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Business Models for Digitalization Enabled Energy Efficiency and Flexibility in Industry: A Survey with Nine Case Studies
Published 26 Jan 2024 in cs.CY | (2402.01718v1)
Abstract: Digitalization is challenging in heavy industrial sectors, and many pi-lot projects facing difficulties to be replicated and scaled. Case studies are strong pedagogical vehicles for learning and sharing experience & knowledge, but rarely available in the literature. Therefore, this paper conducts a survey to gather a diverse set of nine industry cases, which are subsequently subjected to analysis using the business model canvas (BMC). The cases are summarized and compared based on nine BMC components, and a Value of Business Model (VBM) evaluation index is proposed to assess the business potential of industrial digital solutions. The results show that the main partners are industry stakeholders, IT companies and academic institutes. Their key activities for digital solutions include big-data analysis, machine learning algorithms, digital twins, and internet of things developments. The value propositions of most cases are improving energy efficiency and enabling energy flexibility. Moreover, the technology readiness levels of six industrial digital solutions are under level 7, indicating that they need further validation in real-world environments. Building upon these insights, this paper proposes six recommendations for future industrial digital solution development: fostering cross-sector collaboration, prioritizing comprehensive testing and validation, extending value propositions, enhancing product adaptability, providing user-friendly platforms, and adopting transparent recommendations.
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Practical Applications
Practical Applications Derived from the Paper
Below are actionable applications distilled from the paper’s findings, methods (AI/ML, IoT, Digital Twins), the Business Model Canvas (BMC) analysis, and the proposed Value of Business Model (VBM) evaluation index. Each item specifies sectors, potential tools/products/workflows, and key dependencies or assumptions.
Immediate Applications
These can be deployed now using existing technologies and practices discussed in the nine cases and the paper’s recommendations.
- Energy-aware production scheduling in steel galvanizing lines
- Sector: Steel manufacturing (Case 1)
- What: Deploy ant-colony or metaheuristic schedulers integrated with MES/ERP to minimize energy and cost in galvanizing line operations.
- Tools/Workflows: Scheduler engine + plant MES integration; tariff-aware planning; operator-in-the-loop overrides; KPI dashboards.
- Users: Operations planners, energy managers.
- Dependencies/Assumptions: Accurate process-time data, shift constraints, live/forecast electricity prices, change management.
- Online process optimization and inefficiency diagnostics for paper machines
- Sector: Pulp & paper (Case 2)
- What: ML-driven setpoint optimization and real-time inefficiency detection to save energy (e.g., ~11% savings reported).
- Tools/Workflows: Streaming data ingestion from DCS/QCS, ML models for setpoint recommendations, operator advisory UI, A/B testing.
- Users: Process engineers, shift operators.
- Dependencies/Assumptions: High-quality sensor data, robust data cleansing, operator acceptance, safety constraints enforcement.
- Product quality prediction to reduce scrap and energy waste
- Sector: Pulp & paper (Case 5)
- What: Supervised learning models that predict quality outcomes to preempt defects and rework.
- Tools/Workflows: Historical data labeling; feature engineering; model deployment with confidence thresholds; feedback loops for retraining.
- Users: Quality, production engineering.
- Dependencies/Assumptions: Stable correlations between process variables and quality; sufficient labeled data; integration with QCS/LIMS.
- Cross-industry “Energy Key Production Insight” dashboards
- Sector: General manufacturing (Case 4)
- What: Energy-performance dashboards correlating energy use with throughput, product mix, and schedules to reveal actionable opportunities.
- Tools/Workflows: Data connectors to historians/SCADA, automated anomaly detection, benchmarking widgets, alerting.
- Users: Plant managers, energy teams.
- Dependencies/Assumptions: Access to production and energy metering data; KPI definition alignment.
- Transportation fleet carbon and energy analytics
- Sector: Logistics/transportation (Case 3)
- What: Structured mobile/telematics data collection and analytics to reduce fuel/energy use and emissions.
- Tools/Workflows: On-vehicle telemetry; route optimization; eco-driving coaching; fleet-level dashboards.
- Users: Fleet managers, dispatchers.
- Dependencies/Assumptions: Driver buy-in; data coverage; integration with TMS.
