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Executive Summary

Our Understanding of Sisu Auto's Situation

Sisu Auto occupies a distinctive position in the Finnish and European industrial landscape: a focused developer and manufacturer of heavy trucks and tactical military vehicles, operating in demanding conditions that reward durability, precision, and configurability. The defence and heavy-truck markets are experiencing a period of sustained order-driven growth — and with that growth comes a material operational question that Sisu Auto's leadership will need to answer: can the execution foundations that delivered success at current scale continue to deliver predictable capacity, consistent quality, and reliable supply-chain coordination as complexity increases?

Our analysis suggests three interconnected pressure points that merit structured discovery:

• Operating-model visibility: As order volume and headcount grow, the coordination fabric across production, quality, engineering, and supply-chain functions faces increasing strain. Without a verifiable, cross-team view of accountability and capacity versus demand, execution risk can outpace the business's ability to absorb it.
• Industrial AI and data governance readiness: Over the next 12–24 months, industrial AI is expected to shift from isolated automation toward governed, adaptive operations — increasing the value of interoperable production, engineering, and supply data. Fragmented pilots without governance create operational debt rather than competitive advantage.
• Lifecycle data and product-passport readiness: Emerging Digital Product Passport requirements and energy-productivity expectations create a 24–36 month planning horizon in which component provenance, repair history, and durability data may become infrastructure for tenders, maintenance, refurbishment, and parts recovery.

Our Value Proposition

Sujuva brings a proven, anti-prescriptive engagement model that begins with co-design, not imposition. We do not arrive with a pre-packaged methodology and force-fit it to your context. Instead, we establish a Shared Operating Model with your teams — a mutually agreed governance structure that makes our engagement adaptive, measurable, and commercially accountable from day one.

Our track record in heavy industry, manufacturing, and supply-chain environments is directly relevant. In comparable engagements, we have improved delivery reliability from 20% to 99.95%, increased operational capacity by over 70%, reduced lead times by more than 50%, and generated savings of up to €12,000,000 in a single project — not through tool deployment, but through systematic operating-model redesign, governed data use cases, and leadership capability building.

Why Sujuva Is the Right Partner

• Industry depth: Our lead consultant brings 25 years of experience across heavy industry, manufacturing, supply chain, and automotive-adjacent environments, including IATF 16949 certification and MES/APS competency directly relevant to Sisu Auto's production context.
• Co-design principle: We will not finalize engagement scope before completing a structured Discovery phase with your operational and technology leaders — ensuring every investment is grounded in validated need.
• Measurable outcomes: Every phase of this engagement is governed by observable progress indicators — accountability documentation, flow metrics, data ownership baselines — not activity reports.
• Structural partnership: Our Shared Operating Model approach creates a collaborative governance structure that reduces change risk, accelerates learning, and builds internal capability rather than dependency.

Proposed Approach

Our engagement methodology is built on a single governing principle: the right intervention can only be designed after the real condition is understood. Every phase below is therefore structured to validate hypotheses before committing resources to solutions. The approach is explicitly anti-prescriptive — scope is co-defined with Sisu Auto's leadership before any delivery phase begins.

The approach addresses all three buying hypotheses identified in the account intelligence:

• SIG-01: Operating-model visibility during rapid growth
• SIG-02: Industrial AI and data governance readiness
• SIG-03: Lifecycle data and product-passport readiness

1. Agreement of Service Delivery

Before any engagement activity begins, Sujuva and Sisu Auto co-sign a clear Agreement of Service Delivery that defines the mutual commitments, governance principles, decision rights, and the framework for the Shared Operating Model. This agreement is not a fixed-scope contract — it is a living engagement charter that establishes how scope, priorities, and resources will be governed collaboratively throughout the engagement.

