<\/p>\n\n\n\n
Artificial Intelligence is entering the manufacturing world strongly. It is being talked about as lever to increase productivity, improve quality, anticipate breakdowns, optimize planning, reduce decision-making times, and make businesses more responsive. <\/strong>Sensors, MES, ERP, IoT, dashboards, predictive algorithms, and automation systems promise a more connected, intelligent, and data-driven factory. But the promise of technology is only fulfilled when the process supporting it is stable, measurable, and waste-free.<\/p>\n\n\n\n Yet, there is one point that is often underestimated: Technology alone does not transform a production system.<\/strong> You can make it faster, more visible, more automated. But if the processes are unstable, the data is incomplete, responsibilities are unclear, and operational flows are full of waste, digital technology risks amplifying inefficiencies instead of eliminating them. This is where Lean becomes decisive once again: not as a theoretical framework, but as an operational method for distinguishing what creates value from what consumes resources without generating it.<\/p>\n\n\n\n The real question, therefore, is not: \u201cWhat technology should we adopt?\u201d. The correct question is: \u201cWhich process do we want to improve?\u201d<\/strong>. Before introducing AI, Big Data, automation, or advanced dashboards, it is necessary to understand where value is generated, where efficiency is lost, and what waste prevents the organization from operating smoothly. Digitizing an inefficient process often means accelerating the problem, not solving it. Every digital transformation should start with a simple principle: when automation is applied to an efficient process, it increases its efficiency; when applied to an inefficient process, it increases its inefficiency.<\/p>\n\n\n\n This rule is particularly relevant in the age of AI. If a production flow is characterized by rework, delays, unnecessary handling, excess inventory, defects, overproduction, or outdated information, simply introducing technology is not enough. The risk is building a system that is more modern in form but no more effective in substance.<\/p>\n\n\n\n Digitizing chaos means speeding up a loss.<\/strong> Automating a non-value-added activity simply makes it faster. Connecting systems that handle inconsistent data means increasing the speed at which incorrect information circulates throughout the organization. That\u2019s why the starting point isn\u2019t the tool, but the process: digital technology must be a driver of simplification, not a source of added complexity.<\/p>\n\n\n\n For this reason, the correct approach is \u201cLean First, Digital Second, Value Always\u201d:<\/strong> First the process is simplified, stabilized, and standardized; then what truly makes sense to enhance is digitized. This approach is consistent with the logic of value-driven digital transformation, as also explored in depth by Bonfiglioli Consulting<\/a>.<\/p>\n\n\n\n Based on our experience, The transformation must be Lean & Digital <\/strong>and so not to leave <\/strong>by technology, but of the value<\/strong>: the ability to analyze the process, distinguish between what creates value and what generates waste, and build a system in which people, data, processes, and digital tools all work toward the same goal.<\/p>\n\n\n\n Lean teaches us to look at processes from a very practical perspective: every activity must be evaluated in terms of the value it generates for the customer and for the organization. There are value-adding activities, non-value-adding but necessary activities, and non-value-adding activities that can be eliminated.<\/p>\n\n\n\n Traditional forms of waste are well known in the manufacturing world and are defined using Japanese terms. From an operational perspective, their meaning is not theoretical but practical: they indicate where the system wastes time, space, energy, and decision-making capacity. Muda<\/strong> <\/a>It highlights the most obvious forms of waste: defects, rework, scrap, additional inspections, delays, and loss of reliability. Every defect is a visible cost, but it is also a sign of process instability. <\/strong>Overproduction is one of the most dangerous forms of waste, because it involves producing more than is needed, before it is needed, or in quantities that do not match actual demand. Overproduction generates inventory, takes up space, masks problems, and reduces flexibility. Waiting is another significant form of waste: workers waiting for materials, machines idle due to a lack of components, production lines halted due to a lack of information, and decisions postponed because the correct data is not available. Every instance of waiting represents a waste of time, but also a loss of decision-making capacity.