Every digital transformation should start from a simple principle: automation applied to an efficient process increases its efficiency; applied to an inefficient process, it increases its inefficiency.
This is a particularly relevant rule in the age of AI. If a production flow is characterized by rework, waiting, unnecessary movements, excessive inventories, defects, overproduction, or outdated information, introducing technology is not enough. The risk is to build a more modern system in form but not more effective in substance.
Digitizing chaos means making a loss faster. Automating an activity that doesn’t have value means making it just faster. Connecting systems that manage inconsistent data means increasing the speed at which incorrect information circulates within the organization. That’s why the starting point is not the tool, but the process: digital technology should be a lever of simplification, not a multiplier of complexity.
Therefore, the correct path is “Lean First, Digital Second, Value Always”: first, the process is simplified, stabilized, and standardized; then, what makes sense to actually enhance is digitized. This approach is consistent with the logic of value-driven digital transformation, as also explored by Bonfiglioli Consulting.
Based on our experience, the transformation must be Lean & Digital and therefore not start from technology, but from value: from the ability to read the process, to distinguish what creates value from what generates waste, and to build a system where people, data, processes, and digital tools work in the same direction.
Lean teaches us to observe processes with a very concrete perspective: each activity must be evaluated based on the value it generates for the customer and the organization. There are value-creating activities, activities that are not value-creating but necessary, and activities that can be eliminated.
Traditional waste is well known in the manufacturing world and is defined using Japanese terms. In an operational sense, their meaning is not theoretical but practical: they indicate where the system wastes time, space, energy, and decision-making capacity.
For a systematic in-depth look at the topic, see the Lean Thinking glossary as well.
Muda indicates the most obvious waste: defects, rework, waste, additional checks, delays, and loss of reliability. Each defect is a visible cost, but also a sign of instability in the process. Overproduction is one of the most dangerous waste types because it produces more than is needed, either before it is needed or in quantities inconsistent with real demand. Overproduction generates stockpiles, takes up space, hides problems, and reduces flexibility. Expectations represent another significant waste: operators waiting for materials, machines idle due to a lack of components, lines stuck due to a lack of information, decisions delayed because the correct data is not available. In every wait there is a loss of time, but also a loss of decision-making ability.
To these are added unnecessary transports, excessive handling, oversized inventories, inefficient processes and skills that are not fully utilized. Every unnecessary movement, every redundant step, every stock created to compensate for upstream instability is a symptom of a process that is not flowing smoothly. Lean analysis is not just about “seeing” the waste, but about understanding where the dysfunction that produces it originates.
In the Lean language, in addition to the classic waste, there are also:
These waste of resources do not disappear with digital technology. On the contrary, if they are not addressed first, they risk being replicated in information systems, dashboards, and algorithms. Therefore, standardization is not just an organizational detail; it is the prerequisite for any credible AI project.
With digitalization come new forms of waste that are less visible than a surplus of stock or a machine stop, but just as impactful: digital waste.
A digital waste manifests itself whenever an organization consumes time, energy, and resources to manage unreliable, redundant, incomplete, or difficult-to-find information. It may seem like a minor problem compared to a broken machine, but in reality it profoundly impacts the quality of decisions.
The examples are numerous: duplicate data in multiple systems, parallel Excel files, information still collected on paper, repeated manual entries, reports produced with great effort but not used much, dashboards that are not aligned, indicators calculated differently from function to function, IT and OT systems that are not integrated. These are not simple informational flaws: they are organizational costs that slow down the decision-making process and make subsequent actions less reliable.
Whenever a person spends time searching for a given item, 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 quality of information, parallel Excel, paper data, and duplicate entries are concrete examples of digital waste. Effective digitalization reduces the number of steps, not multiplies them.
Digital waste slows down processes, but above all it slows down decisions. In the factory, deciding late often means intervening when the problem has already caused an impact. An anomaly that is not detected in time can become a machine stop. A quality data that is not read correctly can become a waste. A forecast that is not properly evaluated can become excess stock or a lack of service. An alert that is not managed can become an emergency. For this reason, data must be governed throughout the entire flow: collection, validation, integration, reading, and action.
Regarding the topic of data quality and data governance, the resources of the NIST and the World Economic Forum are also useful.
Digitalization, therefore, must not be limited to “producing more data”. It must help the organization produce better, more useful, more timely, and more connected data to action.
One of the most important principles of Lean & Digital Transformation is clear: measure what you can improve. But this statement is true only under one condition: what you measure must be accurate, meaningful, and linked to real processes.
Measuring poorly means improving in the wrong direction. Measuring too much means wasting attention. Measuring without action means creating bureaucracy. Measuring without quality data means generating misinformation.
Therefore, every journey of AI and digital transformation must start with a fundamental question: are our data really usable to make better decisions?