Key outputs of this step:

• Signed engagement charter with mutual commitments
• Preliminary stakeholder map confirming active roles for COO, R&D Director, Supply Chain Director, After Sales Director, and CIO/CTO equivalent
• Agreed discovery objectives and hypothesis validation criteria
• Confirmation of executive sponsorship (CEO/Managing Director level) as a prerequisite for proceeding

2. On-Boarding Phase

The On-Boarding Phase ensures that Sujuva's team is fully integrated into Sisu Auto's context before any diagnostic or design work begins. This phase covers knowledge transfer, team integration, and environment setup.

Activities include:

• Structured briefings with each identified stakeholder group (Operations, R&D, Supply Chain, After Sales, Technology)
• Review of available internal documentation: delivery practices, capacity tracking mechanisms, quality reporting, supplier coordination processes
• Baseline environment setup: collaboration tools (Miro/Confluence/JIRA or equivalent), communication cadences, and access to relevant operational data sources
• Introduction of the Sujuva team to Sisu Auto's production and engineering environment, including a structured Gemba Walk across key operational areas
• Confirmation of data access conditions: which operational, quality, and supply-chain data can be accessed for diagnostic purposes

3. Shared Operating Model

Following On-Boarding, Sujuva and Sisu Auto co-design and activate a Shared Operating Model that governs how the engagement runs day-to-day. This is not an internal Sujuva process — it is a mutually agreed structure that both organisations operate within for the duration of the engagement.

The Shared Operating Model includes:

• Ceremonies and cadences: Weekly delivery syncs, bi-weekly steering reviews, monthly executive checkpoint, and retrospective loops at each phase boundary
• Communication standards: Single escalation path per workstream, defined response SLAs, and a shared decision log maintained in the collaboration environment
• Decision-making model: Clear RACI/DACI for each engagement workstream, with financial decision authority mapped to the agreed governance board structure (Change Program Board → Program Board → Team-level Kanban)
• Continuous improvement loops: Each phase concludes with a structured retrospective that feeds directly into the next phase's design — making the engagement itself adaptive
• Change management within the model: All scope adjustments, resource changes, and priority shifts are handled through the Shared Operating Model's governance and prioritisation structures — not as separate contract amendments
• Progress visibility: A shared Flow Dashboard tracking engagement-level metrics (milestone completion, hypothesis validation status, decision velocity, and stakeholder engagement health)

4. Discovery & Hypothesis Validation Phase (SIG-01 Primary)

This phase directly addresses the operating-model visibility hypothesis. Before any solution design begins, we validate whether the hypothesised coordination and capacity gaps are real, owned, and commercially consequential.

Service applied: Delivery & Capacity Planning Optimization (baseline diagnostic component) + Business Architecture Analysis & Advisor

Activities include:

• Structured discovery interviews with COO, R&D Director, Supply Chain Director, and relevant operational leaders
• Flow Metrics Baseline: quantitative analysis of current delivery capability including cycle time, lead time, throughput, and WIP across key production and delivery streams
• Value Stream Mapping of critical production-to-delivery and order-to-delivery paths
• Capacity-versus-demand visibility assessment: how is demand tracked, forecast, and compared against available capacity today?
• Quality and supply-chain coordination review: which outcomes are reviewed consistently, by whom, and through what mechanisms?
• Stakeholder accountability mapping: documentation of current team responsibilities, escalation paths, and cross-functional coordination mechanisms

Phase outputs:

• Flow Metrics Baseline Report
• Capacity-versus-Demand Visibility Assessment
• Accountability & Coordination Gap Analysis
• Validated or falsified SIG-01 hypothesis with recommended next steps

5. Operating Model Design & Pilot Phase (SIG-01 Resolution)

If SIG-01 is validated, this phase designs and pilots the operating model improvements needed to restore coordination visibility and delivery predictability.

