Nicolas Vandeput

Only a few shine in demand planning. This document shows the 7 habits of a high-performing demand planner: # 1 - Starts with Clean Data ↳ Validates and cleans data before running any forecast # 2 - Balances Art & Science ↳ Uses statistical models as the base, but applies market intelligence to adjust for reality # 3 - Tracks Forecast Accuracy & Bias Religiously ↳ Monitors WMAPE, Bias, and FVA (forecast value added) every cycle. Knows where they win and where they miss. # 4 - Builds Strong Cross-Functional Relationships ↳ Engages sales, marketing, and supply early. Takes Forecasting as a team sport. # 5 - Plans for the Known Unknowns ↳ Has a playbook for promotions, product launches, and external shocks # 6 - Reviews & Learns Every Cycle ↳ Post-mortems are standard. Every miss is a lesson for the next forecast. # 7 - Communicates in Business Language ↳ Doesn't just talk numbers but explains the impact on revenue, service, and inventory Any others to add?

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Euler's Identity and it's geometrical interpretation The most beautiful equation in mathematics that combines five of the most important constants of nature: 0, 1, π, e and i, with the three fundamental operations: addition, multiplication and exponentiation. Euler’s identity is an equality found in mathematics that has been compared to a Shakespearean sonnet and described as "the most beautiful equation." It is a special case of a foundational equation in complex arithmetic called Euler’s Formula, which the late great physicist Richard Feynman called in his lectures "our jewel" and "the most remarkable formula in mathematics." The five constants are: 1. The 0( zero) 2. The number 1. 3. The number π, an irrational number (with unending digits) that is the ratio of the circumference of a circle to its diameter. It is approximately 3.14159… 4. The number e, also an irrational number. It is the base of natural logarithms that arises naturally through study of compound interest and calculus. The number e pervades math, appearing seemingly from nowhere in a vast number of important equations. It is approximately 2.71828…. 5. The number i, defined as the square root of negative one: √(-1). The most fundamental of the imaginary numbers, so called because, in reality, no number can be multiplied by itself to produce a negative number (and, therefore, negative numbers have no real square roots). But in math, there are many situations where one is forced to take the square root of a negative. The letter i is therefore used as a sort of stand-in to mark places where this was done. Prolific mathematician Leonhard Euler was an 18th-century Swiss-born mathematician who developed many concepts that are integral to modern mathematics. He spent most of his career in St. Petersburg, Russia. He was one of the most prolific mathematicians of all time, according to the U.S. Naval Academy (USNA), with 886 papers and books published. Much of his output came during the last two decades of his life, when he was totally blind. There was so much work that the St. Petersburg Academy continued publishing his work posthumously for more than 30 years. Euler's important contributions include Euler's Formula and Euler's Theorem, both of which can mean different things depending on the context. According to the USNA, in mechanics, there are "Euler angles (to specify the orientation of a rigid body), Euler's theorem (that every rotation has an axis), Euler's equations for motion of fluids, and the Euler-Lagrange equation (that comes from calculus of variations)." Euler's identity and Euler's formula are deeply embedded in the mathematical framework of quantum physics. They are essential tools for expressing, manipulating, and interpreting complex-valued functions and operators that describe quantum systems.

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Data from multiple manufacturing diffusion index surveys from September suggested a slight strengthening of new order momentum observed in August was ephemeral. Two charts below show seasonally adjusted new orders relative to the prior month. Thoughts: •The top chart shows ISM’s new orders data. New orders fell back into negative territory at 48.9 (values of 50 or more indicate expansion). Informant comments continue to be unkind about tariffs (https://lnkd.in/gqecsayZ). One respondent in transportation equipment stated, “The addition of the derivative steel and aluminum tariffs in the middle of the month — with no announcement — was devastating. Interest-rate lowering or the ‘One Big Beautiful Bill’ will not impact our business, as all capital projects are on hold until there is some level of certainty and customers start to place orders for new equipment again.” Another respondent in electrical equipment, appliances, and components stated, “Customer orders are depressed for heavy machinery because tariffs are so impactful to high-end capital equipment.” •The bottom chart shows the aggregated seasonally adjusted new orders across five Federal Reserve Banks (New York, Philadelphia, Richmond, Kansas City, and Dallas). After rising to slight expansion in August (+3.66), new orders fell negative again in September (-4.56). Responses to the Dallas Fed’s survey https://lnkd.in/gwGYg-zk included a fabricated metal producer stating, “Orders have been put on hold, and we are not receiving any new [purchase orders] since Aug. 15 on open RFQ's [requests for quotation].” A printing and related products firm stated, “Incoming orders have definitely slowed way down, and all we can attribute it to is the crazy tariff-induced environment we are living in. Instead of Washington, D.C. policies increasing business and making it better, they seem to be adding way too much uncertainty and making things worse.” Implication: 2025 should have been the year where manufacturing activity in the USA recovered. Instead, erratic tariff policies have caused all forward momentum that we observed in January to evaporate. For trucking companies, these latest data indicate we will not see the type of sustained increase in freight demand necessary to trigger the next bull market pricing cycle. As I’ve noted previously, Q2 2026 is the soonest we could see a significant shift in pricing dynamics in carriers’ favor (outside of normal seasonal fluctuations). #supplychain #trucking #freight #truckload #logistics #transportation     