<\/p>\n\n\n\n Added to these are unnecessary transportation, excessive handling, oversized inventories, non-optimized processes, and skills that are not fully utilized. Every unnecessary movement, every redundant step, and every inventory built up to compensate for instability upstream is a sign of a process that isn\u2019t flowing smoothly. Lean analysis isn't just about "spotting" waste, but about understanding where the dysfunction that causes it originates.<\/strong><\/p>\n\n\n\n In Lean terminology, in addition to the classic forms of waste, there are also:<\/p>\n\n\n\n This waste does not disappear with digital transformation. On the contrary, if it is not addressed first, it risks being replicated in information systems, dashboards, and algorithms. That is why standardization<\/strong> It's not just an organizational detail, but the prerequisite for any credible AI project.<\/strong><\/p>\n\n\n\n Digitalization gives rise to new forms of waste\u2014less visible than excess inventory or machine downtime, but just as significant: digital waste.<\/strong><\/p>\n\n\n\n Digital waste occurs whenever an organization spends time, energy, and resources managing information that is unreliable, redundant, incomplete, or difficult to find. It may seem like a minor problem compared to a machine that has come to a standstill, but in reality, it has a profound impact on the quality of decisions.<\/p>\n\n\n\n The examples are numerous:<\/strong> duplicated data across multiple systems, parallel Excel files, information still collected on paper, repeated manual entries, reports produced with great effort but rarely used, unaligned dashboards, indicators calculated differently from department to department, non-integrated IT and OT systems. These are not mere informational flaws: they are organizational costs that slow down decision-making and make subsequent actions less reliable.<\/p>\n\n\n\n Whenever a person spends time searching for data, verifying its reliability, comparing different versions of the same file, or correcting poorly entered information, the organization is consuming energy on activities that do not generate value. Data redundancy, low information quality, parallel Excel sheets, paper-based data, and duplicate entries are concrete examples of digital waste. Effective digitization reduces the number of steps, it does not multiply them.<\/p>\n\n\n\n Digital waste slows down processes, but above all they slow down decisions.<\/strong> In the factory, deciding late often means intervening when the problem has already caused an impact. An anomaly not caught in time can become machine downtime. A quality data point not read correctly can become scrap. An unreliable forecast can become excessive inventory or a service failure. An unmanaged alert can become an emergency. This is why data must be governed along the entire flow: collection, validation, integration, reading, and action. Digitization, therefore, must not be limited to \u201cproducing more data.\u201d. It must help the organization produce better, more useful, more timely, and more action-oriented data.<\/strong><\/p>\n\n\n\n One of the most important principles of Lean & Digital Transformation is clear: You improve what you measure.<\/strong> But this statement is true only on one condition: What you measure must be correct, meaningful, and connected to real processes.<\/strong><\/p>\n\n\n\n Bad measurement means improving in the wrong direction. Measuring too much means scattering attention. Measuring without acting means creating bureaucracy. Measuring without quality data means generating misinformation.<\/p>\n\n\n\n For this reason, every AI and digital transformation journey must start with a fundamental question: Are our data really usable to make better decisions?