Strategic Asset Management

Somewhere along the way, maintenance became a checkbox. A calendar event. A cost to control. But the factory floor is evolving. And so must the mindset. We don’t just repair anymore... We predict. We prescribe. We optimize. And when you optimize consistently, you stop reacting to problems…and start unlocking performance. That’s the real promise of Maintenance 4.0. Not just fewer breakdowns, but smarter resource planning, tighter production schedules, and data-driven capital decisions. It’s maintenance, yes. But not as you know it. To appreciate the significance of Maintenance 4.0, it's essential to understand its evolution of maintenance strategies: • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟏.𝟎 focused on reactive strategies, where actions were taken only after a failure occurred. This approach often led to significant downtime and high repair costs. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟐.𝟎 introduced preventative maintenance, scheduling regular check-ups based on time or usage to prevent failures. However, this method sometimes resulted in unnecessary maintenance activities, wasting resources. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟑.𝟎 saw the advent of condition-based maintenance, utilizing sensors to monitor equipment and perform maintenance based on actual conditions. This strategy marked a shift towards more data-driven decisions but still lacked predictive capabilities. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟒.𝟎 builds upon the foundations laid by its predecessors by leveraging advanced predictive and prescriptive maintenance techniques. Utilizing AI and machine learning algorithms, Maintenance 4.0 can anticipate equipment failures before they occur and prescribe optimal maintenance actions. In addition, the data-driven insights provided by Maintenance 4.0 can facilitate strategic decision-making regarding equipment investments, production planning, and innovation initiatives through better integration with other programs and systems, such as Enterprise Asset Management (EAM) and Asset Performance Management (APM). 𝐅𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞: https://lnkd.in/djjfivw8 ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Sustainability Maturity Self-Assessment 🌎 Understanding the level of sustainability integration within an organization requires structured analysis across multiple operational dimensions. Moving beyond isolated initiatives, this approach provides a clearer view of internal alignment and areas requiring systemic improvement. Disclosure practices are a key area of focus. Integrated reporting that connects sustainability and financial data, alignment with frameworks such as TCFD, and preparation for new regulatory requirements indicate a higher level of maturity. Effective organizations establish clear sustainability targets. These targets are measurable, time bound, and supported by transition plans and internal accountability. They serve as reference points for strategic planning and operational execution. Governance is another critical pillar. The presence of formal structures, leadership ownership, and cross departmental coordination reflects whether sustainability is embedded into core decision making processes. Board oversight acts as a signal of institutional prioritization. Regular engagement, monitoring through defined indicators, and integration into enterprise risk management processes are all essential components. Data quality underpins all sustainability decisions. Organizations are evaluated based on their ability to collect, estimate, and validate key metrics, particularly emissions data aligned with recognized methodologies. Value chain visibility expands the lens beyond internal operations. The ability to monitor sustainability performance upstream and downstream indicates a broader understanding of impact and risk exposure. Procurement strategies also reflect the depth of integration. When sustainability criteria shape supplier selection and guide collaborative initiatives, procurement becomes a tool for driving environmental and social outcomes. This type of evaluation does not produce a static score. Instead, it highlights capability gaps, supports internal benchmarking, and informs priorities for systems level improvements aligned with strategic sustainability objectives. #sustainability #sustainable #esg #business

With public equity and fixed income markets in turmoil in recent weeks the traditional 60:40 portfolio model has again been challenged. There's little doubt uncertainty will pervade these markets for the foreseeable future. Therefore it is timely to release further research on the beneficial portfolio characteristics of private market assets. In this paper "Optimising private market asset allocations" we examine the integration of this asset class within traditional asset allocation strategies to  assess performance impacts across investor risk profiles. We believe that including private market assets can significantly enhance portfolio returns for investors who adopt a risk-based utility-maximising strategy in portfolio construction. Additionally, we find that unlisted infrastructure has the most potential of the private market assets considered to improve portfolio Sharpe ratios, especially for ‘Defensive’ and ‘Balanced’ investors. Our research applies a utility maximisation framework which facilitates risk appetite aware optimisation to tailor portfolios to match specific investor risk preferences and lifecycle stages. A novel two-stage returns unsmoothing approach is used to more accurately estimate true private market return volatility. We show that even after returns unsmoothing, private markets can significantly enhance portfolio outcomes. This study finds that defensive investors benefit from allocations  to infrastructure and private credit, achieving lower volatility and higher returns. Balanced investors see similar advantages with  a stable allocation to infrastructure, while growth investors lean towards private equity for higher risk-reward profiles. This analysis adds further weight to our assertion that private market assets have a material role to play in optimising investor portfolios. With IFM Investors Economics & research Frans van den Bogaerde, CFA and Christopher Skondreas #investment #assetallocation #risk #privatemarkets #portfolioconstruction

