Strategic Industry Analysis

How to Master Industry Research? Industry Research is the backbone of Financial Analysis Here is my 7-step checklist for you! Before starting - If you are a beginner, pick up a relatively simpler industry. Something you understand and deal with on a day to day basis (2-wheelers, FMCG) - As you start understanding some of the simpler industries, move to more complex ones such as Cement, Steel etc. Now for the steps ✅ Step 1: Understand the industry Value Chain - Super critical step - Understand how the industry makes and spends money? - What is the production or service delivery process - Who are the suppliers and buyers? - For example - Steel making uses Iron Ore and Coking Coal as Raw Materials  - Helps understand if some company has captive mines or control over parts of the value chain. ✅ Step 2: Estimating Demand Supply  - Find the size of the industry - For example - what is the steel capacity and what is the steel production ✅ Step 3: Who are the major players? - Market share of major players - Sales, profits, margins and market cap of the top 3-4 players - Competitive strength (Could be visible in better margins for example) ✅ Step 4: A deeper analysis of P&L and Balance Sheet - What are the major costs - Does the business need a lot of debt - How much does it take to put up a unit of capacity ✅ Step 5: What are the major drivers? - Why will the industry grow - Opportunity size (how big can the industry be) ✅ Step 6: Player Behavior and Industry Structure - Is the industry a monopoly / oligopoly / perfect competition? - How do players establish control - price, branding? ✅ Step 7: Government Policy - Government regulation for the industry and its value chain - Likelihood of positive or negative action by the government These 7 steps will ensure a very solid coverage and understanding of the sector. ---  I teach practical #finance concepts through my writing and courses. Check out my earlier posts, and follow me (Peeyush) to stay tuned 🔔 for future posts.

I’m pleased to share the Kingdom’s success story in the aluminum industry—now one of the key pillars powering sectors such as electric vehicles, aviation, and renewable energy. The journey begins at the Al Baitha mine in Qassim, which produces 5 million tons of bauxite annually. From there, it travels through the Kingdom’s railway network to Ras Al Khair Industrial City, where the transformation process unfolds: • From the alumina refinery with a capacity of 1.8 million tons annually • To the smelter with a capacity of 820,000 tons annually • Then to the casthouse with a capacity of 1 million tons annually • Supported by aluminum scrap recycling furnaces with a capacity of 100,000 tons annually • Integrated with the rolling mill that has a production capacity 460,000 tons annually. Together, these operations form Ma’aden’s fully integrated aluminum complex in Ras Al Khair—the largest of its kind worldwide, taking aluminum production from mine to market. One of its most notable milestones is supplying aluminum sheets to major European automotive manufacturers. The ecosystem extends further, supporting the production of 1 mn tons of downstream Aluminum including: • Extrusion plants serving the Kingdom’s mega construction projects needs of doors and window frames • ⁠Castings plants • Electrical cable factories • Beverages Cans and packaging This strong foundation is delivering high-quality, value-added products, strengthening global confidence in Saudi capabilities, and positioning the Kingdom as a true hub for mining and industry. Beyond industrial impact, it is also creating thousands of jobs and enhancing the competitiveness of the national economy.

