 2,600
Total WarehouseUSD 63,000
C · IT Cost
WMSUSD 6,500
ERP IntegrationUSD 1,200
RF LicencesUSD 1,000
Power BIUSD 400
SupportUSD 1,800
Total ITUSD 10,900
D · Packaging Cost
CartonsUSD 7,500
Stretch FilmUSD 1,800
LabelsUSD 1,100
PalletsUSD 2,200
ConsumablesUSD 900
Total PackagingUSD 13,500
E · Other Costs
Quality OperationsUSD 2,800
ComplianceUSD 1,700
InsuranceUSD 1,300
AdministrationUSD 4,500
Total OtherUSD 10,300
LabourUSD 56,700
WarehouseUSD 63,000
ITUSD 10,900
PackagingUSD 13,500
OtherUSD 10,300
Total Monthly Operating CostUSD 154,400

Pricing Scenarios

Scenario-based commercial decision support with visible assumptions and risk markers.

Scenario 1

Competitive

High risk
Operating CostUSD 154,400
Overhead AllocationUSD 15,000
Target Margin12%
Suggested RevenueUSD 192,500
Gross ProfitUSD 23,100
Use caseAggressive tender strategy
Recommended Scenario
Scenario 2

Balanced

Medium risk
Operating CostUSD 154,400
Overhead AllocationUSD 18,000
Target Margin18%
Suggested RevenueUSD 210,244
Gross ProfitUSD 37,844
Use caseRecommended commercial case
Scenario 3

Service Protection

Low risk
Operating CostUSD 154,400
Overhead AllocationUSD 22,000
Target Margin24%
Suggested RevenueUSD 232,105
Gross ProfitUSD 55,705
Use caseComplex operations with high service risk
Consodes does not generate a black-box final price. It provides scenario-based commercial decision support with visible assumptions, cost drivers and risk markers.

Benchmark Comparison

Current RFQ compared with the closest historical projects in the workspace library.

Benchmark A

Pharma 3PL Distribution

Turkey
82% similarity
SectorHealthcare
Average inventory20,000 pallets
Monthly orders39,000
Cost per orderUSD 3.65
Labour productivity118 lines/h
Margin achieved17%
Lesson learned: cold chain zoning underestimated at design stage.
Benchmark B

Healthcare Fulfilment

UAE
76% similarity
SectorHealthcare
Average inventory14,500 pallets
Monthly orders46,000
Cost per orderUSD 3.10
Labour productivity131 lines/h
Margin achieved21%
Lesson learned: returns inspection labour was underestimated.
Benchmark C

FMCG Value-Added Services

Germany
69% similarity
SectorFMCG
Average inventory12,000 pallets
Monthly orders55,000
Cost per orderUSD 2.85
Labour productivity142 lines/h
Margin achieved14%
Lesson learned: VAS productivity varied significantly by SKU family.

Proposal Output

Select sections to export for RFQ-2026-014 — Pharma Cold Chain Distribution.

Sections to export
Preview · Executive Summary

Pharmaceutical Cold Chain Distribution — Solution Proposal

We are pleased to present our proposed contract logistics solution for Global PharmaCo's regional distribution operation. Our design covers an average inventory of 18,000 pallets, including 1,500 cold chain pallet positions, supporting approximately 42,000 orders and 118,000 order lines per month.

Operational model

The operation is designed around dedicated ambient and temperature-controlled zones, staffed by 15 warehouse operators, 2 team leaders and a dedicated management structure, supported by 6 forklifts, 4 reach trucks and 12 packing stations. Total operational workload is calculated at 2,391 hours per month.

Commercial summary

Our recommended commercial case is based on a balanced pricing scenario with a monthly operating cost of USD 154,400 and a suggested monthly revenue of USD 210,244. All assumptions, sources and confidence levels are documented in the appendix.

Open clarifications

This proposal is submitted subject to 18 open clarification questions, including SKU dimensional data, seasonal peak factors and cold chain monitoring requirements. Commercial terms will be finalised on receipt of the outstanding information.

Admin & Settings

Workspace configuration for assumptions, benchmarks, pricing rules and governance.

Assumption library

Reusable operational assumptions applied as defaults to new RFQs.

storage · 24inbound · 12outbound · 18returns · 9

Productivity benchmarks

Sector- and region-specific productivity rates used for calculations.

healthcaree-commerceautomotiveFMCG

Cost categories

Cost families and line items available in the cost model.

labourwarehouseITpackagingother

Pricing margin rules

Minimum margins and approval thresholds per sector and risk level.

min margin 12%approval > USD 150k

Proposal templates

Branded Word and PDF templates for proposal exports.

standard.docxhealthcare.docx

User roles

Role-based access for solution design, commercial and admin users.

admin · 2designer · 6commercial · 4viewer · 9

Approval workflow

Who reviews and approves pricing before proposals are exported.

designer → commercial director

Data retention settings

Retention periods for RFQ data, aligned to customer NDAs.

RFQ data · 36 monthsbenchmarks · indefinite