OT Asset Management under NIST 1800-23 >> NIST 1800-23: Energy Sector Asset Management (ESAM) delivers a blueprint for visibility, control, and resilience across electric utilities, oil & gas, and other critical infrastructure sectors. >>> This project addresses the following characteristics of asset management: > Asset Discovery: establishment of a full baseline of physical and logical locations of assets > Asset Identification: capture of asset attributes, such as manufacturer, model, OS, IP addresses, MAC addresses, protocols, patch-level information, and firmware versions > Asset Visibility: continuous identification of newly connected or disconnected devices and IP and serial connections to other devices > Asset Disposition: the level of criticality (high, medium, or low) of a particular asset, its relation to other assets within the OT network, and its communication with other devices > Alerting Capabilities: detection of a deviation from the expected operation of assets >>> A standardized architecture allows organizations to replicate deployments across sites while tailoring to local needs, ensuring both scalability and security. > At each remote site, control systems generate raw ICS data and protocol traffic (Modbus, DNP3, EtherNet/IP), which is collected by local data servers. > These servers act as the secure bridge, encapsulating serial traffic and transmitting structured data through VPN tunnels back to the enterprise. > Once in the enterprise environment, asset management tools aggregate inputs from multiple sites, giving analysts a single source of truth. > Events and asset health indicators are displayed on centralized dashboards, enabling timely detection of anomalies, vulnerabilities, or misconfigurations. > Importantly, remote management is limited only to the data servers, ensuring that core control systems remain shielded from unnecessary exposure. >>> Here’s a 10-point summary of the ESAM reference design asset management system: > Data Collection – Gathers raw packet captures and structured data from OT networks. > Remote Configuration – Allows secure management and policy-driven data ingestion. > Data Aggregation – Centralizes collected data for further processing. > Monitoring – Continuously observes network activity for anomalies. > Discovery – Detects new devices when new IP/MAC addresses appear. > Data Analysis – Normalizes multi-site traffic into one view and establishes baselines of normal behavior. > Device Recognition – Identifies devices via MAC addresses or deep packet inspection (model/serial). > Device Classification – Assigns criticality levels automatically or manually. > Data Visualization – Displays collected and analyzed information in a centralized dashboard. > Alerting & Reporting – Notifies analysts of abnormal events and generates reports, including patch availability. #icssecurity #OTsecurity

