The Scorecard Has Changed A decade ago, an OEM could win a deal on mechanical performance alone. Cycle time, throughput, build quality, after-sales response — that was the scorecard. It is not the scorecard anymore. Manufacturers buying machines today are no longer just buying a machine. They are buying a node on their digital plant — and increasingly, a source of insight. In Deloitte's 2025 Smart Manufacturing and Operations survey of 600 manufacturing executives, 92% said smart manufacturing will be the main driver of competitiveness over the next three years, and 78% reported allocating more than 20% of their improvement budget to smart manufacturing initiatives. The implication for OEMs is direct: the machine is now expected to contribute to that picture, not sit outside it. A COO at one of the industrial manufacturers we work with put it plainly: "Today, end-users expect more than just high-performance machines. They want greater visibility into how their operations are performing, and the ability to derive insights from the data their systems generate." That sentiment shows up consistently in procurement conversations. The high-performance machine is now table stakes. What differentiates is what the machine reveals once it is running. The Buyer's Checklist Has Changed Walk into any procurement discussion at a serious manufacturer — automotive Tier 1, pharma, F&B, building products — and a new layer of questions now sits alongside the mechanical spec sheet: - Will this machine give our team visibility into performance — uptime, OEE, cycle behaviour — without a separate analytics project? - Can we turn the data this machine produces into something a plant manager actually uses on Monday morning? - Does the machine expose data over OPC UA or MQTT out of the box, with a documented tag structure? Notice the shift. Two of the three questions are about insight, not connectivity. Connectivity is the price of entry; what the buyer is really evaluating is whether the machine helps them see and decide. An OEM who answers "we expose the tags, the customer's IT team can take it from there" is solving half the problem. The OEMs winning today are thinking one layer up — about what the customer will be able to do with the data once it is flowing. Why This Pressure Is Accelerating Three forces are compounding at the same time. Plant-wide digitalisation is moving from pilot to mandate. Manufacturers that ran isolated Industry 4.0 pilots for years are now rolling out Unified Namespace architectures, plant-wide OEE tracking, and AI-driven analytics across sites. The economic case is no longer abstract — Siemens' True Cost of Downtime 2024 report puts the cost of unplanned downtime at the world's 500 largest companies at $1.4 trillion a year, equivalent to 11% of revenue, with automotive plants losing up to $2.3 million per hour. Every new machine that arrives unable to participate in the customer's data fabric becomes a visible gap on the shop floor. Compliance and traceability are tightening. In regulated industries — pharma, food, medical devices — auditors are asking for machine-level data lineage. In automotive, OEMs are pushing traceability requirements down to their suppliers. A machine that cannot produce a clean, timestamped data stream is a compliance liability. End customers are running internal scorecards. Large manufacturers now maintain internal "digital readiness" criteria for capital equipment purchases. Some of them score machines on connectivity standards before the commercial team is even allowed to negotiate. What "Industry 4.0-Ready" Actually Means at the Machine Level It is worth being concrete, because the term gets used loosely. For an OEM, a machine is genuinely I4.0-ready when it ships with: - Open, standards-based connectivity. OPC UA or MQTT exposed natively, not bolted on with a third-party gateway the customer has to buy separately. - A documented tag structure. Production counts, states, alarms, key process parameters — named consistently, mapped to a hierarchy the customer's team can read without a phone call. - Time-series-friendly data. Sampled at meaningful frequencies, with sensible buffering so a network blip does not cost the customer an hour of production history. - Security that an IT team will actually approve. Authenticated connections, role-based access, no hard-coded admin credentials. - Insight built in, not bolted on later. The machine should arrive with the analytics layer in place — performance visibility, downtime context, and the ability for the customer's team to query the data directly, without waiting on a separate BI project to make any of it useful. The Commercial Upside OEMs Underestimate It is easy to read all of this as a cost line — engineering hours, firmware work, documentation effort. The OEMs treating it that way are leaving margin on the table. The more interesting view is that connectivity and insight, built into the machine, open up adjacent revenue: remote monitoring contracts, predictive maintenance services, performance benchmarking across the customer's installed base, and renewals on data-backed value rather than relationships alone. The machine stops being a one-time sale and becomes a recurring touchpoint with the customer. There is a sales-cycle benefit too. When the prospect's digitalisation team can sign off in week two instead of holding the deal in month four, close rates move. Where the Leading OEMs Are Heading The OEMs pulling ahead are treating connectivity and insight as part of the product, not as a customer problem. They ship machines that arrive ready to participate in the customer's data fabric, with tag structures documented, security in place, and — increasingly — a way for the end-customer to actually ask questions of the data without a long analytics project in between. That last piece is where the conversation is moving. Connectivity gets the data out. Insight is what the customer pays for. The machine builders engineering for both are the ones being shortlisted for the next generation of capital purchases. Industry 4.0 readiness has stopped being a marketing claim and become a procurement criterion. The good news for OEMs is that the path forward is well-understood — and the partners, standards, and platforms to get there exist today. It is a question of when to engineer for it, not whether. Where Itanta Fits In This is the gap Itanta works on with OEMs and their end-customers. We help machine builders ship machines that arrive with insight built in — performance visibility, downtime context, and the ability for the plant manager, the digitalisation head, and the operations leader to query the data directly, without waiting on a separate BI project to turn data into something usable. The machine stops being a data source and becomes a decision surface. Today, we work with leading manufacturers and machine builders across pharma, dairy, food and beverage, and packaging — including Krones, Goma Process Technology, Merck's India operations, and OmniaTech India — to make industrial data usable from the machine outwards. The pattern is consistent: the OEMs and end-users investing in this layer now are the ones being shortlisted on the next capital purchase. If you are an OEM thinking through how to make your next generation of machines Industry 4.0-ready — or evaluating what end-customers are starting to expect today — we would be glad to talk. Contact us for a walkthrough of how Itanta works alongside machine builders to turn data into decisions, instantly. --- Sources: Deloitte, 2025 Smart Manufacturing and Operations Survey (600 executives, surveyed Aug–Sept 2024). Siemens, The True Cost of Downtime 2024 (with Senseye Predictive Maintenance).