<\/strong><\/p>\n\n\n\n The data must be native, complete, accurate, certified, and available when needed. They must be collected at the point where the event occurs, avoiding manual steps, transcriptions, and duplications. They must be accessible in the ward, in the control room, in operational meetings, in escalation processes, and at decision-making moments. The issue here is not only technical, but also organizational: without clear responsibility for the data, no dashboard produces real improvement.<\/p>\n\n\n\n Only in this way does data become information. And only when the information is interpreted within a clear process can it become useful knowledge for improvement.<\/p>\n\n\n\n An indicator should not be a number to be observed passively. It should generate a question, a decision, or an action. If a KPI does not trigger a behavior, a responsibility, or an improvement, it risks becoming mere informational noise. A good indicator<\/strong>, therefore, it does not only measure the past: guides future behavior.<\/strong><\/p>\n\n\n\n In the industrial world, the shift from traditional data to Big Data is not just about quantity. It is about the way data is generated, collected, integrated, interpreted, and transformed into value.<\/p>\n\n\n\n Traditional data is often structured, collected at defined intervals, managed in separate systems, and used predominantly for historical analysis. It is important data, but it tends to tell what has already happened.<\/p>\n\n\n\n Big Data, on the other hand, originate from a multiplicity of sources<\/strong>machines, sensors, MES systems, ERP, quality, maintenance, logistics, supply chain, customers, suppliers and digital platforms. This data is more numerous, faster, more variable and often more complex to interpret. The difference is not only quantitative: changes the organization's ability to read weak signals, correlations, and anomalies before they become problems.<\/strong><\/p>\n\n\n\n The real difference, however, is not having more data. It is being able to transform this mass of information into actionable knowledge. A factory can generate millions of data points a day and still remain poorly data-driven if that data is not governed, integrated, and connected to decision-making processes.<\/p>\n\n\n\n Being data-driven does not mean having many dashboards. It means making better decisions because the information is reliable, available, understandable, and action-oriented. Data is valuable not for its abundance, but for its ability to improve a concrete choice.<\/p>\n\n\n\n The 8 Vs of Big Data help understand what characteristics a truly useful information asset must have.<\/p>\n\n\n\n Il Volume \u00e8 la quantit\u00e0 di dati disponibili. Sensori, macchine, ERP, MES, sistemi qualit\u00e0 e manutenzione generano una massa crescente di informazioni. Ma il volume, da solo, non basta. La sfida \u00e8 trovare l’informazione rilevante dentro un flusso sempre pi\u00f9 ampio.<\/p>\n\n\n\n Il Value, il valore, indica quanto un dato aiuta a prendere una decisione, prevenire un’anomalia, ridurre uno spreco, migliorare un KPI o anticipare un problema. Se non genera azione, il dato resta un’informazione sterile.<\/p>\n\n\n\n La Veracity, cio\u00e8 la veridicit\u00e0, riguarda l’affidabilit\u00e0 del dato. Un dato non accurato pu\u00f2 creare disinformazione e portare a decisioni sbagliate. Per questo servono dati certificati, controllati e coerenti tra le diverse fonti.<\/p>\n\n\n\n La Visualisation, la visualizzazione, non deve limitarsi a mostrare numeri. Una buona dashboard deve aiutare le persone a capire cosa sta accadendo, dove intervenire e con quale priorit\u00e0.<\/p>\n\n\n\n La Variety, la variet\u00e0 delle fonti e dei formati, riguarda l’origine dei dati: macchine, operatori, sistemi gestionali, sensori, clienti, fornitori e piattaforme digitali. L’integrazione di fonti diverse \u00e8 essenziale per costruire una lettura completa del processo.<\/p>\n\n\n\n La Velocity, la velocit\u00e0, indica quanto rapidamente cambiano i dati. Alcuni richiedono decisioni in tempo reale, altri analisi pi\u00f9 profonde. La velocit\u00e0 del dato deve essere coerente con il processo decisionale che deve supportare.<\/p>\n\n\n\n La Viscosity, la viscosit\u00e0, descrive quanto \u00e8 difficile estrarre ed elaborare un dato. Se i dati sono lenti da processare o bloccati in sistemi separati, l’organizzazione perde reattivit\u00e0 e il miglioramento rallenta.