The UAE Produces Roughly 1 Out Of Every 25 Tonnes Of Aluminium On Earth According to Emirates Global Aluminium (EGA): → The UAE produces 4% of global aluminium → Serving 400+ customers across 50+ countries → Selling 2.83M tonnes in 2025 → Generating AED 31.98B ($8.71B) in revenue For perspective: → Global aluminium production is 74M tonnes annually → UAE population: 11.5M people Yet the UAE still sits inside the top tier of global aluminium production alongside: → China → India → Russia → Canada Very few countries of this size possess this level of industrial relevance globally. Major export markets include: → Turkey → European Union → United States → Japan → Norway The UAE did not inherit an aluminium advantage. It engineered one. Despite lacking: → Large bauxite reserves → A traditional industrial base the UAE built industrial strength through: → Energy infrastructure → Sovereign investment → Ports & logistics → Industrial zones → Global trade connectivity EGA has now produced: → 50+ million tonnes of cast metal since 1979 What took many industrial economies over a century… the UAE compressed into decades. But this story is much bigger than aluminium. Aluminium increasingly sits inside: → AI data centers → Electric vehicles → Renewable energy → Aerospace → Industrial infrastructure The AI race is no longer only about: → Chips → Models → Software It is increasingly about: → Electricity → Cooling → Grid infrastructure → Physical compute systems According to the IEA: → Data center electricity demand could reach 945 TWh by 2030 Meaning: AI increasingly scales through physical infrastructure. AI → Compute → Electricity → Infrastructure → Materials And aluminium sits deeply across that chain. The deeper layer: Aluminium is essentially converted energy. The UAE is no longer exporting only energy. It is increasingly converting energy into: → Industrial products → Infrastructure materials → Global manufacturing value chains Especially as recycled aluminium can require: → 95% less energy than primary production. At the same time, the UAE is reducing dependence on: → Oil volatility → Pure hydrocarbon exports by building: → Industrial exports → Manufacturing capability → Strategic materials relevance And none of this would have been possible without: → Jebel Ali → Khalifa Port → Global shipping corridors Positioned between: → Asia → Europe → Africa the UAE is increasingly becoming: → A trusted industrial hub → A logistics connector → A globally integrated infrastructure platform This is the real UAE story: A nation of just 11.5 million people building systems with global-scale influence. Energy → AI → Logistics → Infrastructure → Manufacturing → Capital One integrated national architecture. Transforming the UAE into one of the world’s most strategically connected economies. If excellence had a flag, it would be the UAE’s.

If You are running an omnichannel brand, one of the most actionable and impactful analysis that you can do with your data is look at the ratio of online to offline sales, benchmarked against your national average. You can cut it by city/state/product/SKU and each cut tells you something different. Start by establishing your national average online/offline ratio. Say it's 45:55. Now look at every city, state, and product model against that baseline. Few scenarios: Scenario 1: Higher-than-average online share (say 80:20 in a city where the national average is 45:55) = distribution problem, not a demand problem Consumers want your product and that is evident from your online sales. To buy your product, they are waiting for delivery and forgoing the in-store experience. Your brand has demand in that market. What needs improvement is availability, visibility and advocacy in retail counters. Every rupee you invest in distribution here has a higher probability of generating returns because demand is pre-validated Scenario 2: Lower-than-average online share (say 10:90 in a state) = one of two things, and you need to figure out which. Either your offline distribution is so strong there that consumers don’t have too many reasons to buy online, which is the healthy version, and you'll see it reflected in strong secondary sales numbers. Or your brand simply don’t have demand/PMF and consumers aren't searching for you online or finding you offline. The way you distinguish between the two: check absolute volume. If the 20:80 market is also a high-absolute-volume market, your offline game is strong and the low online share is a sign of distribution maturity. If it's a low-absolute-volume market with a low online share, you have a brand salience and demand problem. And trying to pressurize Distributors and sales team will not work. In fact it will only lead to more churn which will further reduce the sales volume in that geography. Here the Product and marketing team needs to get to work and solve for product market fit and brand salience in that geography. Now apply the same logic at the model level. If a specific SKU has a 50:50 online/offline split nationally while the rest of your portfolio sits at 30:70, that SKU is under-distributed relative to its demand. Retailers either aren't stocking it, don't know it exists, or aren't being incentivised to push it. This is an assortment and trade marketing problem, not a product problem The beauty of this ratio is its simplicity. You don't need a sophisticated data platform to compute it. You need your e-commerce order data by pincode and your secondary sales data by pincode, both of which any omnichannel brand will always have. One simple table gives you the diagnostic. The ratio doesn't tell you why a market is over- or under-indexed. But it tells you where to look, and whether the problem is distribution, brand, or product. And that's usually enough to make the next decision.

