Management Systems Consulting

𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗲𝗶𝗴𝗵𝘁 𝘀𝗶𝗺𝗽𝗹𝗲 𝘀𝘁𝗲𝗽𝘀 𝗳𝗼𝗿 𝗖𝗙𝗢𝘀 𝘁𝗼 𝗺𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹𝘀... You need to know your numbers. No one else will. But how can you best monitor and analyze the financials? First an overview of the eight steps to improve... 1. Establish KPIs 2. Financial reporting 3. Variance analysis 4. Financial ratios 5. Forecasting 6. Financial planning 7. Technology and Analytics 8. Financial reviews ---------- 1️⃣ Establish KPIs Identify and track key financial metrics that are relevant to the organization. These may include revenue growth, profitability margins, cash flow, ROI, and working capital ratios. Establish benchmarks and targets to assess performance. 2️⃣ Financial reporting Implement a robust financial reporting system that provides timely and accurate financial information. Regularly create financial statements, including income, balance sheets, and cash flow statements. 3️⃣ Variance analysis Perform variance analysis to compare financial results against budgets, forecasts, and prior periods. Identify and analyze the reasons for significant variances. Use variance analysis to identify trends, opportunities, and potential risks. 4️⃣ Financial ratios Utilize financial ratios and KPIs to assess financial health and performance. These may include liquidity ratios, profitability ratios, efficiency ratios, and leverage ratios. Monitor changes in these ratios over time and benchmark them. 5️⃣ Forecasting Develop financial forecasting models and conduct scenario analysis to project future financial performance. Assess the impact of different scenarios on financials, like market fluctuations, pricing changes, and legal shifts. 6️⃣ Financial planning Collaborate with the executive team on the development of long-term financial plans, budgeting processes, and resource allocation. Provide financial insights and analysis for strategic initiatives, investment decisions, and growth strategies. 7️⃣ Technology and Analytics Use financial technologies and analytics tools to enhance financial monitoring and analysis. Implement data visualization tools to present financial information. Explore advanced analytics techniques, such as predictive modeling and data mining. 8️⃣ Financial reviews Schedule regular financial reviews with the executive team and relevant stakeholders. Present financial performance reports, discuss key findings and address any questions or concerns. Provide financial insights and highlight risks and opportunities. ---------- I have used these steps many times with success to create tangible results and business leaders are eager for you to step in and get it done. Are you currently following these eight steps? Anything you'd add or change? #finance #cfo #accountingandaccountants #analytics 🎧 Listen to our #FinanceMaster Podcast here: https://bit.ly/3NLSt73 🧑🎓 Learn how we can help your finance team here: https://bit.ly/3prsWXH

Knowledge Management is hands down the most important factor for scalable GenAI adoption. Here’s a breakdown of the key components: 𝗖𝗲𝗻𝘁𝗿𝗮𝗹 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: The knowledge lifecycle spans the entire knowledge management process, interacting with all other components. It acts as the main decision-making and routing mechanism. 𝗖𝗿𝗲𝗮𝘁𝗲: Documenting knowledge and guiding users on how to capture their experiences with knowledge (both positive and negative). 𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗲: Structuring content and organising it in a way that ensures ease of access and effective retrieval. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲: Knowledge management relies on systems thinking. As systems evolve, knowledge must be continually improved. 𝗦𝗵𝗮𝗿𝗲: The way existing and new knowledge is presented to users determines its effectiveness. Every business must understand its knowledge-sharing practices—at its core, this is change management. 𝗥𝗲𝘂𝘀𝗲: Reducing redundant work is fundamental to knowledge management. Creating reusable knowledge leads to faster time-to-value for an organisation. For every instance of unsuccessful scaling of an AI solution, there is often a story of poor knowledge management. The more projects we complete at Insurgence, the clearer it becomes that effective and automated knowledge management is at the heart of successful AI adoption at scale. Yes, it’s not glamorous, but it drives progress for the initiatives that do capture attention. Step 1: Find great ideas for AI. Step 2: Build a mechanism to enable them to thrive throughout your organisation at scale. Mandatory component of Step 2: Knowledge Management. At Insurgence we're doing both. Feel free to reach out for a yarn on where AI could help out your team!

