Data Analytics in Supply Chain Decisions

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!

𝐃𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐭𝐡𝐚𝐭 𝐠𝐥𝐨𝐛𝐚𝐥 𝐦𝐨𝐛𝐢𝐥𝐞 𝐝𝐚𝐭𝐚 𝐭𝐫𝐚𝐟𝐟𝐢𝐜 𝐢𝐬 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐭𝐨 𝐫𝐞𝐚𝐜𝐡 𝐚 𝐬𝐭𝐚𝐠𝐠𝐞𝐫𝐢𝐧𝐠 77.5 𝐞𝐱𝐚𝐛𝐲𝐭𝐞𝐬 𝐩𝐞𝐫 𝐦𝐨𝐧𝐭𝐡 𝐛𝐲 2027? This explosion of data presents both a challenge and a massive opportunity for telecommunication companies. But are they equipped to handle it? The telecommunications industry is undergoing a seismic shift. Why should you care? Because this transformation impacts how we connect, communicate, and experience the digital world. A recent study showed that poor network performance can lead to a 30% increase in customer churn. 👉 In today's hyper-connected world, customer expectations are higher than ever, and telcos need to leverage data to stay ahead of the curve. 👉 Traditional data management systems struggle to keep pace with the sheer volume, velocity, and variety of data generated by modern telecom networks. Sifting through massive datasets to gain actionable insights is like finding a needle in a haystack. 👉 This makes it difficult to optimize network performance, personalize customer experiences, and develop innovative new services. Telcos need a new approach to data management to unlock the true potential of their data. 𝐓𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧? 👉 Deutsche Telekom, one of the world's leading telecommunications providers, is leading the charge by designing the telco of tomorrow with BigQuery. 👉 By leveraging BigQuery's powerful data warehousing and analytics capabilities, Deutsche Telekom is able to ingest and analyze massive datasets in real time. This enables them to gain valuable insights into network performance, customer behavior, and market trends. 👉 They can now proactively identify and resolve network issues, personalize offers and services for individual customers, and develop new revenue streams. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 👉 Real-time Insights: BigQuery enables real-time analysis of massive datasets, allowing telcos to react quickly to changing network conditions & customer needs. 👉 Improved Customer Experience: By understanding customer behavior and preferences, telcos can personalize services and offers, leading to increased customer satisfaction and loyalty. 👉 Innovation & Growth: Access to rich data insights empowers telcos to develop innovative new services & explore new business models. 👉 Scalability & Flexibility: Cloud-based solutions like BigQuery offer the scalability and flexibility needed to handle the ever-growing data demands of the telecommunications industry. This journey highlights the transformative power of data in the telecommunications industry. By embracing cloud-based data solutions, telcos can unlock valuable insights, improve customer experiences & drive innovation. The future of telecom is data-driven, and companies that embrace this reality will be the leaders of tomorrow. Follow Omkar Sawant for more. #telecommunications #bigdata #cloud #digitaltransformation #datanalytics

Accounting ≠ forecasting. Accounting reports the numbers. But in FP&A we use mapping and flexible models to make sense of the figures and plan for what's next. (1) Accounting systems are too detailed The core of accounting is the chart of accounts. It tracks every single transaction to the ledger, which then flows to the trial balance and financial statements. They're critical for tracing back to vendor and customer activities, vouching, and audits. But FP&A hardly ever needs that much detail. We don't need 57 different accounts for travel and entertainment for forecasting. We need one or two for the P&L. (2) Why mapping is essential In the example you see here, this entire P&L is mapped back to the chart of accounts, which is grouped into classes. While there may be 57 different accounts for travel and entertainment, they're all rolled up into a limited number of buckets. Each can be mapped to a single line within marketing expense and a single line within sales expense. Mapping creates the bridge between the detail of accounting and the insight of FP&A (3) Rolling forecasts connect the two Every month, this model can automatically import the data from Netsuite, Quickbooks, or other accounting software. Driven by dates at D3 and D4, the actuals from the system get added. The forecast then rolls automatically into the next month using a dynamic array. While some financial modelers may use historical run rates, it's smart to combine an analysis of run rates with driver-based assumptions. For example, travel and entertaining may be based upon the average spend over the past 6 months. But it may also be tied to promotional campaigns, trade shows, and other known initiatives. (4) Dynamic arrays make this possible I've been very vocal about my love of dynamic arrays for operational models. What used to take hours and days many years ago, now takes seconds or minutes. Functions like FILTER, UNIQUE, SEQUENCE, LET, and CHOOSEROWS make this all possible. The model can adapt instantly when new GL accounts or departments are added. Helper columns are no longer necessary. And we can build a formula once and have it live forever, never having to worry that it'll break. 💡 More than 2,000 people registered for the last "Advanced FP&A: Financial Modeling with Dynamic Excel". If you'd like to join the next one, you can here. https://lnkd.in/eT3XTmMk