Data must be native, complete, accurate, certified and available when needed. It must be collected at the point where the event occurs, avoiding manual steps, transcriptions and duplications. It must be accessible in the department, in the control room, in operational meetings, in escalation processes and in decision-making moments. Here the issue is not only technical but also organizational: without a clear responsibility for the data, no dashboard produces real improvement.
Only then does the data become information. And only when the information is interpreted within a clear process can it become useful knowledge for improvement.
An indicator should not be a number to be passively observed. It should generate a demand, a decision, or an action. If a KPI does not trigger behavior, responsibility, or improvement, it risks becoming just informational noise. A good indicator, therefore, does not just measure the past: it guides future behavior.
In the industrial world, the transition from traditional data to Big Data is not just about quantity. It concerns the way data is generated, collected, integrated, interpreted and transformed into value.
Traditional data is often structured, collected at defined intervals, managed in separate systems, and used primarily for ad-hoc analysis. It is important data, but it tends to tell only what has already happened.
Big Data, on the other hand, comes from a multitude of sources: machines, sensors, MES systems, ERP, quality, maintenance, logistics, supply chain, customers, suppliers, and digital platforms. They are more numerous, faster, more variable, and often more complex to interpret. The difference is not just quantitative: it changes the organization’s ability to read weak signals, correlations, and anomalies before they become problems.
The real difference, however, is not having more data. It is about being able to transform this mass of information into operational knowledge. A factory can generate millions of data per day and still remain little data-driven if that data is not governed, integrated, and connected to decision-making processes.
Being data-driven doesn’t mean having many dashboards. It means making better decisions because the information is reliable, available, understandable, and action-oriented. Data is not valued for its abundance, but for its ability to improve a specific choice.
The 8 V of Big Data help us understand what characteristics a truly useful information asset should have.
Volume is the amount of data available. Sensors, machines, ERP, MES, quality systems and maintenance generate an increasing amount of information. But volume alone is not enough. The challenge is to find the relevant information within an ever-expanding flow.
The value, the value, indicates how much a given data helps to make a decision, prevent an anomaly, reduce a waste, improve a KPI or anticipate a problem. If it does not generate action, the data remains sterile information.
Veracity, that is, truthfulness, concerns the reliability of the data. An inaccurate data can create misinformation and lead to wrong decisions. For this reason, certified, verified and consistent data from different sources are needed.
Visualization, visualization, must not be limited to showing numbers. A good dashboard must help people understand what is happening, where to intervene, and with what priority.
Variety, the variety of sources and formats, concerns the origin of the data: machines, operators, management systems, sensors, customers, suppliers, and digital platforms. Integrating different sources is essential to build a complete reading of the process.
Velocity, or speed, indicates how quickly data changes. Some require real-time decisions, others deeper analysis. The speed of the data must be consistent with the decision-making process it must support.
Viscosity, or the viscosity, describes how difficult it is to extract and process data. If data is slow to process or locked in separate systems, the organization loses responsiveness and improvement slows down.
Virality, or the speed with which an alert, anomaly, or deviation reaches the right people at the right time through defined routines and channels, is crucial in manufacturing environments. This dimension is particularly important in manufacturing contexts because the speed with which information spreads directly impacts the timeliness of intervention.
These 8 V show that the theme is not simply “having data”, but building an information system capable of generating better, faster, and more reliable decisions. The real challenge is not possessing large amounts of data, but transforming it into value.
To generate value, data must be managed methodically. It is not enough to collect, archive, or visualize it in a dashboard. A structured process is needed that enables the transformation of raw data into reliable information, the information into knowledge, and the knowledge into operational decisions.
What problem do we want to solve? What performance do we want to improve? What decision do we want to support? Do we want to reduce machine downtime, improve OTD, reduce waste, optimize inventory, increase line efficiency, make forecast more reliable? Without a clear objective, the risk is collecting a lot of data but not knowing how to use it. The data must always come from a business question or a process requirement.
Once the goal is defined, it is necessary to identify which data is needed, from which sources it comes, who generates it, how frequently it must be updated, and at what level of reliability. Collection must be as close as possible to the point where the event occurs. The more manual steps are introduced, the greater the risk of error, delay, and duplication.
Data quality is not automatic. Before being used, the information must be cleaned, normalized, validated, and made consistent. This phase allows for the elimination of duplicates, missing values, inconsistent formats, insertion errors, and inconsistencies between different systems. It is one of the most important steps, because dirty data generate weak analysis and wrong decisions.
Exploration allows for the identification of correlations, anomalies, deviations, trends, and recurring patterns. This is where the organization begins to transform the numbers into understanding. For example, relationships can be observed between micro-stops and production shifts, between defects and batches of material, between delivery delays and product families, between energy consumption and operating conditions. This reading enables the transition from perception to objective knowledge of the process.