🛑 “𝐒𝐭𝐨𝐩 𝐮𝐬𝐢𝐧𝐠 𝐀𝐈 𝐣𝐮𝐬𝐭 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞.” 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐚𝐬𝐬𝐞𝐭 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐝𝐞𝐩𝐞𝐧𝐝𝐬 𝐨𝐧 𝐡𝐨𝐰 𝐝𝐞𝐞𝐩𝐥𝐲 𝐰𝐞 𝐭𝐫𝐚𝐢𝐧 𝐨𝐮𝐫 𝐩𝐞𝐨𝐩𝐥𝐞 - 𝐧𝐨𝐭 𝐨𝐮𝐫 𝐦𝐨𝐝𝐞𝐥𝐬. I shared this in a recent interview with Ignites Asia - the premium Financial Times news service for asset managers - and I meant every word. Asset management remains one of the most underserved industries when it comes to AI transformation. We manage trillions. We assess risk for a living. We make decisions that shape retirements, sovereign wealth, and entire economies. And yet - far too many workflows still run on Excel and Outlook. But the ground is shifting beneath our feet: 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐰𝐚𝐭𝐜𝐡𝐢𝐧𝐠 - 𝐚𝐧𝐝 𝐚𝐬𝐤𝐢𝐧𝐠 𝐬𝐡𝐚𝐫𝐩𝐞𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬. It’s no longer enough to invest in AI-themed stocks or cite innovation in pitch decks. Institutional clients - from super funds to family offices - and even UHNW individuals are now asking how firms internally use AI to improve performance and efficiency. Questions like “How are you using AI in your investment process?” are becoming standard in RFPs, due diligence meetings, and client conversations. If your answer stops at email drafting or document summarisation, you risk not just sounding outdated - but appearing strategically out of touch. In a trust-driven industry, demonstrating AI fluency is fast becoming part of your firm’s credibility and differentiation. 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐨𝐫𝐬 𝐚𝐫𝐞 𝐠𝐚𝐢𝐧𝐢𝐧𝐠 𝐠𝐫𝐨𝐮𝐧𝐝 - 𝐞𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐥𝐲 𝐭𝐡𝐞 𝐚𝐠𝐢𝐥𝐞 𝐨𝐧𝐞𝐬. Boutique and tech-forward firms are embedding AI into research, valuation, and monitoring. They surface signals from unstructured data, automate what once required full teams, and speed up execution. The traditional advantage of scale is being challenged by adaptability and sharper infrastructure. Incumbents who don’t evolve risk becoming obsolete - not by size, but by stagnation. 𝐘𝐨𝐮𝐫 𝐭𝐚𝐥𝐞𝐧𝐭 𝐞𝐱𝐩𝐞𝐜𝐭𝐬 𝐛𝐞𝐭𝐭𝐞𝐫 - 𝐚𝐧𝐝 𝐭𝐡𝐞𝐲’𝐫𝐞 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 𝐲𝐨𝐮. Today’s top PMs, analysts, and ops leaders want more than a big brand. They want tools that elevate their thinking. They hear what peers are doing with LLMs and workflow automation. When they see others cutting reporting time in half or experimenting with GPTs, they wonder: “Why aren’t we doing this?” Without a clear, empowering AI strategy, you risk losing top talent to firms that offer capability, not just compensation. So stop using AI just to write. Let it be your research assistant. Let it be your investment partner. Let it be your competitive advantage. 👉 CEOs of asset managment firms should personally lead the way and lead the AI fluency of their orgnisation. As Peggy Lee, CEO of BNP Paribas AM, said in the same Ignites Asia article: “Having a personal interest in AI is essential if you want to take your organisation to the next level.” ----- 👉 Follow Will Liang for more 👉 DM me to chat

Time-Consistent Dynamic Mean-Variance Asset Allocation: Markowitz mean-variance analysis was originally designed for a single-period framework. It has also been used for multiple periods in the case of myopic investors, who in each period maximize the objective of the following period. However, when solving the dynamic asset-allocation problem with mean-variance criteria, we encounter the problem that dynamic programming techniques fail because of the impossibility of applying the iterated expectation property. This fact is known as the time-inconsistency problem of the mean-variance criterion. Basak and Chabakauri (2010) solve the time-inconsistency problem for two securities, a riskless bond and a risky stock, in an incomplete-market setting, and provide a simple, tractable solution for the risky stock holdings. Moreover, the authors obtain this result without solving the Hamilton-Jacobi-Bellman equation. The formulas below correspond to the general result of Basak and Chabakauri (2010). In them: - theta_t^* is the optimal dollar amount invested in the stock at time t (the optimal stock investment policy) - S_t and X_t are, respectively, the stock price and the state variable at time t - mu_t=mu(S_t,X_t,t) - r is the (constant) bond interest rate - gamma is the risk aversion parameter - sigma_t=sigma(S_t,X_t,t) - rho is the correlation between the two Brownian motions w_t and w_Xt - nu_t=nu(X_t,t) - E_t^* is the expectation under the so-called hedge-neutral measure - T is the finite horizon The authors show that their time-consistent solution is generically different from the pre-commitment solutions in the extant literature (a mean-variance investor under pre-commitment maximizes her initial objective and pre-commitments to that initial investment policy, not deviating at subsequent times). The authors study different special cases of their general framework. Specifically, a CEV model in a complete market, a mean-reverting stochastic-volatility model in an incomplete market, and a time-varying Gaussian mean-returns model in an incomplete market. In a later work, Björk et al. (2014), consider unrealistic that Basak and Chabakauri's optimal policy does not depend on current wealth and propose a time-consistent model, in which the risk aversion depends dynamically on current wealth. These authors place the problem within a game theoretical framework and look for subgame perfect Nash equilibrium strategies. When the risk aversion is inversely proportional to wealth, they find an analytical solution where the optimal policy is proportional to current wealth. References: - Basak and Chabakauri (2010): "Dynamic Mean-Variance Asset Allocation", Review of Financial Studies, 23 (8), 2970-3016. - Björk, Murgoci and Zhou (2014): "Mean-Variance Portfolio Optimization With State-Dependent Risk Aversion", Mathematical Finance, 24 (1), 1-24. - Markowitz (1952): “Portfolio Selection,” Journal of Finance, 7, 77-91. #finance #mathematics #markets