<\/p>\n\n\n\n La Virality, la viralit\u00e0, riguarda la velocit\u00e0 con cui un alert, un’anomalia o uno scostamento raggiungono le persone giuste, nel momento giusto, attraverso routine e canali definiti. Questa dimensione \u00e8 particolarmente importante nei contesti manifatturieri, perch\u00e9 la rapidit\u00e0 di diffusione dell’informazione incide direttamente sulla tempestivit\u00e0 dell’intervento.<\/p>\n\n\n\n Queste 8 V mostrano che il tema non \u00e8 semplicemente “avere dati”, ma costruire un sistema informativo capace di generare decisioni migliori, pi\u00f9 rapide e pi\u00f9 affidabili. La vera sfida non \u00e8 possedere grandi quantit\u00e0 di dati, ma trasformarli in valore.<\/p>\n\n\n\n Per generare valore, il dato deve essere gestito con metodo. Non basta raccoglierlo, archiviarlo o visualizzarlo in una dashboard. Serve un processo strutturato che permetta di trasformare il dato grezzo in informazione affidabile, l’informazione in conoscenza e la conoscenza in decisioni operative.<\/p>\n\n\n\n Quale problema vogliamo risolvere? Quale performance vogliamo migliorare? Quale decisione vogliamo supportare? Vogliamo ridurre i fermi macchina, migliorare l’OTD, diminuire gli scarti, ottimizzare lo stock, aumentare l’efficienza di linea, rendere pi\u00f9 affidabile il forecast? Senza un obiettivo chiaro, il rischio \u00e8 raccogliere molti dati ma non sapere come utilizzarli. Il dato deve sempre nascere da una domanda di business o da un’esigenza di processo.<\/p>\n\n\n\n Una volta definito l’obiettivo, occorre individuare quali dati servono, da quali fonti provengono, chi li genera, con quale frequenza devono essere aggiornati e con quale livello di affidabilit\u00e0. La raccolta deve essere il pi\u00f9 possibile vicina al punto in cui l’evento accade. Pi\u00f9 passaggi manuali si introducono, pi\u00f9 aumenta il rischio di errore, ritardo e duplicazione.<\/p>\n\n\n\n Data quality is not automatic. Before they can be used, information must be cleaned, normalized, validated, and made consistent. This phase makes it possible to eliminate duplicates, missing values, non-homogeneous formats, entry errors, and inconsistencies between different systems. It is one of the most important steps, because dirty data generates weak analysis and bad decisions.<\/p>\n\n\n\n L’esplorazione permette di individuare correlazioni, anomalie, deviazioni, trend e pattern ricorrenti. \u00c8 qui che l’organizzazione inizia a trasformare i numeri in comprensione. Per esempio, si possono osservare relazioni tra microfermate e turni produttivi, tra difetti e lotti di materiale, tra ritardi di consegna e famiglie di prodotto, tra consumi energetici e condizioni operative. Questa lettura consente di passare dalla percezione alla conoscenza oggettiva del processo.<\/p>\n\n\n\n Dopo aver compreso il comportamento del processo, \u00e8 possibile costruire modelli analitici, predittivi o prescrittivi. Qui entrano in gioco strumenti avanzati come AI, machine learning, simulazioni e algoritmi di ottimizzazione. La modellazione permette di anticipare scenari, stimare rischi, prevedere guasti, supportare decisioni di scheduling, individuare cause radice e suggerire azioni correttive. Ma il modello ha valore solo se resta collegato al processo reale e se le persone sanno interpretarne gli output.<\/p>\n\n\n\n L’ultimo passaggio consiste nel rendere il dato fruibile nei momenti decisionali. Dashboard, control room, alert, report automatici, workflow digitali e sistemi di escalation devono aiutare le persone a capire rapidamente cosa sta accadendo, dove intervenire e con quale priorit\u00e0. La visualizzazione non deve essere decorativa, ma operativa. Un buon sistema informativo non mostra semplicemente dati: orienta l’azione.<\/p>\n\n\n\n Questo processo in 6 passi consente di superare la semplice disponibilit\u00e0 dell’informazione e costruire una vera cultura data-driven. \u00c8 qui che AI e Big Data possono generare valore: non come esercizio tecnologico, ma come parte di un processo decisionale integrato.<\/p>\n\n\n\n AI finds its maximum potential when integrated into a mature Lean system. Lean reduces waste, variability, and complexity. Digital makes data visible in real time. AI helps read patterns, anticipate scenarios, and support predictive decisions.<\/p>\n\n\n\n Together, these elements make it possible to move from reactive management to preventive, proactive, and predictive management.<\/strong>. It is at this intersection that digital stops being a simple technological investment and becomes a measurable competitiveness factor.