Machine learning beats traditional forecasting methods in multi series forecasting. In one of the latest M forecasting competitions, the aim was to advance what we know about time series forecasting methods and strategies. Competitors had to forecast 40k+ time series representing sales for the largest retail company in the world by revenue: Walmart. These are the main findings: ▶️ Performance of ML Methods: Machine learning (ML) models demonstrate superior accuracy compared to simple statistical methods. Hybrid approaches that combine ML techniques with statistical functionalities often yield effective results. Advanced ML methods, such as LightGBM and deep learning techniques, have shown significant forecasting potential. ▶️ Value of Combining Forecasts: Combining forecasts from various methods enhances accuracy. Even simple, equal-weighted combinations of models can outperform more complex approaches, reaffirming the effectiveness of ensemble strategies. ▶️ Cross-Learning Benefits: Utilizing cross-learning from correlated, hierarchical data improves forecasting accuracy. In short, one model to forecast thousands of time series. This approach allows for more efficient training and reduces computational costs, making it a valuable strategy. ▶️ Differences in Performance: Winning methods often outperform traditional benchmarks significantly. However, many teams may not surpass the performance of simpler methods, indicating that straightforward approaches can still be effective. Impact of External Adjustments: Incorporating external adjustments (ie, data based insight) can enhance forecast accuracy. ▶️ Importance of Cross-Validation Strategies: Effective cross-validation (CV) strategies are crucial for accurately assessing forecasting methods. Many teams fail to select the best forecasts due to inadequate CV methods. Utilizing extensive validation techniques can ensure robustness. ▶️ Role of Exogenous Variables: Including exogenous/explanatory variables significantly improves forecasting accuracy. Additional data such as promotions and price changes can lead to substantial improvements over models that rely solely on historical data. Overall, these findings emphasize the effectiveness of ML methods, the value of combining forecasts, and the importance of incorporating external factors and robust validation strategies in forecasting. If you haven’t already, try using machine learning models to forecast your future challenge 🙂 Read the article 👉 https://buff.ly/3O95gQp

Startups often begin with a vision, a strong belief in an idea, and a gut feeling about the market. But scaling a startup requires more than intuition—it demands data-driven decisions that guide product development, customer retention, and revenue growth. 1. Finding Product-Market Fit with Data Instead of guessing what customers want, successful startups: ✅ Analyze user behavior—Which features get the most engagement? Where do users drop off? ✅ Use A/B testing—Test different versions of features, landing pages, or pricing models to see what resonates. ✅ Leverage surveys & feedback loops—Direct customer insights can validate assumptions and refine offerings. 2. Boosting Customer Retention with Data Analytics Acquiring new customers is expensive, but retaining them is key to sustainable growth. Data helps startups: 🔹 Segment customers—Identify high-value users and personalize their experiences. 🔹 Predict churn—Spot patterns that indicate when a customer is about to leave and intervene proactively. 🔹 Optimize onboarding—Track friction points in the user journey and improve the first-time experience. 3. Optimizing Revenue and Monetization Strategies Startups must experiment with revenue models to maximize profitability. Data helps by: 📊 Identifying profitable pricing strategies—Analyzing purchase behavior to adjust pricing tiers. 📈 Tracking customer lifetime value (LTV)—Ensuring the cost of acquiring a customer (CAC) is justified. 💡 Experimenting with revenue streams—Using insights to explore upsells, subscriptions, or partnerships. The Bottom Line? Data Wins. Relying solely on intuition can be risky. Combining gut instinct with real-world analytics creates a powerful engine for scalable, smart growth. 𝑾𝒉𝒂𝒕’𝒔 𝒐𝒏𝒆 𝒘𝒂𝒚 𝒚𝒐𝒖𝒓 𝒔𝒕𝒂𝒓𝒕𝒖𝒑 𝒉𝒂𝒔 𝒖𝒔𝒆𝒅 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒎𝒂𝒌𝒆 𝒔𝒎𝒂𝒓𝒕𝒆𝒓 𝒅𝒆𝒄𝒊𝒔𝒊𝒐𝒏𝒔? 𝑫𝒓𝒐𝒑 𝒚𝒐𝒖𝒓 𝒕𝒉𝒐𝒖𝒈𝒉𝒕𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #DataDrivenDecisionMaking #StartupEcosystem #Startups #StartupScaling