Capital Constraints: The Quiet Force Behind Strategic Trade-Offs Capital is one of the most tightly regulated aspects of banking—and one of the most misunderstood. While most institutions know their CET1 ratios and monitor RWAs carefully, capital constraints often influence decisions in ways that are subtle, indirect, and not immediately visible in commercial discussions. Capital is not just a compliance issue. It is a finite resource. And how that resource is allocated shapes the bank’s risk appetite, pricing strategy, growth priorities, and overall profitability. Understanding this constraint—and managing it strategically—is far more advantageous than simply aiming to “stay above the buffer.” Here are three ways capital constraints shape outcomes across the balance sheet: 1. Not all capital consumption is obvious Some products consume capital invisibly. For example, undrawn credit lines attract capital due to potential future exposure. So do operational risk activities, such as certain payment services. Without a clear framework, business lines may grow portfolios that appear profitable but quietly reduce headroom. A realistic view of capital consumption, including through off-balance-sheet exposures, is vital to avoid hidden constraints. 2. Risk-weighted assets (RWAs) do not always align with economic risk Regulatory capital models use standardised or internal weightings, but these often diverge from actual economic risk. A low-risk, high-quality mortgage portfolio may carry a higher capital charge than a corporate exposure with less predictable cash flows. Strategic capital management requires more than just minimising RWAs—it involves optimising the mix of assets to align regulatory requirements with real risk and return. 3. Capital allocation must be linked to pricing and performance If capital is treated as “free,” business lines will pursue growth that undermines long-term value. Capital costs should be embedded in FTP and pricing frameworks, ensuring that products reflect their true contribution to return on capital. This also supports more accurate performance evaluation, helping leadership prioritise growth in business areas that create sustainable value—not just headline income. What does effective capital management look like? It involves realistic stress testing, to ensure capital buffers are sufficient not only under baseline conditions but also under market stress. It requires dynamic monitoring, so that shifts in credit mix, market conditions, or regulatory rules can be addressed early. And it demands alignment between capital planning and commercial strategy—so that growth ambitions do not outpace available resources. Treasury, finance, and risk teams should work closely together to manage the capital constraint holistically, with clear communication to business units and senior management. Capital is not just a number—it is a strategic lever. When used well, it enables the bank to grow sustainably.

Most Projects Fail to Deliver Full Value… Because Stakeholder Management Is an Afterthought. ~ Conflicting priorities stall critical decisions. ~ Misaligned expectations derail project timelines. ~ Key sponsors disengage, leaving teams without support. And yet, when these challenges arise, most teams focus on “more updates” or “more stakeholder meetings.” But the real issue isn’t the frequency of communication – It’s ineffective stakeholder management. Here’s what I consistently see in projects: → Too Many Decision-Makers – Multiple stakeholders with conflicting goals slow down consensus and project momentum. → Competing Priorities – What’s urgent for one stakeholder may be irrelevant for another, creating constant friction. → Limited Resources – Tight budgets and stretched teams make balancing stakeholder demands increasingly difficult. These challenges lead to delays, frustration, and loss of stakeholder trust. What’s the solution? A structured and strategic stakeholder management approach, not just ad hoc engagement. Here’s how I help organisations elevate their stakeholder management: 1. Clarify Expectations Early → Align all stakeholders on shared goals, roles, and success metrics upfront. 2. Strategic Stakeholder Mapping → Using tools like the Power-Interest Matrix to categorise stakeholders and tailor engagement accordingly. 3. Targeted Communication Strategies → Communicating the right information, to the right people, at the right time. 4. Action-Oriented Engagement Plans → Prioritising critical stakeholders and focusing efforts where they create the most impact. When organisations manage stakeholders effectively, the outcomes speak for themselves: → Faster decision-making: Streamlined discussions and fewer bottlenecks.  → Stronger stakeholder alignment: Reduced conflicts and enhanced project cohesion.  → Higher project success rates: Deliverables that meet or exceed expectations.  → Improved stakeholder relationships: Greater trust and long-term collaboration. Stakeholder management isn’t a soft skill – it’s a business-critical strategy. Are competing priorities slowing your projects down? Let’s address it. Drop me a message and let’s explore how structured stakeholder engagement can drive project success and stakeholder buy-in. —- 📌 Want to become the best LEADERSHIP version of yourself in the next 30 days? 🧑💻Book 1:1 Growth Strategy call with me: https://lnkd.in/gVjPzbcU #Leadership #Strategy #Projects #Success #Growth