Businesses almost always overestimate short-term risks and underestimate long-term change. The biggest risks aren’t the ones making headlines today—they’re the ones quietly reshaping markets over time. Businesses fixate on immediate threats, scrambling for quick fixes, while missing the deeper shifts happening underneath. Real disruption is a long-term shift in who consumes and how. Look at how this plays out: ➡️ The alcohol industry is panicking over GLP-1s. But men drink three times more than women, and women take 80% of GLP-1s. The real risk isn’t an overnight sales drop—it’s a fundamental change in consumption patterns. ➡️ HFSS rules and UPF debates feel like compliance headaches, but they signal a bigger shift in how consumers think about food, regulation, and health. But short term sales targets cloud judgements. ➡️ Businesses treated Trump’s first presidency as a short-term shock—until it reshaped global trade, taxation, and corporate strategy. Now he’s back. How many companies are actually prepared? ➡️ Supply chain shocks, inflation, and interest rates have rewritten cost structures, yet many businesses still operate as if “normal” will return. The mistake? Thinking about risk as an event, not a system. Most businesses react to immediate threats but fail to prepare for structural change. If you're running a food business, ask yourself: ❓Are we reacting to headlines, or modeling long-term demand shifts? ❓Are we pricing in volatility, or assuming stability will return? ❓Are we playing defense against policy changes, or building a model that thrives in any environment? The companies that win aren’t the ones that avoid risk—they’re the ones that absorb it, adapt to it, and turn it into an advantage.

The latest study from the Council of Economic Advisers, The White House states that ~10% of jobs are vulnerable to AI disruption. That may seem alarming, but let’s take a step back. In 2018, 60% of the jobs Americans held didn't even exist in 1940—created by technologies that emerged over the years (David Autor). Here’s the real concern: Many AI-vulnerable jobs haven’t evolved to match their increasing complexity. Workers in these roles are more exposed to disruption because they haven’t been given the chance to upskill. But this isn't new. Economic evolution is the hallmark of a dynamic economy. Just like we’ve adapted to past technologies, workers and industries will adapt to AI. The key lies in how we approach it. Why businesses should care: Organizations that proactively identify and support employees vulnerable to AI disruption aren’t just doing good—they’re making smart financial decisions. 💡 Investing in upskilling and mobility for these workers could unlock millions in retention and productivity. Mass layoffs due to AI aren’t likely. The real shift? Slower hiring and reduced demand for certain roles. We’re already seeing fewer job postings for writers, coders, and even artists. So, what activities are at risk? Roles involved in processing information, analyzing data, scheduling, and administrative tasks are prime targets. Industries to watch? Architecture, engineering, legal, computer science, and mathematics. Surprising jobs at risk of AI disruption: Airline Pilots, Copilots, and Flight Engineers Nuclear Power Reactor Operators Private Detectives and Investigators Commercial and Industrial Designers These highly specialized roles, which traditionally require significant human judgment, are surprisingly vulnerable to AI-driven changes. Business leaders, what barriers are preventing you from launching upskilling initiatives to future-proof your workforce? The future of work is evolving, but we can shape how it unfolds. #FutureOfWork #AIandJobs #Upskilling #WorkforceTransformation #AI

I tried to look for this and couldn't believe it didn't exist. We know every industry is getting hit hard with change but how are the companies within each industry actually responding? We have disruption indices that tell you how fast things are moving. We have readiness surveys where executives grade their own homework. But, nobody is measuring the gap between the two. So I built it. 🤓 The Hyder Index tracks 15 US industries across six change signals (customers, talent, policy tailwinds, money, culture, and disruptions) and five response indicators (earnings call language, strategic hiring, capital reallocation, product activity, and response timing). Every score is based on publicly verifiable data. No surveys. No self-assessments. What companies are doing, not what they say they are doing. A score of 50 means equilibrium. Higher means the gap is widening. Some of what I found: - No industry scored in the Green Zone. Not one. - Technology & AI leads at 81. Not because tech companies are unaware, but because the pace of change is so extreme that even active responses are falling behind. - Media & Entertainment scored 79. Google search traffic to publishers is down 33%. 17,000+ jobs cut. And two-thirds of publishers report zero efficiency gains from their AI efforts. - Education has the lowest response score (15) of any industry. 89% of students are using AI for coursework, yet the average institution takes 12 to 18 months to set an AI policy. By the time the policy is live, the technology has already evolved. - The closest to equilibrium? Hospitality at 49. The pandemic forced the industry to build organizational muscle for rapid change. That survival muscle is still paying dividends today. This is Version 1.0. The methodology is completely transparent. I expect people to push back on scores, and I welcome it. That is how the data gets better. Please feel free to geek out with me on this so I don't feel so alone! But here is what I know after 17 years of advising companies across six continents: most companies do not fail because they miss the change coming. They fail because they respond to it without the right aim or the right timing. The Hyder Index measures that gap. Check it out here: hyderindex.com I will be publishing monthly updates and industry deep dives. If your industry is in here, I would love to hear whether the score matches what you are seeing on the ground. (And if you want me to bring this data to your executive team or your next main stage, lemme know. It’s been really fun so far.) What industry are you most curious about? #HyderIndex #StrategicUrgency #FutureOfWork #BusinessStrategy