Services applied: Creating Shared Operating Model + Delivery & Capacity Planning Optimization + Organizational Architecture Optimization

Activities include:

• Co-design of a future-state operating model for production, quality, and supply-chain coordination — built with Sisu Auto's teams, not imposed on them
• Design of a lightweight, data-driven Capacity Planning & Forecasting Model using probabilistic forecasting rather than deterministic estimation
• Flow Dashboard configuration: real-time tracking of Cycle Time, Lead Time, Flow Efficiency, Planning Accuracy, First-Time-Right, and WIP
• Pilot implementation in a defined operational area with a Pilot Implementation Kit (competence descriptions, operating rhythm, process maps, KPI breakdown)
• Change Curve preparation: proactive communication of the expected efficiency dip in months 1–2 before the 30–50% gains emerge at months 2–6
• Leadership coaching to support the transition from command-and-control to enabling leadership styles

6. Data Governance & AI Readiness Foundation Phase (SIG-02)

This phase is activated only if SIG-02 is validated in discovery — that is, if industrial AI readiness is confirmed as a real, owned, and commercially consequential priority within the engagement horizon.

Services applied: AI Readiness Assessment + AI-Driven Value Creation Engine + Business Requirement Breakdown Concept

Activities include:

• AI Maturity Scorecard: assessment of data readiness, infrastructure, talent, and governance across production, engineering, and supply-chain functions
• Data ownership mapping: which teams own which data, how is it governed, and where are the interoperability constraints?
• OT/IT integration baseline: identification of the integration landscape relevant to production, quality, and supply-chain data
• Prioritised use-case identification: a small number of production, quality, or supply-chain data use cases with clear business owners and measurable outcomes
• AI Governance Framework: initial guardrails, Human-in-the-Loop escalation matrix, and data privacy boundaries
• Pilot use case design: end-to-end design of one high-impact, business-driven use case ready for measured deployment

7. Lifecycle Data Foundation Phase (SIG-03)

This phase is a proactive, longer-horizon initiative activated only if SIG-03 is validated — that is, if digital-product-passport expectations are confirmed as commercially relevant to Sisu Auto's products, tenders, or lifecycle services within a 24–36 month horizon.

Services applied: Service Discovery + Designing Service-Based Business Architecture + Sustainability Assessment & Development

Activities include:

• Lifecycle data baseline: assessment of how component provenance, repair history, supplier data, and durability records are currently captured, governed, and made auditable
• Service discovery across the After Sales, Supply Chain, and Engineering functions to map lifecycle data flows and ownership gaps
• Structured foundation design for vehicle and component lifecycle data — traceable component and repair records, supplier-data interoperability requirements, and early lifecycle-service offering blueprints
• ESG and Digital Product Passport readiness assessment: which requirements may apply, on what timeline, and what architecture would be needed

8. Capability Building & Knowledge Transfer

Running across all delivery phases, this continuous stream ensures that Sisu Auto's teams build the internal capability to own and sustain the changes introduced — not remain dependent on Sujuva.

Services applied: The Modern Way - Training Program + Communities of Knowledge & Learning + DevOps Coach (where technology enablement is confirmed)

Activities include:

• Tailored training in lean-agile ways of working, flow-based delivery, and data-driven capacity planning
• Establishment of Communities of Practice around key capability areas confirmed in discovery
• Train-the-Trainer guides and internal facilitation capability building
• Knowledge repository setup in the agreed collaboration environment

Team Composition

Sujuva fields a lean, senior team specifically selected for the industrial, manufacturing, and operating-model transformation context of this engagement. Where real consultant profiles from our Experience Database match the required competencies, we deploy those consultants by name. Supporting roles are staffed by experienced practitioners from our extended network.

Team Structure

The engagement is structured around a flat, accountable team topology:

• Jori Eskolin serves as both Engagement Lead and primary subject-matter expert, maintaining direct client relationships at all stakeholder levels — from COO to operational team leads.
• Supporting specialists are activated conditionally based on validated hypotheses — ensuring no unnecessary cost is incurred before a real need is confirmed.
• All team members operate within the Shared Operating Model's governance structure, with clear accountability for their respective workstreams.
• Client-side counterparts for each workstream are identified during On-Boarding and maintained throughout the engagement as the primary co-design partners.