🚢 Transforming Logistics with AI: Key Insights from Windward’s Customers 📊 Over the past few years, Windward Ocean Freight Visibility (OFV) has supported key players in the logistics industry. While there are several solutions in the market , and while all of our customers have a world class TMS with some visibility data , we wanted to provide a tangible guide for customers to understand in a quantifiable , clear way, the benefits of our products for their operations.. Here’s a summary of the key benefits our customers have experienced: • Accurate Routing Information: Improved milestone coverage to 91%, reducing transshipment delays by over 30%. • Reliable ETA Predictions: Windward’s Maritime AI™ delivers 15% more accurate ETAs, enhancing decision-making and reducing unexpected delays by 25%. • Real-Time Data Updates: Fresh data every 10 hours, ensuring operators always have the latest information, cutting down outdated data-related errors by 40%. • Comprehensive Milestone Tracking: Complete visibility over ATD and ATA data for all shipment legs, improving tracking accuracy by 35%. • Enhanced Data Quality: Automatic error notifications and validation for booking references, BOLs, and container numbers, decreasing data entry errors by 20%. • Streamlined Communication: Shareable shipment pages providing a single source of truth for all stakeholders, increasing customer satisfaction by 50%. Discover how these insights can optimize your logistics operations and drive success. 📈 Boost operational efficiency 🌐 Achieve better strategic planning 🤝 Enhance customer satisfaction Ready to leverage the power of AI for your logistics? Let’s talk ! Read more here: https://lnkd.in/e7HMbxAs #Logistics #SupplyChain #AI #DataManagement #WindwardAI Windward

"How Penske #Logistics Transforms Fleet Intelligence with #DataStreaming and #AI" Real-time visibility is no longer a luxury in logistics—it’s a business-critical necessity. As global supply chains grow more complex and customer expectations rise, logistics and transportation providers must move away from delayed, static data pipelines. Data Streaming with technologies like #ApacheKafka and #ApacheFlink enables logistics companies to capture, process, and act on streaming data the moment it’s generated. From telematics and sensor data to inventory and ERP systems, every event can drive a smarter, faster response. A standout example is #PenskeLogistics. With over 400,000 vehicles in its fleet, Penske Logistics uses Confluent's fully-managed Kafka service to process 190M+ IoT events daily. Their platform powers real-time fleet health monitoring, predictive maintenance, automated compliance, and enhanced customer experiences. This shift to #EventDrivenArchitecture is not theoretical. Leading companies across the supply chain—LKW Walter, Uber Freight, Instacart, Maersk—are deploying similar architectures to modernize their operations. Penske’s journey is especially impressive. They’ve avoided over 90,000 roadside incidents through real-time diagnostics and predictive alerts. AI-powered tools further accelerate response times and improve uptime across the fleet. And this is just the beginning. As EVs and autonomous vehicles increase, the volume of edge data will grow exponentially. Penske is already scaling its platform to prepare—and combining Kafka with AI to deliver real-time, intelligent automation. Want to learn more? Check out my latest blog post: https://lnkd.in/e4fUWvXw

Most companies don’t actually know their customers as well as they think they do. 🤔 In a world where personalization reigns supreme, direct-to-consumer brands have no excuse not to be data-driven. Here's the harsh truth: without deep insights into customer behavior, you're leaving opportunities (and revenue!) on the table. 🚪💸 Let me paint a picture: One of our clients, a fintech company, wanted to boost the adoption of their premium app features. They had amazing functionality, but the uptake just wasn’t there. Using Fivvy, we identified the right users—those already engaging with competitive apps and showing behaviors that indicated premium preferences. The result? An 18% increase in premium feature adoption and 7% less churn in this segment. Here’s the kicker: it wasn’t about spamming all users. It was about precision. Personalized push notifications and targeting worked because we truly knew their audience. 🎯 In the age of abundant data, the winners are the companies that leverage it smartly—not just to sell but to add real value to their customers' lives. What’s stopping your company from becoming truly customer-centric? #CustomerExperience #DataDriven #Fintech #CustomerSuccess #DirectToConsumer #Fivvy