The WCOM Playbook: 3 Keys to Transformation 1. Loss Intelligence: Start with Strategy Before jumping to solutions, they mapped all losses (productivity, quality, cost) and tied them to business outcomes. → Your Takeaway: Use value-stream mapping to identify where margins leak—don’t assume you already know. 2. Loss Eradication: Focus on High-Impact Fixes They prioritized quick wins (e.g., reducing changeover times) alongside systemic fixes (e.g., predictive maintenance). → Your Takeaway: Balance 30% "low-hanging fruit" (for momentum) with 70% structural improvements (for sustainability). 3. Loss Prevention: Culture is the Foundation 85%+ employee engagement, 100+ improvement teams, and leadership role-modeling the "Zero Losses" mindset turned WCOM into "how we work." → Your Takeaway: Tie KPIs to individual/team accountability—culture change requires skin in the game. Who Can Replicate This? Manufacturers: Battling rising COGS and global competition Logistics Firms: Needing to optimize asset utilization Healthcare Systems: Facing margin pressure and labor shortages Any company where operational excellence = competitive advantage The Bigger Lesson This wasn’t just a cost-cutting exercise. By embedding WCOM into supply chain, product development, and business processes, they turned operations into a growth engine. Want to transform your operations from a cost center to a profit driver? Let’s discuss how to adapt this framework to your business. #OperationalExcellence #CostTransformation #ZeroLoss #LeanManufacturing #Leadership #managementconsulting 🔗 Case study link in comments 👇

What’s one thing that can turn a good sustainability plan into a great one? As we work to make businesses more sustainable, there’s one approach that often flies under the radar but makes a real difference: Six Sigma. Yes, the same Six Sigma that transformed manufacturing can also be a powerful tool in sustainability efforts. Here’s how. Six Sigma starts with a focus on the customer—whether that’s a buyer or the environment. It’s a way of reducing waste, spotting inefficiencies, and refining processes to reduce errors. In sustainability, accuracy matters more than ever. Six Sigma helps teams pinpoint where waste occurs, how much, and what impact it has, using data to make decisions with confidence. To break it down, Six Sigma follows five steps, each with a purpose: -Define – This is where the team starts by identifying the problem clearly. Imagine a project aiming to cut down on packaging waste. Define the specific waste issues, what success would look like, and who the key “customers” of this improvement are—whether it’s the planet, a community, or the bottom line. -Measure – Next, collect data. For instance, if packaging waste is the focus, measure how much waste is currently generated. Analyzing the flow of materials allows for precise benchmarks that ensure improvements are tracked effectively. -Analyze – This is where teams dig deep, examining the causes of waste or inefficiencies. In our packaging example, they might find that excessive or non-recyclable materials are the primary issues, pinpointing areas to change. -Improve – Now, with root causes in hand, it’s time to make changes. Teams might test out solutions like biodegradable materials or redesigning packaging to use less. Improvements are guided by data, making the process both strategic and impactful. -Control – Finally, sustaining progress means implementing control systems. Regular checks make sure that the new packaging methods continue to reduce waste and meet environmental goals. The result? Real, data-backed progress. Studies show that Six Sigma projects can reduce errors and waste by up to 50% while increasing productivity. For sustainability, that means cutting resource use, lowering emissions, and hitting those ambitious goals. Have you used Six Sigma in your work? Or Are you considering it for sustainability efforts?

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

Continuing with the GenAI series, I am excited to share how we revolutionised the knowledge management system (KMS) for a leading client in the manufacturing industry. R&D teams in manufacturing often face the tedious task of manually sifting through complex engineering documents and standard operating procedures to ensure compliance, uphold safety standards, and drive innovation. This manual process is not only time-consuming but also prone to errors. To address this, we collaborated with our client to automate their R&D function’s KMS using Generative AI (GenAI). By allowing precise querying of specific sections of documents, our solution sped up access to critical information, reducing search time from hours to mere seconds. Our Generative AI team processed over 110 R&D-related documents, leveraging Large Language Models (LLMs) to generate accurate responses to complex queries. Hosted on a leading cloud platform with an Angular-based UI, the solution delivered remarkable benefits, including: - Significant accuracy in generated answers - Faster and more accurate data search and summarisation - Enhanced decision-making with easier access to critical R&D information - Improved overall employee productivity By implementing GenAI for knowledge management, the client's R&D function was also able to improve its competitive edge by tracking and responding quickly to market trends and consumer behavior. With plans to scale the solution to process over 1,500 documents across multiple departments, the client is creating a centralised hub for all their information needs. Taking advantage of GenAI can revolutionize knowledge management by delivering the right information to the right person on demand and enabling strategic impact. #GenAI #ManufacturingInnovation #KnowledgeManagement #GenAIseries #GenAIcasestudy #Innovation #R&D #DigitalTransformation #AI #Deloitte