Jori Eskolin — Lead Advisor Profile Highlights

Jori brings a uniquely relevant combination of competencies for the Sisu Auto context:

• Heavy industry and manufacturing depth: Direct experience at Wärtsilä, Canatu, and Swissport Finland across production, quality, and supply-chain transformation
• MES/APS/PDM competency: Direct experience with Manufacturing Execution Systems and Advanced Planning & Scheduling — essential for Sisu Auto's production environment
• IATF 16949 certification: The automotive industry quality standard, directly transferable to Sisu Auto's heavy-vehicle manufacturing context
• Statistical thinking and Six Sigma Black Belt: Enables rigorous, data-driven hypothesis validation rather than consultancy-by-intuition
• AI-Driven Operating Model expertise: Agentic AI Readiness Creation and KPI Breakdown/Data Governance competencies directly relevant to SIG-02
• ISO9000 Lead Auditor: Quality system governance expertise applicable to Sisu Auto's production quality and supplier coordination challenges

Deliverables

All deliverables are co-designed with Sisu Auto's teams during the relevant phase. Acceptance criteria are defined collaboratively at phase initiation, not imposed retrospectively. Deliverables marked (conditional) are activated only upon validation of the corresponding buying hypothesis.

Engagement Timeline

The timeline below reflects a phased, hypothesis-validated engagement. Conditional phases (SIG-02 and SIG-03) are shown with indicative timing but are activated only upon confirmation of the relevant hypothesis during Discovery. The total engagement horizon spans 10–18 months depending on which hypotheses are validated and the pace of Sisu Auto's internal decision-making.

A critical dependency throughout is executive sponsorship confirmation — without an active, committed sponsor at CEO or equivalent level, no phase proceeds.

Pricing

Pricing Model Overview

Sujuva operates on a time-and-materials model with phase-gated investment decisions. This means Sisu Auto commits to one phase at a time, with a formal go/no-go decision point at each phase boundary before the next investment is approved. This structure directly reflects our discovery-first, hypothesis-validated approach — you do not pay for phases that are not warranted by the evidence.

Conditional phases (SIG-02 and SIG-03) are priced as options, not commitments. Their activation requires explicit executive confirmation based on Discovery findings.

All fees are quoted exclusive of VAT. Travel and accommodation expenses for on-site work in Finland are billed at cost with a 5% administration fee, capped at an agreed monthly ceiling confirmed during On-Boarding.

Cost Breakdown by Phase

Investment Summary

Payment Schedule

Value Justification

The investment framing for this engagement is grounded in the cost of inaction, not the cost of our fees:

• In a comparable manufacturing transformation engagement, a single day of maintenance shutdown delay cost €3,000,000. Our operating model and capacity planning improvements eliminated that risk entirely.
• In an aviation/ground services engagement of comparable scale, delivery reliability improved from 20% to 99.95% and capacity increased by over 70% — changes that made the business operationally viable.
• A construction-sector client reduced lead time and WIP by over 50% and improved delivery reliability from 20% to 90% — directly translating to contract win rate and customer satisfaction.

Quality Assurance

QA Philosophy

Quality in this engagement is not a separate gate or audit process — it is embedded in every phase through the Shared Operating Model's continuous improvement loops, observable progress indicators, and stakeholder acceptance criteria. We apply the same lean-agile quality principles to our engagement delivery that we help clients apply to their operations.

All quality commitments are measured against agreed baselines established during On-Boarding. No KPI is declared without a defined measurement method and a named owner on both the Sujuva and Sisu Auto sides.