- IoT-based energy monitoring and anomaly detection in brownfield plants
- Sector: Cross-industry
- What: Retrofit meters/sensors and connect to cloud/edge analytics for real-time energy visibility and fault detection.
- Tools/Workflows: IoT gateways, secure OPC UA connectors, edge analytics, rules/ML-based anomaly detection.
- Users: Maintenance, energy managers.
- Dependencies/Assumptions: Network reliability, cybersecurity posture, device interoperability.
- “Explainable recommendation” advisors for operators
- Sector: Cross-industry
- What: XAI-enabled advisory systems that suggest energy-optimal actions with transparent rationale to build trust.
- Tools/Workflows: SHAP/LIME-based explanation layers; human-in-the-loop validation; recommendation logging for audits.
- Users: Operators, supervisors.
- Dependencies/Assumptions: Usable UI; explanation fidelity; training and governance.
- Service and revenue models for digital energy solutions (consulting + maintenance)
- Sector: Software/ESCOs
- What: Offer consulting, customization, and maintenance SLAs tied to energy-savings KPIs (shared-savings or subscription models).
- Tools/Workflows: Standard onboarding playbooks; remote monitoring; customer success processes.
- Users: Solution providers; industrial customers.
- Dependencies/Assumptions: Clear M&V (measurement & verification) protocols; contractual clarity on data ownership.
- Portfolio prioritization using the VBM index
- Sector: Industry, accelerators, corporate innovation
- What: Apply the paper’s VBM to rank digitalization opportunities by TRL, relevance breadth, and BMC factors before scaling pilots.
- Tools/Workflows: Lightweight VBM calculator (spreadsheet or SaaS), stage-gate criteria, sensitivity analysis.
- Users: PMOs, R&D directors, funders.
- Dependencies/Assumptions: Consistent scoring; stakeholder alignment on thresholds (e.g., TRL ≥ 7 for commercialization).
- Policy programs aligned to TRL and commercial readiness
- Sector: Policy/funding agencies
- What: Embed TRL- and VBM-based criteria in calls, require real-world testing plans, and support interoperability standards.
- Tools/Workflows: Updated grant rubrics; post-award validation milestones; data-sharing guidance.
- Users: Agencies, public funders.
- Dependencies/Assumptions: Stakeholder engagement; standard definitions (ISO TRL 16290).
- “Lite” energy-flexibility playbooks for horticulture SMEs
- Sector: Horticulture/greenhouses (Cases 6–7)
- What: Price-aware scheduling for heating/lighting/ventilation without full digital twin—leveraging tariffs and simple rules.
- Tools/Workflows: Day-ahead price feeds; rule engines; operator dashboards.
- Users: Greenhouse operators.
- Dependencies/Assumptions: Tolerance for small production schedule shifts; reliable price signals; basic metering.
- Curriculum and case-based training for industrial digitalization
- Sector: Academia and workforce development
- What: Use the nine BMC-analyzed cases and VBM as teaching modules to train “digital energy” practitioners.
- Tools/Workflows: Case study packs; capstone projects; TRL/VBM exercises.
- Users: University programs, corporate L&D.
- Dependencies/Assumptions: Access to anonymized data; industry partnerships.
Long-Term Applications
These require further R&D, scaling, validation, or ecosystem development (many align with cases reporting TRL < 7 or with broader recommendations in the paper).
- Full-scope digital twins for closed-loop optimization
- Sectors: Horticulture (Cases 6–7), process industries
- What: High-fidelity digital twins of energy systems and production flows with automated control to optimize energy, cost, GHG, and quality simultaneously.
- Tools/Workflows: Hybrid physics-ML models; co-simulation; safe RL/control; automated retraining; MLOps for OT.
- Dependencies/Assumptions: Robust validation in operational environments; safe autonomy; cyber-secure integration with control layers.
- Cross-sector, configurable AI platforms for energy optimization
- Sectors: Manufacturing, process industries (Case 8-like “KI4ETA” evolution)
- What: Modular, domain-adaptable platforms that transfer solutions across sectors (steel → chemicals → food).
- Tools/Workflows: Domain adapters; semantic data models; model marketplaces; low-code configuration.
- Dependencies/Assumptions: Interoperability standards; sufficient similarity in process signatures; governance for IP/data.