For enterprises Knowledge as a Service (KaaS) is getting crucial for AI readiness. The knowledge layer needs to sit on top of existing enterprise systems, making organizational knowledge accessible, maintainable, and AI-ready while preserving existing operational capabilities and governance. Let me try to bring clarity to KaaS Knowledge Discovery and Mapping Map all operational databases and their relationships Identify data warehouses and their current analytical models Document unstructured data sources (documents, emails, process documentation, pictures, videos etc.) Catalog existing business intelligence reports and dashboards Knowledge Flow Analysis Map how data flows between different systems Identify key business processes and their data dependencies Document decision points that require knowledge access Knowledge Structure Development Categorize data based on business context and usage Identify critical knowledge areas and their relationships Create taxonomy for organizing enterprise knowledge Establish metadata framework for knowledge assets Knowledge Model Creation Design knowledge graphs connecting different data sources Create semantic relationships between business concepts Develop ontology for business domain knowledge Map data lineage across systems Technical Implementation Deploy knowledge management platform Implement connectors to operational databases and data warehouses Set up real-time data synchronization mechanisms Create APIs for knowledge access and retrieval Processing Pipeline Develop ETL processes for knowledge extraction Implement AI-powered categorization systems Create automated tagging and classification workflows Set up validation and quality control mechanisms Knowledge Transformation Enrich operational data with business context Create relationships between different knowledge components Implement version control and lifecycle management Integration Layer Connect knowledge platform with existing BI tools Enable knowledge discovery through search interfaces Implement role-based access control Create audit trails for knowledge usage AI Readiness Knowledge Componentization Break down complex information into AI-digestible components Create training datasets for AI models Implement RAG (Retrieval Augmented Generation) capabilities Develop knowledge validation workflows AI Integration Set up AI models for knowledge processing Implement machine learning for continuous improvement Create feedback loops for knowledge refinement Enable automated knowledge updates Operational Excellence Monitoring Setup Implement usage tracking and analytics Create performance dashboards Set up alerting for knowledge quality issues Monitor system performance and utilization Governance Implementation Establish knowledge management policies Define roles and responsibilities Create maintenance procedures Implement compliance controls #GenerativeAI #EnterpriseAI #LLMIntegration #AIImplementation #Innovation

𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿𝘀 ... 𝗱𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀. 𝗧𝗵𝗲𝘆 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗵𝗼𝘄 𝘁𝗵𝗲𝘆 𝗶𝗺𝗽𝗿𝗼𝘃𝗲. Some teams stop once they’ve “climbed the mountain.” Targets hit. Problems solved. Metrics green. But in continuous improvement, that’s just base camp. If you stop climbing, gravity takes over. Competition, entropy, complacency. You slide back faster than you expect. That’s why the best practitioners always improve. Here are 𝟭𝟬 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗜 𝘂𝘀𝗲 𝘁𝗼 𝗿𝗲𝗳𝗹𝗲𝗰𝘁 𝗼𝗻 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗶𝘁𝘀𝗲𝗹𝗳 𝗶𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿:  1. How visible are senior leaders at Gemba?  2. How well are standards used as a baseline for improvement (vs. a tool for control)?  3. How well does the organisation embrace a "no problem is a problem" mindset?  4. How well are we "countermeasuring" root causes (vs. "firefighting" symptoms)?  5. How much is continuous improvement a daily habit for everyone?  6. How well is continuous improvement tied to strategy?  7. How well are leaders acting as coaches to grow employees?  8. How "psychologically safe" and honest is the culture in this organisation?  9. How often does visual management drive action? 10. How often do we reflect on our continuous improvement journey? None of these questions are comfortable. That’s the point. Let's remember Jim Collins' stage 1 of decline: Hubris born out of success. Let's stay humble 🙏 Continuous improvement isn’t about reaching the summit. It’s about never confusing progress with arrival. 𝗧𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝘆𝗼𝘂 𝘀𝘁𝗼𝗽 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗻𝗴 𝗼𝗻 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗶𝗺𝗽𝗿𝗼𝘃𝗲, 𝘆𝗼𝘂’𝘃𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝘁𝗵𝗲 𝗱𝗲𝘀𝗰𝗲𝗻𝘁. 📌 Want to 𝗹𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁? Sign up for my newsletter: https://lnkd.in/d3Zmay-H Practical insights for you based on 27 years in Procter & Gamble and Danaher.