<\/p>\n\n\n\n In the maintenance<\/strong>, this means anticipating failures and reducing unplanned downtime. In quality<\/strong>, identify deviations before they generate defects. In planning<\/strong>, simulate scenarios and improve scheduling and forecasting. In the internal logistics<\/strong>, optimize flows, routes, and stock levels. In performance control<\/strong>, make root cause analyses faster.<\/p>\n\n\n\n But everything starts from a base: clear processes, reliable data and eliminated waste.<\/strong><\/p>\n\n\n\n AI does not replace the method. It enhances it. <\/strong>It does not eliminate the need for standards, roles, responsibilities, and routines. On the contrary, it requires even more disciplined organization in process and information management.<\/p>\n\n\n\n An algorithm can suggest a prediction, but the organization must know how to read it, validate it, and turn it into a decision. A system can generate an alert, but a routine capable of handling it must exist. A dashboard can make a variance visible, but trained people are needed to interpret it and take action.<\/p>\n\n\n\n Without this connection between data and action, digital transformation remains incomplete.<\/p>\n\n\n\n One of the most frequent errors in digital journeys is confusing visualization with improvement. A dashboard can make data more accessible, but it does not automatically guarantee better decisions.<\/strong><\/p>\n\n\n\n For a dashboard to generate value, it must be embedded within a management routine. It must be clear who uses it, when it is consulted, which indicators are priorities, which thresholds trigger an escalation, what actions must be taken, and how the effectiveness of decisions is verified.<\/p>\n\n\n\n The control room, daily meetings, problem-solving moments, escalation processes, and periodic performance reviews are where data becomes action. It is there that the organization interprets variances, identifies causes, assigns responsibilities, and activates improvement. The dashboard, by itself, doesn't improve anything: it only improves if it is part of a rigorous and regular management practice.<\/strong><\/p>\n\n\n\n Unused data is waste. An unmanaged alert is waste. A report produced but not discussed is waste. A beautiful dashboard that is not linked to decisions is digital waste.<\/p>\n\n\n\n True transformation does not happen when a company \u201csees\u201d more data, but when it makes better decisions thanks to that data.<\/p>\n\n\n\n Every AI project should start with value. What problem do we want to solve? What waste do we want to eliminate? What process do we want to make more stable? What decision do we want to improve? What data do we really need?<\/p>\n\n\n\n Only after answering these questions does it make sense to choose the technology. Otherwise, the risk is starting with the tool and subsequently looking for a problem to apply it to. A well-set-up project does not start with the software, but with the need for improvement.<\/p>\n\n\n\n Lean & Digital Transformation is not the adoption of software or a platform. It is a journey of redesigning processes, the organization, skills, and governance. Technology is a lever, not the starting point.<\/p>\n\n\n\n This approach makes it possible to avoid three very common mistakes. The first is digitizing unstable processes. The second is building information systems that are full of data but poor in meaning. The third is adopting advanced technologies without creating the organizational conditions to actually use them.<\/p>\n\n\n\n Digital works when it enhances a process that is already understood, simplified, and governed. AI generates value when it works on reliable data. Big Data becomes competitive advantage when it supports better decisions. And Lean remains the method that allows us to distinguish what creates value from what produces noise.<\/p>\n\n\n\n <\/p>\n\n\n\n Why applying technology to an inefficient process does not eliminate waste, it only speeds it up. Before digitizing, it is necessary to simplify, stabilize, and standardize the process according to the \u201cLean First, Digital Second, Value Always\u201d logic.<\/p>\n\n\n\n These are inefficiencies related to data management: duplicate information, parallel Excel files, manually entered data multiple times, underutilized reports, or unaligned dashboards. They consume time and energy without generating value, just like traditional Lean waste.