SLA & KPI Matrix

Reporting Cadence

• Weekly Delivery Sync: 60-minute working session with operational counterparts covering progress, blockers, decisions needed, and next-week priorities. Documented in shared meeting log.
• Bi-Weekly Steering Review: 90-minute session with COO and relevant workstream leads covering phase progress, milestone status, hypothesis validation status, and emerging risks. Includes Flow Dashboard review.
• Monthly Executive Checkpoint: 60-minute session with executive sponsor (CEO/MD) covering phase-level progress, investment decisions for upcoming phases, strategic alignment, and any escalated issues.
• Phase Retrospective: Structured retrospective at each phase boundary covering what went well, what to improve, and what to change in the next phase. Outputs feed directly into the next phase design through the Shared Operating Model's continuous improvement loop.
• Quarterly Written Report: Formal written summary of engagement progress, deliverable acceptance status, KPI performance, and recommended adjustments. Distributed to all named stakeholders.

Escalation Procedure

• Level 1 (Operational): Issues raised at Weekly Delivery Sync. Resolved between Sujuva Engagement Lead and Sisu Auto operational counterpart within 5 business days.
• Level 2 (Programme): Unresolved Level 1 issues or issues with cross-workstream impact escalated to Bi-Weekly Steering Review. COO or equivalent makes resolution decision.
• Level 3 (Executive): Unresolved Level 2 issues, hypothesis-level disagreements, or investment decision blocks escalated to Monthly Executive Checkpoint. Executive sponsor holds final resolution authority.
• Critical path risk: Any issue assessed as threatening the engagement's critical path is escalated directly to Level 3 within 48 hours, regardless of cycle timing.

Deliverable Quality Process

• Every deliverable is internally reviewed by the Sujuva Engagement Lead before submission.
• Acceptance criteria for each deliverable are agreed with the relevant Sisu Auto stakeholder at the start of the phase in which the deliverable is produced.
• A maximum of two revision cycles is allowed per deliverable before the issue is escalated to Level 2.
• All accepted deliverables are stored in the shared collaboration environment and remain accessible to Sisu Auto in perpetuity.

Risk Management

Risk management for this engagement operates within the Shared Operating Model's governance structure. Risks are identified, assessed, and owned collaboratively — not held privately by Sujuva. The risk register below reflects the most material risks identified from the account intelligence, both documents analysed, and our experience in comparable industrial engagements. It is a living document reviewed at every Bi-Weekly Steering Review.

Likelihood and Impact are rated on a 1–3 scale: 1 = Low, 2 = Medium, 3 = High.

Relevant Experience

Case Study 1: Aviation & Ground Services — Delivery Reliability from 20% to 99.95% (Real)

Client: Confidential — Aviation/Ground Services Organisation (600+ employees)

Industry: Aviation, Manufacturing-Adjacent Operations

Challenge: The organisation faced critically low delivery reliability (20%), unsustainable cost structures, high injury rates, and a fragmented operating model that prevented coordination across production, logistics, and service delivery functions. Leadership operated in a command-and-control mode that suppressed team autonomy and made scaling impossible without proportional headcount growth.

Approach: Jori Eskolin led a full operating model redesign engagement that began with a structured discovery and flow metrics baseline before any solution design commenced. The engagement established a Shared Operating Model with the client's leadership team, introduced value stream-based coordination across all functions, implemented probabilistic capacity planning, and ran a pilot in a defined operational area before organisation-wide rollout. A Leadership Lab component shifted leadership behaviour from directive to enabling. Communities of Practice were established to sustain the capability after the engagement.

Outcomes:

• Delivery reliability improved from 20% to 99.95%
• Operational capacity increased by over 70%
• Cost per item reduced by over 50%
• Workplace injuries reduced by over 95%
• Lead time for key products reduced from 4 hours to 30 minutes
• Organisation remains operationally viable with net profit over 10%

Case Study 2: Construction & Manufacturing Subcontractor — Supply Reliability from 20% to 90% (Real)

Client: Confidential — Construction/Manufacturing Subcontractor

Industry: Construction, Manufacturing

Challenge: The organisation was severely affected by supply reliability below 20% from a key subcontractor, creating cascading delivery failures across the production system. There were no data-driven capacity models, planning was manual and estimation-based, and WIP had grown to unmanageable levels.