Here is how we are preparing marketing teams for MMM. Many CMOs tell me they're implementing MMM, which is fantastic progress. But when I ask about their data architecture, there's often this moment of hesitation - a recognition that the foundation might not be fully in place. "They don't know what they don't know". The reality is that Marketing Mix Modelling requires a proper data infrastructure. Marketers know this for the most part, but practically rolling it out is a challenge if you haven't done it before. The reality is that you will continue to waste media, even if you are using MMM. MMM is not an automatic magic wand. This diagram represents what I call the 'essential plumbing' of media effectiveness to get you MMM ready: 1️⃣ It starts with metadata standardisation - connecting creative data and audience data, against unique campaign IDs across platforms (Google DV360, Meta, Amazon DSP, The Trade Desk, Criteo). These platforms are all black boxes, so you need more control over the data before it enters the #DSP, otherwise reporting and insights downstream will be VERY ordinary. 2️⃣ Your data then needs proper integration - tools like Fivetran help consolidate these data streams into your warehouse (for example, Google #BigQuery) by extracting, transforming, and normalizing data from disparate sources into a consistent format. At this stage, it's still passing the Unique ID we created upfront. It then reconnects and maps data correctly once it lands in your data warehouse. That's the real magic!! 3️⃣ Once structured properly, this foundation enables actionable insights for media briefs, strategy, and performance tracking. This is the utility layer that brings value to MMM. For example, MMM cannot function like it should if there are poor briefs from marketing. While it might not seem like the glamorous part of marketing at first glance, marketers who master this infrastructure are the ones who will consistently win effectiveness awards and earn the respect of their CFOs. This foundation enables the creative and strategic work to be celebrated. What's concerning is that organisations often continue to increase media investment (as reported by IAB) before solving these fundamental data challenges. But these growth stats aren't worth celebrating when we know how much of that increased spend is wasted due to poor data foundations. Then there's genuine surprise when they struggle to demonstrate ROI to financial stakeholders. Before diving into sophisticated attribution models or incrementality testing, I'd encourage you to assess honestly: have you established these data fundamentals? Because without this type of framework, you're essentially measuring inaccuracies with increasingly complex methodologies. Getting this right first changes everything downstream. And here's the real kicker... Most of the upfront work is still being managed by excel spreadsheets 😱

You’ve all seen the recent eMarketer chart saying ~60% of US marketers want better/faster MMM. To achieve this in 2025, I believe we need to focus on solving 3 challenges integral to MMM: 1. Data Standardisation 2. Advanced Modelling 3. Seamless Reporting Let me walk you through what this means and how we can make it a reality. DATA The data phase is where much of the bottleneck in MMM occurs. We all know the pain of delayed projects caused by fragmented or poorly prepared datasets. Clean, consistent, and timely data is essential, and this requires a commitment to a robust taxonomy. Every campaign must follow standardised naming conventions, tagging, and data logging processes. This allows brands to automate data collection and preparation seamlessly. Automation hinges on consistency. When data adheres to a clear structure, it integrates smoothly into modelling systems without requiring constant adjustments. MODELLING Once the data is prepared, the next challenge is ensuring that modelling itself is efficient. Modern MMM must accommodate a spectrum of methodologies - from basic regression to more sophisticated approaches tailored to specific verticals, data granularity, and business objectives. To achieve this, you need an automated modelling engine. Such an engine should be capable of navigating millions of possible model combinations to identify the best fit, balancing both statistical rigour and commercial viability. Incorporating prior knowledge - such as results from experiments, benchmarks, or previous MMM projects - into models ensures greater accuracy and relevance. This step is particularly critical in addressing challenges like insufficient data or low spend levels, which can otherwise lead to misleading conclusions. REPORTING Even with the best data and models, the value of MMM rests on how insights are presented and applied. Marketers need dashboards that are intuitive, dynamic, and prescriptive. These tools should clearly depict response curves, saturation points, and optimal investment levels across channels. To achieve this level of sophistication, reporting tools must be tightly integrated with the data and modelling systems. Fragmented processes - where reporting relies on separate platforms - introduce inefficiencies and delays, undermining the very goal of speed in MMM. - Note that improving just one of these aspects (data collection, modelling, or reporting) is not enough. True efficiency comes from addressing all 3 phases simultaneously. Automating data processes without robust modelling capabilities will still lead to delays. Similarly, having state-of-the-art modelling tools won’t help if data preparation or reporting lags behind. Marketers and analytics directors must view MMM as an interconnected system, where each phase feeds seamlessly into the next. Only by adopting this holistic approach can we dream of faster, more effective MMM solutions.