Business Model Simulation & Optimization

Application Domains. Infrastructure Networks, Green Buildings, Water Utilities, Agriculture

The design and investment decisions in sustainable infrastructure systems such as green buildings and resource recovery in water utilities are influenced by the business model, investor risk perceptions, stakeholder preferences and lifecycle costs
of these investments. Given the uncertainties in time and as a function of type of investor, user, policy, and business model or revenue assumptions, a decision feedback loop can be simulated and optimized for a specific infrastructure system,
investor, and user/stakeholder.

Whereas traditionally, investment models align with social infrastructure, regulated business, or demand-based infrastructure, the business of interdependence (networked infrastructure) and the emerging data economy are changing infrastructure finance
models, risk perception, and design decisions.

Cycle of research
Expected risks graphed against expected returns

Integration of Financial Innovation with CEE Domain Expertise

“By considering stakeholder network characteristics in terms of energy conservation and use, we can develop a decision framework for green building designs under various agent-based models. Understanding the risk perception and evaluation processes by different types of investors will provide feedback to infrastructure design and investment decisions.”

–Carol Menassa, Professor & John L. Tishman  Faculty Scholar

“Conceptualization of urine diversion streams and new treatment infrastructure as a resource recovery strategy for water utilities needs to be validated using rigorous business model analysis. Is this a new line of business for the utility, a third party fertilizer revenue stream, or a scalable marketplace for nutrients?”

–Glen Daigger, Professor of Practice,  Environmental and Water Resources Engineering

“The integration of IoT and big data approaches for the deployment of ‘smart drains’ in agricultural fields helps to manage erosion and uncontrolled runoff. We have the technology and testbeds. However, we need to understand how the IoT value can be captured or monetized by the farmers and the cities or townships, and how investors beyond public grants can get engaged to aid in the design of scaled agricultural infrastructure.”

–Branko Kerkez, Professor and Director, Real-Time  Water Systems Lab

“Our NSF network of infrastructure hazard simulation grant seeks to integrate models will allow us to evaluate cascading effects from disruptions in infrastructure systems. It would be of great value to be able to connect technical risk assessment and risk mitigation with financial risk management – particularly insurance and reinsurance…”

–Sherif El-Tawil, Professor and Director,  Computational Structural Simulation Laboratory  (CSSL)

“Four step planning models that connect human activity to route choice in transportation systems connect scenario models with equilibrium and pricing analysis, but have feedback loops to strategic level transportation network designs… An extension of the feedback loops to investment and asset valuation models would help in the buildout of adaptive systems.”

–Neda Masoud, Assistant Professor, CEE

Financial Network Mapping

Financial network mapping
Financial network mapping

Application Domain. Infrastructure Systems Investing

Visualization is key in big data analytics.

Financial network maps are network visualizations of the financial system of emerging industry systems and clusters. The underlying premise is that industries change as the result of regulations, economic dynamics and technological innovation. The result is that new industry segments become integrated in ‘traditional value chains’, particularly as ‘resilience and adaptive investing’ is becoming a trend in economic development.

The integration of information technologies, data analytics, artificial intelligence and machine learning in traditional industries is resulting in industrial renewal, such as the shift from transportation to mobility access, from centralized energy to ‘micro-grids’, from water treatment and provision to ‘smart water’, from traditional agriculture to ‘climate-resilient farming’, are enabling data-driven business models and disrupting key sectors of the economy.

Our research seeks to understand how network theory can be used to explain shifts in functional cross-sectoral industry ecosystems at high levels of granularity.

We have focused on smart mobility, smart grid, green chemistry, and smart agriculture to uncover financial relations in emerging industry ecosystems.

Students and Collaborators

Dimitris Assanis, PhD, Mechanical Engineering; Ryan Moya, Erb Institute and Ross School of Business

Antti Tahvanainen, PhD, Research Institute of the Finnish Economy, Helsinki, Finland; Susan Zielinski, Smart Mobility, UM; Robert Hampshire, UMTRI; Siqian Chen, Professor, IOE.

Ryan Moya, MS/MBA; currently senior program manager (Energy/Sustainability) at Commercial Building Real Estate (Seattle), working on Microsoft’s real estate portfolio

Robert Hampshire, Professor of Operations Management and Policy, The University of Michigan.

Left: Financial network relationships (nodes and edges) between industry segments engaged in smart mobility. The edges reflect the frequency of financial interactions between the industry segments (nodes) in the industry.

Right: Identification of anchor (red) and catalyst (blue) industry segments using network theory (centrality and connectivity). Anchor nodes are characterized by limited, high density networks, where catalyst nodes reflect extensive low density networks between various anchors and other catalysts.

(Source: Adriaens and Tahvanainen, 2016; Data: Bloomberg; Visualization: Gephi)