Approach: Jori Eskolin developed a Production Planning System with Advanced Planning & Scheduling (APS) and Manufacturing Execution System (MES) components — directly relevant to Sisu Auto's production context. The engagement began with a structured current-state analysis, established a flow metrics baseline, and redesigned the production planning and supplier coordination process before any technology was deployed.

Outcomes:

• Lead time and WIP reduced by more than 50%
• Delivery reliability improved from 20% to 90%
• Cost of Poor Quality (COPQ) reduced by 30%

Case Study 3: IT Consultancy & Data Organisation — Operating Model Transformation (Real)

Client: Confidential — IT Consultancy & Data Organisation

Industry: ICT/Technology, Professional Services

Challenge: The organisation faced declining profitability (3% net profit), low win ratios, fragmented delivery, and a request-to-offer process that took weeks rather than days. Operational silos prevented coordination across delivery, sales, and technical functions. Leadership had not committed to the mindset shift needed for sustainable change.

Approach: A full operating model redesign engagement including value stream mapping, service blueprint design, data-driven capacity planning, and leadership coaching. The engagement established a governed set of prioritised initiatives with measurable outcomes, replacing a fragmented project portfolio with a coherent value stream structure.

Outcomes:

• Net profit increased from 3% to 15%
• Winning ratio increased by 50%
• NPS improved by 35% in the first three months
• Utilisation rate increased from 65% to 90%
• Request-to-offer time reduced by 70%

Case Study 4: Oil & Gas — Maintenance Outage Optimisation — €12,000,000 Savings (Real)

Client: Confidential — Oil & Gas / Consulting Organisation

Industry: Oil & Gas, Industrial Operations

Challenge: A major maintenance shutdown involving more than 4,000 employees faced planning fragmentation, unclear accountability, and coordination failures that threatened both schedule and safety. The cost of a single day's delay was €3,000,000.

Approach: Jori Eskolin developed and implemented a structured planning and performance improvement process covering the full shutdown lifecycle, with clear accountability mapping, capacity-versus-demand tracking, and a Maintenance Turnover Manual. The engagement established data-driven coordination across all teams involved in the shutdown.

Outcomes:

• Approximately €12,000,000 in savings achieved
• Zero risk events occurred
• Maintenance outage lead time reduced by four days
• Management team meeting quality improved by more than 50%

Terms and Conditions

Key Assumptions

• This proposal is based on the account intelligence and buying hypothesis analysis available as of the proposal date. All three signal hypotheses (SIG-01, SIG-02, SIG-03) are treated as discovery questions, not confirmed requirements. Phase scope will be adjusted based on Discovery findings.
• The engagement assumes that Sisu Auto will confirm and provide access to an executive sponsor at CEO or COO level before Phase 4 commences. Without confirmed sponsorship, no delivery phase proceeds.
• Named stakeholders (Tomi Gardemeister, Tuukka Tarkiainen, Juho Laine, Pekka Lidman, Henry Cusell, Homen Christian) are identified as discovery hypotheses. Their current roles, authority, and availability must be validated during On-Boarding before any hypothesis is advanced with them.
• Sisu Auto will provide access to relevant operational, quality, and supply-chain data for the purposes of the Flow Metrics Baseline and capacity modelling in Phase 4. If data access is restricted, scope will be adjusted and the access gap will be formally documented.
• Conditional phases (Phase 6 — SIG-02; Phase 7 — SIG-03) are not active commitments. Each requires a formal go/no-go decision by Sisu Auto's executive sponsor based on Discovery findings before investment is approved.
• Sujuva team members will require on-site access at Sisu Auto's facilities in Finland for Gemba Walks, stakeholder interviews, and workshop facilitation. Travel and accommodation costs are additional to consulting fees and are billed at cost with a 5% administration fee.
• Client-side availability of named stakeholders for interviews, workshops, and review cycles is required as specified in each phase. Delays caused by stakeholder unavailability may affect milestone dates and will be documented in the shared risk register.