Why are some cities innovation powerhouses while others lag behind? This recent study uses 100+ years of U.S. patent data to uncover fractal and scaling patterns that explain how innovation clusters, spreads, and self-organizes in space. By combining patent records with geometric network modeling, the research shows that inventor activity forms fractal clusters, follows Zipf-like scaling, and reaches a peak “inequality horizon” at ~20 km where clustering and diffusion balance. The takeaway: innovation isn’t driven only by major hubs like Silicon Valley - it emerges from the geometry of human connections. Strengthening regional links, not just concentrating growth in large cities, can support more equitable and resilient innovation ecosystems. More: https://lnkd.in/dMEF_3MP #365Papers #Day332 —-------------------------------------------------------- 🌍 𝐋𝐞𝐚𝐫𝐧 𝐠𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐦𝐞: https://lnkd.in/d4spRwNA

I have used Porter’s Five Forces (1980) for decades in my work as a board member and executive educator. When applied correctly, they are as useful as ever—especially in well-defined industries, where - suppliers and customers are identifiable - value chains are relatively clear - profit pools can be traced. The framework becomes harder to apply when ecosystems compete with ecosystems, platforms blur industry boundaries, and competitive forces are more difficult to diagnose. Within a well-defined industry, attractiveness declines when the following forces are strong: - Threat of new entrants: How easy is it for newcomers to erode profits by entering the game? - Bargaining power of suppliers: How much value can upstream players extract from the industry? - Bargaining power of buyers: How easily can customers push prices down or demand more? - Threat of substitutes: How many alternative ways exist to solve the same customer problem? - Rivalry among existing competitors: How intensely do incumbents fight over the same profit pool? How great strategists use the Five Forces framework: Level 1: Assess current industry attractiveness and define strategy accordingly. Level 2: Anticipate how each force will evolve and build relevant capabilities and resources ahead of the competition. Level 3: Actively reshape the industry by weakening the forces: (1) raise entry barriers (2) reduce supplier power (3) reduce customer power (4) limit substitutability (5) nurture “good” competitors and weaken “bad” ones. Old framework. Timeless strategic relevance. Source: Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. New York, NY: Free Press. Repost if you agree. Comment if you don’t. Follow me for more reframing.

Is your digital transformation destined to become another statistic? A staggering 70% of software implementations fail. Think about that. All the budget, the planning, and the effort... wasted. But the reason they fail isn't the technology. It's the rollout strategy. The "big-bang" launch, where you try to convince everyone at once, is doomed. Why? Because it defies a fundamental law of human behavior. You're trying to sell a new vision to a skeptical majority who are wired to resist change and demand proof. This approach erodes trust and leaves you with fuzzy metrics that can't prove a win. So, what's the solution? Stop fighting human nature. Leverage it. The Law of Diffusion of Innovations provides the blueprint. To achieve mass success, you must first win over the hearts and minds of your innovators and early adopters (the first ~16% of your team). These are your champions. They are moved by purpose, not by a long list of features. They jump on board because they believe in the why—the vision of a smarter, better, more empowered way to work. Once you win them over, you create the visible proof and momentum needed for the rest of the organization to follow. This isn't just a theory; it's a field-tested production recipe for cultural change. In our new video, I break down the 90-day blueprint to successfully implement new technology by winning over the right people in the right order. Ready to de-risk your next rollout and drive real adoption? #DigitalTransformation #ChangeManagement #Innovation #Leadership #Industry40 #Manufacturing #PlantManager #ITManager #FutureOfWork