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𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬: 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧𝐬 𝐅𝐫𝐨𝐦 𝐖𝐢𝐭𝐡𝐢𝐧 (𝐏𝐚𝐫𝐭 𝟐 / 𝟐) Invisible costs and hidden inefficiencies hitting your supply chain hard? In this continuing piece, Arielle Rolland and Alexandre Gontcharov tackle the critical role of Data Products in managing "the rare". These “Edge Cases” can have a significant impact on your business. Taking a cue from Part 1, our community authors explore how Data Products can unify fragmented systems and enable supply chains to become not just reactive, but proactive and resilient. Discover why edge cases are not just anomalies but symptoms of a larger, systemic issue that can be solved with a strategic approach to data. ↩️ 𝐖𝐡𝐚𝐭’𝐬 𝐈𝐧𝐬𝐢𝐝𝐞? ✅ 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐚𝐧 𝐄𝐝𝐠𝐞 𝐂𝐚𝐬𝐞 Understand why these seemingly rare events are far more common and costly than you think. ✅ 𝐖𝐡𝐲 𝐒𝐨 𝐌𝐚𝐧𝐲 𝐄𝐝𝐠𝐞 𝐂𝐚𝐬𝐞𝐬 Explore how digitalisation and fragmented systems make edge cases inevitable and hard to spot. ✅ 𝐔𝐧𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 Discover how data products provide a consistent, trusted layer for analysis by unifying fragmented data. Learn how a clear, shared data model uncovers and quantifies hidden scenarios, shifting teams from reactive workarounds to proactive resolution. ✅ 𝐅𝐫𝐨𝐦 𝐅𝐢𝐫𝐞𝐟𝐢𝐠𝐡𝐭𝐢𝐧𝐠 𝐭𝐨 𝐅𝐨𝐫𝐞𝐬𝐢𝐠𝐡𝐭 Understand how a strategic data approach builds resilience from within, enabling faster decisions and more resilient operations. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐘𝐨𝐮𝐫 𝐎𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧: Many organisations overlook the significant, hidden costs driven by edge cases, from logistics and manufacturing to sales. This piece provides a comprehensive blueprint for using Data Products to make the invisible visible, giving leaders a unified view of the full value stream and enabling more effective cross-functional decision-making. Ready to build a supply chain that thrives on complexity? Dive into this piece and learn how to use Data Products to transform hidden issues into a competitive advantage! ➡️ 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐡𝐞𝐫𝐞: https://lnkd.in/dtcbawAv 🗣️ 𝐂𝐚𝐥𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐄𝐱𝐩𝐞𝐫𝐭𝐬! 𝐀𝐭 Modern Data 101, we collaborate with industry leaders to bring top-tier insights to a thriving data community. Have a unique perspective to share? We’re all ears! (All submissions are vetted for quality & relevance.) 🔔 Follow Modern Data 101 and stay updated with our weekly highlights from the modern data space.

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🚀 Big day for supply chain and freight tech acquisitions! First, in returns software: Optoro has been acquired by Blue Yonder. Then, in the ongoing convergence of freight and finance tech: Denim has joined forces with our customer, Truckstop.com. Add in Overhaul and FreightVerify and it’s clear that consolidators are springing into action this year. At Catena Clearing, we see all of this innovation reinforced by one critical layer: telematics data. It feeds the workflows powering returns, payments, compliance, and risk and it’s why we’re on pace for our strongest quarter yet, likely signing 15 new customers.

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80% of companies admit to major gaps in data preparation while investing heavily in AI. Most are about to waste money on tools that can't access the data they need. DHL learned this the hard way. Their AI voicebot kept missing "Ja" - the German word for "yes." One tiny data processing failure taught them everything about infrastructure gaps. Today, that same voicebot handles a million calls monthly, resolving half without human intervention. The difference? They fixed the plumbing first. Coca-Cola took a different approach from day one. No 50,000-person AI bootcamp. No consultant army. They created a sandbox with exactly six people: legal for data governance, communications for internal messaging, and tech for sandbox infrastructure. This specific combination works because legal sets data boundaries, comms manages change resistance, and tech builds safe testing environments. That sandbox birthed Create Real Magic and showed the company what was possible before anyone bet serious money. Here's what works: 1. Fix your data infrastructure before buying AI tools. DHL's "Ja" problem shows why data quality matters more than model sophistication. 2. Build your own six-person team. Legal handles data access and compliance. Communications manages internal expectations and resistance. Tech creates isolated testing environments with real but low-stakes data. 3. Use two-way doors for decisions. Reversible experiments get approved fast. Irreversible bets get reviewed thoroughly. Don't let irreversible review processes slow every reversible idea. Both companies treated AI as infrastructure, not press releases. They built working systems instead of making announcements. Every organization sits somewhere on a simple grid: infrastructure readiness multiplied by urgency. I've mapped this into four zones (see chart below): - Stagnation (low urgency/low readiness): drifting toward irrelevance - Complacency (low urgency/high readiness): resources without motivation - Frustration (high urgency/low readiness): action without infrastructure - Innovation (high urgency/high readiness): where transformation happens As Cisco's Jeetu Patel warns: "Eventually there will be only two kinds of companies: those that are AI companies, and those that are irrelevant." My article is just one of many thought-provoking contributions in this I by IMD issue. The whole magazine is packed with ideas worth exploring (link in comments). P.S. Comment below saying 'free trial please' and I'll DM you the QR code for 3-month free access to our school's I by IMD Magazine.

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