Exclusions

• This proposal does not include software licensing, technology procurement, or system implementation services. Technology recommendations may be made as outputs of Phase 6 (SIG-02) but procurement and implementation are excluded from this scope.
• Regulatory compliance advice, legal interpretation of Digital Product Passport requirements, or formal ESG certification are excluded. Phase 7 (SIG-03) includes a readiness assessment and foundation blueprint but does not constitute legal or regulatory advice.
• Recruitment, HR advisory, or employment contract services are excluded. Organisational design outputs may include role profile recommendations but do not extend to recruitment execution.
• Integration engineering, software development, or cloud infrastructure deployment are excluded from this engagement scope.
• Services not explicitly listed in the Vendor Service Portfolio used in this proposal are excluded.

Intellectual Property Ownership

• All deliverables produced under this engagement, including reports, frameworks, blueprints, training materials, and the Flow Dashboard configuration, become the property of Sisu Auto upon full payment of the fees associated with the phase in which they were produced.
• Sujuva retains ownership of its pre-existing methodologies, frameworks, tools, and reusable templates. Where pre-existing Sujuva intellectual property is incorporated into deliverables, Sisu Auto receives a perpetual, non-exclusive licence to use those components for internal purposes.
• Neither party may use the other party's name, logo, or confidential information in external communications without prior written consent.

Confidentiality

• Both parties agree to treat all information shared during this engagement as strictly confidential. This includes operational data, financial information, stakeholder details, strategic plans, and engagement findings.
• Sujuva will not share Sisu Auto's confidential information with third parties except where required by law or with Sisu Auto's prior written consent.
• Sisu Auto will not share Sujuva's methodologies, frameworks, or engagement materials externally without prior written consent.
• Confidentiality obligations survive the termination of this engagement for a period of three years.
• Sujuva may reference this engagement as a case study in future marketing materials only with Sisu Auto's explicit written consent and subject to agreed anonymisation.

Change Management Procedure

All change requests arising during the engagement — including scope changes, resource adjustments, priority shifts, new hypothesis activations, and deliverable modifications — are addressed and handled as part of the ongoing Service Delivery operated within the mutually agreed Shared Operating Model defined during the On-Boarding Phase.

This means that changes are not treated as separate contract amendments requiring legal renegotiation. Instead, they flow through the existing governance, prioritisation, and decision-making structures of the Shared Operating Model:

• Operational changes (task-level adjustments, clarification of deliverable scope) are resolved at the Weekly Delivery Sync between the Sujuva Engagement Lead and the relevant Sisu Auto operational counterpart.
• Workstream-level changes (phase scope adjustments, resource reallocation within existing phases, milestone date shifts) are reviewed and decided at the Bi-Weekly Steering Review by the COO or relevant workstream owner.
• Strategic changes (activation of conditional phases, significant scope expansion, investment decisions for new workstreams, or engagement termination) are decided at the Monthly Executive Checkpoint by the executive sponsor.
• All change decisions are documented in the shared decision log maintained in the collaboration environment. A change is considered approved when the relevant authority level has recorded an explicit decision in the log.
• If a proposed change would require investment beyond the agreed phase budget, the Sujuva Engagement Lead prepares a brief impact assessment (scope, effort, cost, timeline effect) for review at the relevant governance level before the change is approved.
• The Shared Operating Model's backlog is the single source of truth for all active and pending work. New requests enter the backlog and are prioritised against existing commitments — they do not automatically displace agreed milestones.

Proposal Validity

This proposal is valid until 2026-09-04. After this date, pricing and team availability are subject to review. Sujuva reserves the right to withdraw or revise this proposal if material changes to the engagement context are identified after the submission date.

Governing Law

This engagement and any resulting agreements are governed by the laws of Finland. Any disputes arising from this engagement that cannot be resolved through the Shared Operating Model's escalation procedure will be subject to the jurisdiction of Finnish courts.