Online Portfolio Selection (OLPS) is a framework in finance where the goal is to allocate wealth across a set of financial assets dynamically to maximize cumulative returns over time. Unlike traditional portfolio management, OLPS assumes no prior knowledge of the market distribution and continuously adjusts the portfolio based on observed market data, making it particularly suitable for non-stationary environments. The frequent portfolio rebalancing in OLPS can lead to significant transaction costs that erode gains. Also, its focus on maximizing returns may lead to high portfolio volatility. Some advanced OLPS strategies can be computationally expensive, especially with a large number of assets or high-frequency data. Intelligent software agents must strategically allocate capital among multiple assets in each period to maximize cumulative wealth. However, the non-stationary nature of financial markets introduces significant risks, as past performance is no guarantee of future results. Effective OLPS requires a delicate balance between diversity, sparsity, and risk control. Unfortunately, existing algorithms often struggle to achieve this balance, prioritizing one property while compromising the others. To address the aforementioned issue, the authors of [1] propose an asset subset-constrained minimax (ASCM) optimization framework that generates optimal portfolios from diverse investment strategies represented as asset subsets. ASCM consists of: • A minimax optimization model that focuses on risk control by considering a set of loss functions constrained by different asset subsets. • The construction of asset subsets via price-feature clipping, which effectively reduces redundant assets in the portfolio. • A state-based estimation of price trends that guides all ASCM loss functions, facilitating the generation of sparse solutions. The ASCM minimax model is solved using an efficient iterative updating formula derived from the projected subgradient method. Furthermore, near-linear time complexity is achieved through a novel initialization scheme. #QuantFinance Experimental results show that ASCM outperforms eight other algorithms in terms of cumulative-wealth, including the benchmark BCRP, on five of six real-world financial datasets. Notably, ASCM significantly improves upon BCRP ( the best constant rebalanced portfolio ), achieving a 67-fold increase in cumulative wealth on the TSE dataset. The links to the paper [1] and #Python GitHub repo, are posted in the first comment.

Sustainability is no longer an option in infrastructure development; it’s an imperative. In recent years, the imperative to incorporate sustainability into infrastructure development has gained significant traction. State and central impact assessment authorities are at the forefront of this movement, ensuring that urban and rural projects align with environmental goals and the Sustainable Development Goals (SDGs). By setting forth specific guidelines and conditions, these authorities are steering the construction industry towards greener, more sustainable practices. The Push for Green Infrastructure Sustainability in infrastructure isn't just about aesthetics; it’s a holistic approach to building that considers environmental impact, resource efficiency, and community well-being. The recent guidelines established by impact assessment authorities reflect a commitment to integrating green features into new developments, ensuring they are not only functional but also environmentally responsible. Central and state impact assessment authorities are driving this shift, aligning urban and rural projects with environmental goals and Sustainable Development Goals (SDGs). By establishing clear guidelines, they are pushing the construction industry toward greener, more responsible practices. Key Guidelines for Sustainable Development 1. Renewable Energy Adoption: At least 5% of energy needs must come from solar or other renewable sources, reducing fossil fuel dependency. 2. Green Recreational Spaces: Developers must include green areas to boost biodiversity, improve air quality, & enhance community well-being. 3. Water Management: Effective wastewater management encourages reuse, with treated water discharge limited to 35%. 4. Air Quality Standards: Dust mitigation measures protect workers & residents, ensuring better air quality. 5. Waste Management: Proper segregation and recycling of solid waste reduce landfill dependency. 6. Groundwater Protection: Regular monitoring ensures sustainable use and safeguards against contamination. 7. Sewage and Rainwater Systems: Sewage treatment plants and rainwater harvesting foster sustainable water practices. The Broader Impact These guidelines represent more than compliance; they mark a shift toward sustainable development by: Protecting the Environment: Reducing ecological footprints and preserving biodiversity. Improving Community Health: Green spaces & cleaner air and water enhance residents’ well-being. Boosting Economic Efficiency: Long-term cost savings through resource conservation. Demonstrating Social Responsibility: Developers embracing sustainability build trust & goodwill. Green practices in infrastructure development are vital for a healthier, more resilient future. By adhering to these guidelines, we ensure that development goes hand in hand with environmental stewardship, benefiting communities & safeguarding resources for generations to come. #Sustainability #GreenInfrastructure #UrbanPlanning