𝗪𝗵𝗮𝘁 𝘆𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝘂𝗻𝘃𝗲𝗶𝗹𝘀 𝗮𝗯𝗼𝘂𝘁 𝘆𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 Ever wondered about the true maturity of your procurement organisation? 𝗛𝗲𝗿𝗲'𝘀 𝗮 𝗵𝗶𝗻𝘁: 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗵𝗼𝘄 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗺𝗮𝗻𝗮𝗴𝗲𝗱 𝗮𝗻𝗱 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲𝗱. It's indicative of your ability to generate comprehensive strategies and plans with a big picture in mind and the ability to perform your operations effectively. Here a few pointers giving away your attitude towards facts-driven decisions and your ability to learn from your past and project your future: 1️⃣ 𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘀𝗲𝗱 𝘃𝘀 𝘀𝗰𝗮𝘁𝘁𝗲𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 Is your data centralised in a common data hub or scattered across various platforms such as your S2P, ERP and best of breed solutions? Centralised data indicates streamlined operations and higher maturity. 2️⃣ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘃𝘀 𝘀𝘁𝗮𝘁𝗶𝗰 𝗿𝗲𝗽𝗼𝗿𝘁𝘀 Are you leveraging real-time insights, or are you stuck with static reports? Access to real-time data proofs a dynamic & forward-thinking approach. 3️⃣ 𝗗𝗮𝘁𝗮 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 Can your team easily access good quality data for quick decision-making, or does it take days of manual searching, cleansing & compiling? High quality data & ease of access points at higher levels of efficiency 4️⃣ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜 Is your data integrated with advanced analytics tools for deeper insights or AI solutions for continuous learning, or are you still relying on basic spreadsheets? The use of advanced tools could hint at higher levels of insights & maturity. 5️⃣ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 & 𝗣𝗿𝗲𝗱𝗶𝗰𝘁 𝘃𝘀 𝗪𝗮𝗶𝘁 𝗮𝗻𝗱 𝗥𝗲𝗮𝗰𝘁: Are you learning from historical data and using forecasting & prediction to identify possible future opportunities & risks or only reacting to new situations. Learning from the past and modelling future scenarios show a strategic mindset. 6️⃣ 𝗗𝗮𝘁𝗮 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 Is there a clear data standards, processes, roles & accountabilities established? Defined data ownership and governance processes reflect in higher maturity. 𝗜𝗻 𝗲𝘀𝘀𝗲𝗻𝗰𝗲, 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘁𝗼𝗿𝗲𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 𝟬 𝗮𝗻𝗱 𝟭'𝘀. It showcases your operational efficiency, agility, and strategic foresight and your ability to continuously learn and keep your house in order. ❓How else does your data reflect your Procurement maturity #procurement #datagovernance #procurementexcellence #maturitymodel #digitaltransformation

Gartner has a new case study on unlocking value from data 🚀 🌐 A leading oil and lubricants company faced challenges with a complex data landscape. 📊 This included multiple data sources, inconsistent reporting, and poor data quality. 📈 To address these issues, they implemented a modern data and analytics strategy. 🎯 The Solution 🌊 Harmonized Data Architecture: Established a unified logical data model aligned with a global data lake 🌎 Data Quality and Governance: Introduced a global data quality management strategy across operations 🔄 Data Value Chain: Enabled seamless data lineage for automated normalization 📊 The Results 🕒 Efficiency: Reduced human effort by over 90% 📈 Data Quality: Improved through local governance based on global KPIs. 🚀 Scalability: Enhanced ability to introduce new technologies quickly. 🌟 By harmonizing data architecture and focusing on quality, organizations can unlock value delivery and achieve a data-driven culture. 💪 This approach improves decision-making, operational efficiency, and competitiveness. Their work continues as the core team paves the way for further maturing its data lake, global data model, and data management and #analytics capabilities. Gartner clients who subscribe to our AI and Emerging Technologies topic in Digital Supply Chain Value Realization should check out the 🔗 link in the comments to read the full: ℹ️ Case Study: Data & Analytics Intelligence to Unlock Value Delivery From my colleagues Christian Titze and Leonard Ammerer. Great work on this case study, gentlemen.