{"id":40484,"date":"2026-06-20T12:03:28","date_gmt":"2026-06-20T10:03:28","guid":{"rendered":"https:\/\/www.logicline.de\/pay-per-use-mechanical-engineering-equipment-as-a-service"},"modified":"2026-07-03T07:11:38","modified_gmt":"2026-07-03T05:11:38","slug":"pay-per-use-mechanical-engineering-equipment-as-a-service","status":"publish","type":"post","link":"https:\/\/www.logicline.de\/en\/pay-per-use-mechanical-engineering-equipment-as-a-service","title":{"rendered":"Pay-Per-Use in Mechanical Engineering: What Manufacturers Need Before Implementing Usage-Based Billing"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"40484\" class=\"elementor elementor-40484 elementor-40471\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7c03afc0 e-flex e-con-boxed e-con e-parent\" data-id=\"7c03afc0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-61419d4e elementor-widget elementor-widget-text-editor\" data-id=\"61419d4e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>If you can&#8217;t properly document usage, you can&#8217;t properly bill for it either.<\/strong>  This is precisely why many pay-per-use initiatives in mechanical engineering fail.<\/p>\n\n<p>The core concept is easy to outline: Before getting into pricing logic, contract models, or forecasts, three things must be in place\u2014 <strong>clear asset allocation, reliable usage data, and an honest assessment of the installed base<\/strong>. Only then does an idea become a model that supports the day-to-day operations of service, finance, and management. <\/p>\n\n<p><strong>The key questions first:<\/strong><\/p>\n\n<ul>\n<li>Which machines are out in the field?<\/li>\n<li>Which of these are connected?<\/li>\n<li>What types of value can be assessed for each asset?<\/li>\n<li>Which contracts allow for which billing methods?<\/li>\n<li>Where is the data sufficient today\u2014and where is it not?<\/li>\n<\/ul>\n\n<p>In short: <strong>Pay-per-use follows the sequence <em>of digitization \u2192 connectivity \u2192 decision-making \u2192 automation<\/em>.<\/strong> Those who start with pricing plans are building on gaps. Those who start with data reduce billing disputes and lay the groundwork for forecasts, service, and financing. <\/p>\n\n<p>From there, the platform question also becomes clear: Salesforce as the hub for contract and service context, <a href=\"https:\/\/www.logicline.de\/en\/ai-in-service-own-knowledge-base\"><strong>Service Decision Intelligence (SDI)<\/strong><\/a> for traceable usage metrics with source attribution and EU-compliant processing within the customer\u2019s infrastructure, supplemented by GRAX-powered CRM and contract context without API limits.<\/p>\n\n<figure>\n<img alt=\"4-Step Model for Service Digitalization: Digitalize, Connect, Decide, Automate \u2013 Maturity Path for Pay-per-Use in Mechanical Engineering\" decoding=\"async\" src=\"\/wp-content\/uploads\/2026\/06\/pay-per-use-maturity-roadmap.jpg\">\n<figcaption>Pay-per-use is at the end of the maturity sequence: Digitize \u2192 Connect \u2192 Decide \u2192 Automate.<\/figcaption>\n<\/figure>\n\n<p><\/p>\n\n<h2>Why Usage-Based Billing Fails Without Reliable Usage Data<\/h2>\n\n<p>Without reliable usage data, pay-per-use becomes a matter of guesswork rather than actual billing. For the finance department, it\u2019s not the concept that matters, but clear evidence of usage. <\/p>\n\n<h3>The Financial Risk Behind Weak Usage Metrics<\/h3>\n\n<p>For CFOs, this is no minor issue. If usage data cannot be clearly traced back to a single source, billing lacks a reliable basis. Recurring revenue can only be planned if the underlying usage metrics are reproducible. This requires an automated transfer into the billing process\u2014one that is auditable and free of manual intermediate steps. Anything else leads to follow-up questions, delays, and additional effort to resolve issues.    <\/p>\n\n<p>In practice, we often see the same pattern: machines send individual values, but timestamps, device references, or plausibility checks are missing. Then the discussion doesn\u2019t start with the price, but with the question of whether usage was even recorded correctly. That\u2019s exactly where the model falls apart from the finance department\u2019s perspective.  <\/p>\n\n<p>This becomes even more important at the fleet level. Only when a large number of machines provide data according to a uniform format can a model be created that can be used for control and billing purposes. <\/p>\n\n<h3>Why Connected Fleets Are More Important Than Individual Machines<\/h3>\n\n<p>Pay-per-use only becomes cost-effective with a larger, connected fleet of similar machines with measurably varying utilization rates. A single machine can demonstrate the potential. However, this does not yet result in a robust business model. Without IoT telemetry, there is no reliable way to measure usage and no billing system that can be operated at scale.   <\/p>\n\n<p>That is precisely why pay-per-use belongs at the end of a maturity sequence: <strong>Digitize \u2192 Connect \u2192 Decide \u2192 Automate<\/strong>. Anyone who starts with pricing logic or contract models is missing the heart of the matter. First, inventories, usage sources, and data quality must be in order. Only then can one assess which billing logic is actually viable for each machine, customer, or fleet.   <\/p>\n\n<p>For this step, it often makes sense to conduct a sober assessment of the current situation, such as through an <a href=\"https:\/\/www.logicline.de\/en\/services\/installed-base-assessment\">installed base assessment<\/a>. Once machine types, connectivity, data points, and utilization patterns have been accurately recorded, the sales process can later be integrated into Salesforce. The real hurdle, however, lies before that: the question of whether usage can be reliably demonstrated from both a technical and a business perspective.  <\/p>\n\n<div class=\"callout\">\n<p style=\"margin: 0;\"><strong>Thinking about pay-per-use, but unsure if your fleet can provide the data?<\/strong>\nIn 30 minutes, we\u2019ll assess your installed base and use a concrete example to show which machines could already be billed today\u2014no sales pitch.\n\u2192 <a href=\"https:\/\/www.logicline.de\/en\/contact\"><strong>Schedule an initial consultation<\/strong><\/a><\/p>\n<\/div>\n\n<h2>What Manufacturers Need Before Usage-Based Billing<\/h2>\n\n<p>Before pay-per-use can work in everyday practice, three things must be clarified: <strong>a clear asset identity, reliable usage data, and a realistic picture of the installed base<\/strong>. Behind this lie three simple but business-critical questions: Which assets are actually in the field, which ones provide reliable data, and for which machines is the available data already sufficient for reliable billing? <\/p>\n\n<h3>A clean installed base and unique asset identity<\/h3>\n\n<p>Usage-based billing often fails not because of the rate plan, but because of how usage is assigned. If machines are recorded twice, serial numbers are not maintained consistently, or the contract reference is missing, usage cannot be billed accurately later on. <\/p>\n\n<p>The <a href=\"https:\/\/www.logicline.de\/en\/services\/digital-machine-file\">digital machine file (IOTAM)<\/a> lays the foundation here. A unique, structured data record is created for each asset, bringing together its configuration, service history, and contract details. Only then can the usage metrics of a specific machine be assigned to the appropriate contract. For service managers, this isn\u2019t just an IT detail\u2014it\u2019s essential for ensuring that invoices remain traceable and that inquiries don\u2019t drag on for days.   <\/p>\n\n<h3>Telemetry, ERP data, and contract data in a single usage model<\/h3>\n\n<p>Usage data never comes from just one source. The machine provides data such as operating hours, cycles, or utilization via IoT telemetry. ERP data shows material consumption. Service events provide the necessary context: downtime, maintenance windows, or failures. In addition, contract data specifies <strong>which parameters may be billed to which customers<\/strong>.    <\/p>\n\n<p>Only when this data is automatically consolidated can billing become repeatable. This is precisely where Salesforce becomes attractive to many manufacturers: not as an end in itself, but as a place where service, contract, and asset context are brought together. If diagnostic or predictive cases also need to be evaluated, Service Decision Intelligence (SDI) can serve as a decision-making layer. What matters most here is that recommendations are provided with <strong>source references<\/strong> and that data can be processed within the <strong>customer\u2019s infrastructure in compliance with EU regulations<\/strong>, if desired. This is a particularly crucial point, especially in disputes over usage values or billable downtime.    <\/p>\n\n<h3>A Reality Check on the Fleet Before Designing the Rate Plan<\/h3>\n\n<p>Before rates are set, the fleet needs a sober reality check. Which types of vehicles are already connected? Where is telemetry missing? Which usage patterns can be accurately measured and, therefore, billed?   <\/p>\n\n<p>An Installed Base Assessment distinguishes precisely between billable and non-billable segments. It identifies data gaps, evaluates connectivity coverage, and highlights which parts of the fleet are immediately suitable for a usage-based model and which need to be retrofitted first. In practice, we often see that a pilot project with just a few well-connected machines runs smoothly. Things get more difficult as soon as older model series, different control systems, and incomplete master data come into play.   <\/p>\n\n<p>For a usage-based model to work, the data must be reliable enough that all parties involved\u2014manufacturers, service partners, and customers\u2014trust it. Without this trust, any billing will remain contentious. A pilot program involving a small number of well-connected machines demonstrates that the principle works. However, a reliable model can only be established if this level of data quality can be replicated across the entire fleet. That is precisely why the approach is not \u201ctechnology first,\u201d but rather: <strong>Digitize \u2192 Connect \u2192 Decide \u2192 Automate<\/strong>.    <\/p>\n\n<h2>How SDI Makes Fragmented Data Useful for Billing and Forecasts<\/h2>\n\n<p>Once asset identities and data sources are in place, data collection alone is not enough. For billing and forecasting, you need a logic that seamlessly integrates telemetry, ERP consumption data, and contract context. <strong>Service Decision Intelligence (SDI)<\/strong> handles exactly this task. Only when a fleet of similar machines can transform a large volume of individual data points into a reliable trend can a scalable billing model be created.  <\/p>\n\n<h3>Transparent usage calculations instead of opaque billing<\/h3>\n\n<p>As soon as a customer receives an invoice based on operating hours or cycles, the same question almost always comes up: \u201cWhere does this figure come from?\u201d Without a source reference, this can quickly turn into a dispute. <\/p>\n\n<p>This is exactly where SDI comes in. Every calculated usage value can be traced back to its source\u2014that is, to the corresponding telemetry or ERP data point. For pay-per-use, this isn\u2019t just a detail\u2014it\u2019s the foundation. Billing and forecasting thus draw on the same data source. This reduces the effort required for reconciliation and makes invoices verifiable rather than prone to disputes.    <\/p>\n\n<p>This is especially important in mechanical engineering. If usage values aren\u2019t clearly documented, every discrepancy between the invoice, service report, and contract sparks a separate discussion. SDI provides a clear path here: value, source, calculation, and allocation remain traceable. For service managers, this means less follow-up clarification and greater certainty in day-to-day billing.   <\/p>\n\n<h3>Data Sovereignty and Contractual Context as Prerequisites for Scaling<\/h3>\n\n<p>Whether pay-per-use in mechanical engineering can be scaled in an auditable manner or gets stuck on a case-by-case basis depends on two factors: <strong>data sovereignty<\/strong> and <strong>the contractual context<\/strong>.<\/p>\n\n<p>For many companies, storing sensitive machine data in third-party cloud environments is not an option. SDI is therefore designed to <strong>run<\/strong> on the <strong>customer\u2019s own infrastructure<\/strong>. This supports EU-compliant implementations and keeps control of the data where it belongs. What\u2019s more, SDI is designed to be LLM-agnostic via MCP\/BYOM. Those who want to switch models or integrate their own models remain flexible rather than having to commit early on.    <\/p>\n\n<p>The contractual context is just as important. A usage value alone does not determine which rate applies, what special terms have been agreed upon, or when billing takes effect. This is where GRAX comes in: GRAX stores CRM and contract history as a structured dataset\u2014without the API limits that can otherwise become a bottleneck. This ensures that the context remains fully available for billing and reconciliation processes when usage data needs to be compared with the contractually agreed-upon rate plan.   <\/p>\n\n<p>In Salesforce, this does not result in a loose juxtaposition of metrics, contracts, and forecasts, but rather a shared working foundation. SDI thus serves as a bridge between the database, the rate plan, and the forecast. <\/p>\n\n<h2>The Business Model Behind Scalable Equipment-as-a-Service<\/h2>\n\n<p>When usage is measurable, the operating model determines how the system scales. Based on the data infrastructure and billing logic, Level 4 brings us to what often makes the difference in practice: <strong>automation<\/strong>. Pay-per-use isn\u2019t just a different price list\u2014it\u2019s a different operating model. This is precisely where the difference between traditional sales and a viable EaaS offering becomes apparent.    <\/p>\n\n<p>In <strong>the \u201cDigitize \u2192 Connect \u2192 Decide \u2192 Automate\u201d<\/strong> process model, the bottleneck usually lies not in data collection, but in ensuring consistent execution across multiple machines, locations, and contracts. Anyone who bills for usage needs established processes for service, finance, IT, and sales. Otherwise, a pilot project can quickly turn into extra work instead of business.  <\/p>\n\n<h3>Traditional Equipment Sales vs. Usage-Based Service Revenue<\/h3>\n\n<p>In traditional business, the sale is the final step. With Equipment-as-a-Service, it\u2019s just the beginning. The manufacturer remains responsible for availability, maintenance, and a reliable data flow\u2014which forms the basis for every invoice. This shifts responsibility across multiple areas. However, the model becomes scalable only through the fleet, not through individual machines. It is only by monetizing the installed base that a pilot project can be turned into a sustainable business.     <\/p>\n\n<div class=\"tbl\">\n<table>\n<thead>\n<tr>\n<th>Feature<\/th>\n<th>Traditional Equipment Sales<\/th>\n<th>Pay-per-Use \/ EaaS<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Revenue Logic<\/strong><\/td>\n<td>One-time payment upon delivery<\/td>\n<td>Recurring, usage-based<\/td>\n<\/tr>\n<tr>\n<td><strong>Data Requirements<\/strong><\/td>\n<td>Low (optional for warranty)<\/td>\n<td>Real-time, verifiable telemetry<\/td>\n<\/tr>\n<tr>\n<td><strong>Service Model<\/strong><\/td>\n<td>On-Demand Service<\/td>\n<td>Manufacturer Manages Maintenance<\/td>\n<\/tr>\n<tr>\n<td><strong>Asset Owner<\/strong><\/td>\n<td>Client<\/td>\n<td>Manufacturer or financing partner<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n<p>With pay-per-use, the manufacturer only earns revenue when the machine is running. Downtime affects both parties. This changes maintenance planning, escalation procedures, and the way service contracts are drafted. In Salesforce, these processes usually only become manageable when contract rules, usage data, and service cases are integrated into a single, unified process. For diagnostic and AI-driven decisions, Service Decision Intelligence (SDI) often serves as the layer that validates recommendations with source attribution and can be operated within the customer\u2019s infrastructure in compliance with EU regulations.    <\/p>\n\n<h3>A real-life example and a clearly identified typical scenario<\/h3>\n\n<p>This example illustrates how significantly the focus is shifting from the product to ongoing use. <a href=\"https:\/\/kraussmaffei.com\/\" rel=\"noopener\" target=\"_blank\">KraussMaffei<\/a> has implemented this approach with <strong>flexPay<\/strong>: Customers pay based on actual operating hours, billed through a financing partner. Pay-per-use thus shifts the focus from the sale to ongoing availability and verifiable usage. <\/p>\n\n<p><strong>Typical scenario (not a real-world example):<\/strong> A mass-production manufacturer with a large, networked fleet of similar processing machines implements a usage-based model. Usage varies greatly depending on the location and shift schedule. Only the fleet data allows for reliable rates and clear rules regarding downtime in the contract.  <\/p>\n\n<p>What successful models have in common is actually quite straightforward:<\/p>\n\n<ul>\n<li>The billing rules are established before the start.<\/li>\n<li>Exceptions are governed by the terms of the contract.<\/li>\n<li>The data is transparent to all parties involved.<\/li>\n<\/ul>\n\n<p>Without this foundation, every invoice becomes a potential source of dispute. That is precisely why it is not enough to simply connect individual machines digitally. The key factor is whether manufacturers can manage their installed base as a controllable portfolio\u2014for example, using a digital machine file (IOTAM) to capture the machine context and an Installed Base Assessment to classify the fleet within Salesforce.  <\/p>\n\n<h2>Conclusion: Build the foundation in the correct order<\/h2>\n\n<p>Pay-per-use is not the starting point. In mechanical engineering, it is the result of an installed base that has been accurately recorded, networked, and made usable for billing. The sequence is clear: <strong>Digitize \u2192 Connect \u2192 Decide \u2192 Automate<\/strong>. Without this foundation, any pricing logic remains nothing more than a calculation model without reliable evidence.   <\/p>\n\n<p>Usage-based billing stands or falls on reliable, traceable usage data\u2014across system boundaries and with source attribution. This is exactly where <strong>Service Decision Intelligence (SDI)<\/strong> comes in: SDI combines telemetry, ERP consumption data, and contract context into a verifiable basis for billing and forecasting. For service managers and executive management, it\u2019s not just the pricing model that matters, but whether every billing item can be substantiated. Trust is built on verifiable data, not on pricing logic.   <\/p>\n\n<p>In this way, the installed base becomes a driver of growth throughout its entire lifecycle\u2014right through to strategies for <a href=\"https:\/\/www.logicline.de\/en\/end-of-life-management-mechanical-engineering\">end-of-life and the late lifecycle<\/a>. That\u2019s why the starting point isn\u2019t a pricing model, but a fleet assessment. Two pragmatic approaches:  <\/p>\n\n<ul>\n<li><strong><a href=\"https:\/\/www.logicline.de\/en\/services\/installed-base-assessment\">Installed Base Assessment<\/a><\/strong> \u2013 when it is unclear which machines are networked and which usage data is reliable. It distinguishes between billable and non-billable segments and identifies where retrofitting is needed. <\/li>\n<li><strong><a href=\"https:\/\/www.logicline.de\/en\/contact\">Initial Consultation<\/a><\/strong> \u2013 once the database is set up and you\u2019d like to discuss the specific usage and billing model for your fleet.<\/li>\n<\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<section data-dce-background-color=\"#00000000\" class=\"elementor-element elementor-element-c254285 e-flex e-con-boxed e-con e-parent\" data-id=\"c254285\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div data-dce-background-color=\"#FFFFFF\" class=\"elementor-element elementor-element-55aff5e e-flex e-con-boxed e-con e-child\" data-id=\"55aff5e\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-48de356 e-con-full e-flex e-con e-child\" data-id=\"48de356\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-4b7d6a6 e-flex e-con-boxed e-con e-child\" data-id=\"4b7d6a6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0fa94fe elementor-widget elementor-widget-heading\" data-id=\"0fa94fe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">FAQs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ed3a7f2 elementor-widget elementor-widget-n-accordion\" data-id=\"ed3a7f2\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;expanded&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2480\" class=\"e-n-accordion-item\" open>\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"true\" aria-controls=\"e-n-accordion-item-2480\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h4 class=\"e-n-accordion-item-title-text\"> When does pay-per-use become worthwhile in mechanical engineering? <\/h4><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2480\" class=\"elementor-element elementor-element-8046bac e-flex e-con-boxed e-con e-child\" data-id=\"8046bac\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e3a4f16 elementor-widget elementor-widget-text-editor\" data-id=\"e3a4f16\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Pay-per-use in mechanical engineering is particularly effective when reliable usage data is available and there is a large, networked fleet of similar machines in the field. Only under these conditions can usage be made measurable, traceable, and billable. For mass producers with high production volumes and varying load profiles, this model is often particularly attractive: The more similar the machines are in design and the more accurately the data is collected, the easier it is to implement a usage-based pricing model without disputes over measurement values. Without this data foundation, pay-per-use quickly becomes a risk. This is precisely where work begins with the digital machine file (IOTAM) and the maturity model: Digitize \u2192 Connect \u2192 Decide \u2192 Automate\u2014first capture data accurately, then connect machines, next evaluate usage, and only then gradually automate billing.    <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2481\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2481\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h4 class=\"e-n-accordion-item-title-text\"> What data do I need for usage-based billing? <\/h4><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2481\" class=\"elementor-element elementor-element-5414f61 e-con-full e-flex e-con e-child\" data-id=\"5414f61\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e5ed46 elementor-widget elementor-widget-text-editor\" data-id=\"1e5ed46\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>For usage-based billing in mechanical engineering, reliable and verifiable usage data is essential. Without this foundation, any billing is open to challenge\u2014internally, by the customer, and by the service provider. This typically involves operating hours, the number of units produced, or the actual machine runtime. Added to this are technical parameters such as temperature, displacement, process forces, or wear indicators. Data is typically collected via telemetry, but sensor data alone is rarely sufficient: For robust billing, ERP consumption data and other sources must also be included. Service Decision Intelligence (SDI) acts as the intelligence layer here and establishes the link to the individual machine. Crucial for decision-makers: data ownership and source attribution\u2014it must be clear which source a value comes from and how it was incorporated into the billing.      <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2482\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2482\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h4 class=\"e-n-accordion-item-title-text\"> How do I get started with a partially connected fleet? <\/h4><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2482\" class=\"elementor-element elementor-element-cca8e05 e-con-full e-flex e-con e-child\" data-id=\"cca8e05\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7504c5c elementor-widget elementor-widget-text-editor\" data-id=\"7504c5c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Start by taking stock of your installed base: Which machines can already be connected, and how robust is the data? Based on this, you can identify where initial usage data can be collected accurately and reliably. Next, move forward with the implementation for the network-ready portion of the fleet and expand data interfaces step by step. It is crucial that the source of the usage data can be clearly traced. A clear maturity path helps with classification: Digitize \u2192 Connect \u2192 Decide \u2192 Automate. This prevents billing models from being promised too early, even though there are still gaps in data depth, device connectivity, or traceability.     <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>If you can&#8217;t properly document usage, you can&#8217;t properly bill for it either. This is precisely why many pay-per-use initiatives in mechanical engineering fail. The core concept is easy to outline: Before getting into pricing logic, contract models, or forecasts, three things must be in place\u2014 clear asset allocation, reliable usage data, and an honest [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":40470,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[43050],"tags":[],"class_list":["post-40484","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-strategy-business-model"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/posts\/40484","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/comments?post=40484"}],"version-history":[{"count":5,"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/posts\/40484\/revisions"}],"predecessor-version":[{"id":40764,"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/posts\/40484\/revisions\/40764"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/media\/40470"}],"wp:attachment":[{"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/media?parent=40484"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/categories?post=40484"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.logicline.de\/en\/wp-json\/wp\/v2\/tags?post=40484"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}