<\/p>\n\n\n\n These are the eight characteristics that an information asset must have to generate value: Volume, Value, Veracity, Visualisation, Variety, Velocity, Viscosity, and Virality. Together they help understand whether data are truly useful to support better decisions.<\/p>\n\n\n\n Through a six-step process: defining the goal, gathering the data, preparing it, exploring it, modeling it, and finally presenting it in a way that drives concrete action, such as through dashboards, alerts, or management routines.<\/p>\n\n\n\n AI enhances a mature Lean system: it helps read patterns, anticipate failures, optimize planning, and speed up root cause analysis. But it only works if applied to stable processes and reliable data, it does not replace them.<\/p>\n\n\n\n In an increasingly complex industrial world, the real difference will not be made by the companies that collect the most data, but by those that know how to transform it into knowledge, decisions, and continuous improvement.<\/p>\n\n\n\n Lean waste and digital waste are two sides of the same coin:<\/strong> Organizational energy is wasted on activities that do not generate value. The former manifest themselves in physical, production, and operational flows. The latter in information flows, systems, data, and decision-making processes.<\/p>\n\n\n\n To this end, digital transformation must start from an integrated reading: streamlined processes, quality data, clear governance, widespread skills, and technologies consistent with business objectives. It is a matter of method even before tools.<\/p>\n\n\n\n AI can become an extraordinary accelerator, but only if it finds fertile ground: stable processes, readable workflows, reliable data, defined responsibilities, and effective decision-making routines. From this perspective, value stems from the alignment between people, processes, and data, not merely from the availability of technology.<\/p>\n\n\n\n Because you improve what you measure. But you truly improve only when you measure well, with reliable, meaningful data connected to value.<\/p>\n\n\n
To explore further the connection between method and continuous improvement, see also the content on Lean Thinking<\/a>.<\/p>\n\n\n\n
\n\n\n\nDigitizing chaos means making it faster<\/strong><\/h2>\n\n\n\n
Lean Waste: Where Value Is Lost<\/strong><\/h2>\n\n\n\n
For a systematic exploration of this topic, see also the Lean Thinking Glossary<\/a>.<\/p>\n\n\n\nMuda: i sette sprechi classici della produzione<\/strong><\/h3>\n\n\n\n
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Data: What Really Matters<\/strong><\/h2>\n\n\n\n
Cosa sono gli sprechi digitali<\/strong><\/h3>\n\n\n\n
Perch\u00e9 gli sprechi digitali rallentano le decisioni<\/strong><\/h3>\n\n\n\n
On the topic of data quality and data governance, the resources of the NIST<\/a> and the World Economic Forum<\/a>.<\/p>\n\n\n\nMeasure well to improve<\/strong><\/h2>\n\n\n\n
I requisiti dei dati per decisioni affidabili<\/strong><\/h3>\n\n\n\n
Traditional data and Big Data<\/strong><\/h2>\n\n\n\n
The 8 Vs of Big Data<\/strong><\/h2>\n\n\n\n
Volume<\/strong><\/h3>\n\n\n\n
Value<\/strong><\/h3>\n\n\n\n
Veracity<\/strong><\/h3>\n\n\n\n
Visualization<\/strong><\/h3>\n\n\n\n
Variety<\/strong><\/h3>\n\n\n\n
Velocity<\/strong><\/h3>\n\n\n\n
Viscosity<\/strong><\/h3>\n\n\n\n
Virality<\/strong><\/h3>\n\n\n\n
How to create value from data<\/strong><\/h2>\n\n\n\n
1. Definire l’obiettivo<\/strong><\/h3>\n\n\n\n
2. Raccogliere i dati<\/strong><\/h3>\n\n\n\n
3. Preparare i dati<\/strong><\/h3>\n\n\n\n
4. Esplorare i dati<\/strong><\/h3>\n\n\n\n
5. Modellare i dati<\/strong><\/h3>\n\n\n\n
6. Presentare e automatizzare<\/strong><\/h3>\n\n\n\n
AI and continuous improvement<\/strong><\/h2>\n\n\n\n
From dashboard to decision<\/strong><\/h2>\n\n\n\n
First value, then technology<\/strong><\/h2>\n\n\n\n
Frequently asked questions about Lean, data, and digital transformation<\/strong><\/h2>\n\n\n\n
Why digitization alone is not enough to improve a manufacturing company?<\/strong>
<\/h3>\n\n\n\nWhat is meant by digital waste?<\/strong>
<\/h3>\n\n\n\nWhat are the 8 Vs of Big Data?<\/strong>
<\/h3>\n\n\n\nHow do you turn data into a useful decision?<\/strong>
<\/h3>\n\n\n\nWhat is the role of artificial intelligence in a Lean & Digital journey?<\/strong>
<\/h3>\n\n\n\nWhy does value come before technology?<\/strong><\/h2>\n\n\n\n