- Automated participation in energy/flexibility markets (DR, ancillary services)
- Sectors: Energy-intensive industries; aggregators
- What: Systems that identify flexible loads, quantify flexibility, bid into markets, and orchestrate responses without harming production.
- Tools/Workflows: Flexibility forecasting; risk-aware bidding; production-aware control; settlement and M&V.
- Dependencies/Assumptions: Market access and tariffs; regulatory frameworks; contractual risk management.
- Smart heat management with predictive waste-heat utilization
- Sector: Steel manufacturing (Case 9)
- What: End-to-end optimization of heat flows (reheating furnaces, waste heat recovery, thermal storage) using predictive control.
- Tools/Workflows: Thermal network models; storage dispatch; predictive maintenance; LCA-coupled KPIs.
- Dependencies/Assumptions: Capital upgrades (WHR units, storage); detailed thermal data; multidisciplinary coordination.
- Standardized data-sharing and semantic interoperability for industrial energy
- Sectors: Cross-industry, software
- What: Adoption of OPC UA/TSN, ISA-95/88 mappings, and sector-specific ontologies to enable plug-and-play analytics.
- Tools/Workflows: Data contracts; digital thread; reference architectures; certification programs.
- Dependencies/Assumptions: Vendor participation; IP/privacy safeguards; alignment with cybersecurity standards (IEC 62443).
- Transparent, auditable XAI for regulated industrial decision support
- Sectors: Energy-intensive and regulated industries
- What: Explainability and audit trails as first-class features to meet compliance, safety, and ESG reporting needs.
- Tools/Workflows: Model cards; traceable recommendation logs; counterfactuals; bias tests.
- Dependencies/Assumptions: Standardized XAI metrics; regulator guidance on acceptable transparency.
- Integrated carbon accounting and dynamic LCA within operational optimization
- Sectors: Food processing, chemicals, materials
- What: Real-time carbon intensity tracking (including Scope 2 grid mix and Scope 3 inputs) to co-optimize cost, energy, and GHG.
- Tools/Workflows: Emission factor services; BOM-to-LCA mapping; optimization with multi-objective trade-offs.
- Dependencies/Assumptions: Reliable emissions data; supplier cooperation; accepted accounting methodologies.
- Public procurement and funding frameworks guided by VBM
- Sector: Policy/funding
- What: Institutionalize VBM-like indices to prioritize projects with scalable value propositions and sufficient TRL and relevance breadth.
- Tools/Workflows: Standard scoring templates; reviewer training; portfolio dashboards.
- Dependencies/Assumptions: Consensus on weights and thresholds; periodic recalibration based on outcomes.
- Workforce transformation and new roles (Digital Energy Operator)
- Sector: Industry HR/training
- What: Establish roles bridging operations, energy management, data science, and cybersecurity; certification pipelines.
- Tools/Workflows: Competency frameworks; apprenticeship programs; vendor-neutral certifications.
- Dependencies/Assumptions: Industry-academia collaboration; sustained demand signals.
- Secure edge–cloud control architectures for real-time optimization
- Sectors: Manufacturing, utilities interface
- What: Low-latency, resilient architectures that keep control at the edge while leveraging cloud for heavy analytics.
- Tools/Workflows: Edge inference; digital twin synchronization; zero-trust security; failover strategies.
- Dependencies/Assumptions: OT–IT convergence maturity; certified hardware; incident response capabilities.
Notes on Common Assumptions and Dependencies
- Data quality and access: Success hinges on reliable, time-synchronized process and energy data; integration with SCADA/MES/ERPs is essential.
- TRL and validation: Many solutions require operational-environment trials to progress beyond TRL 6; structured pilots and M&V are critical.
- Interoperability and standards: Adoption depends on open protocols and semantic models to avoid vendor lock-in.
- Cybersecurity and safety: Any control or advisory system must comply with industrial cybersecurity and functional safety norms.
- Business model alignment: Clear revenue and risk-sharing models (subscriptions, shared savings) improve adoption likelihood.
- Change management and skills: Operator trust, training, and XAI transparency materially affect usability and impact.
- Economics and policy: Energy price signals, incentives, and market access (for flexibility) shape ROI and feasibility.
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