After understanding the behavior of the process, it is possible to build analytical, predictive, or prescriptive models. This is where advanced tools such as AI, machine learning, simulations, and optimization algorithms come into play. Modeling allows for anticipating scenarios, estimating risks, predicting failures, supporting scheduling decisions, identifying root causes, and suggesting corrective actions. But the model only has value if it remains connected to the actual process and if people know how to interpret its outputs.
The final step consists of making the data usable in decision-making moments. Dashboards, control rooms, alerts, automatic reports, digital workflows, and escalation systems must help people quickly understand what is happening, where to intervene, and with what priority. Visualization should not be decorative, but operational. A good information system does not simply display data; it guides action.
This 6-step process enables us to go beyond mere information availability and build a true data-driven culture. This is where AI and Big Data can generate value: not as a technological exercise, but as part of an integrated decision-making process.
AI finds its maximum potential when it is embedded in 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.
Together, these elements allow us to move from reactive management to preventive, proactive and predictive management. It is at this intersection that digital technology ceases to be a simple technological investment and becomes a measurable factor of competitiveness.
In maintenance, this means anticipating failures and reducing unplanned downtime. In quality, identifying deviations before they lead to defects. In planning, simulating scenarios and improving scheduling and forecasting. In internal logistics, optimizing flows, routes, and stock levels. In performance control, making root cause analysis faster.
But it all starts from a foundation: clear processes, reliable data, and eliminated waste.
AI does not replace the method. It enhances it. It does not eliminate the need for standards, roles, responsibilities, and routines. On the contrary, it requires an even more disciplined organization in the management of processes and information.
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 there must be a routine capable of handling it. A dashboard can make a deviation visible, but people trained to interpret it and intervene are needed.
Without this connection between data and action, digital transformation remains incomplete.
One of the most common mistakes in digital workflows is confusing visualization with improvement. A dashboard can make data more accessible, but it does not automatically guarantee better decisions.
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 prioritized, which thresholds trigger escalation, what actions must be taken, and how the effectiveness of decisions is verified.
The control room, daily meetings, moments of problem solving, escalation processes, and periodic reviews of performance are the places where data becomes action. It is there that the organization interprets deviations, identifies the causes, assigns responsibilities, and activates improvement. The dashboard alone does not improve anything; it only improves if it is integrated into a rigorous and regular management practice.
Unused data is a waste. Unmanaged alerts are a waste. A report produced but not discussed is a waste. A beautiful dashboard but not linked to decisions is a digital waste.
The real transformation does not happen when the company “sees” more data, but when it decides better thanks to that data.
Every AI project should start from the 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?
Only after answering these questions does it make sense to choose technology. Otherwise, the risk is to start with the tool and then look for a problem to apply it to. A well-defined project does not start with software, but with the need for improvement.
Lean & Digital Transformation is not the adoption of software or a platform. It is a path of redesigning processes, organization, skills, and governance. Technology is a lever, not the starting point.
This approach allows us to avoid three very common mistakes. The first is to digitize unstable processes. The second is to build information systems full of data but lacking meaning. The third is to adopt advanced technologies without creating the organizational conditions to truly use them.
Digital works when it enhances an already understood, simplified, and governed process. AI generates value when it works with reliable data. Big Data becomes competitive when it supports better decisions. And Lean remains the method that enables you to distinguish what creates value from what produces noise.
In an increasingly complex industrial world, the real difference will not be made by companies that collect more data, but by those that will know how to transform it into knowledge, decisions, and continuous improvement.
Lean waste and digital waste are two sides of the same problem: organizational energy wasted on activities that do not generate value. The former manifest themselves in physical, productive, and operational flows. The latter in informational flows, systems, data, and decision-making processes.
For this reason, digital transformation must start with an integrated approach: streamlined processes, quality data, clear governance, distributed expertise, and technologies that are consistent with business objectives. It is a matter of method before it is a matter of tools.
AI can become an extraordinary accelerator, but only if it finds fertile ground: stable processes, readable flows, reliable data, defined responsibilities, and effective decision-making routines. In this perspective, value arises from the alignment between people, processes, and data, not just from the mere availability of technology.
Because you improve what you measure. But you really improve only when you measure well, with reliable, meaningful data linked to value.
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Why apply technology to an inefficient process does not eliminate waste; it only makes it faster. Connecting systems with inconsistent data makes incorrect information circulate faster. That is why the Lean First, Digital Second, Value Always logic applies: first we simplify, stabilize, and standardize the process, then we digitalize what makes sense to enhance.
Digital waste is time, energy, and resources consumed managing unreliable, redundant, incomplete, or hard-to-find information. Typical examples are duplicate data across multiple systems, parallel Excel files, paper-based information, repeated manual entries, poorly used reports, KPIs calculated differently, and non-integrated IT and OT systems.
Muda indicates the obvious waste in production: defects, rework, overproduction, waiting times, unnecessary transport, excessive stock and handling. Mura is variability, that is, fluctuations in load and parameters that make flows unstable. Mura is the overload of people, machines and systems, which increases errors and operational stress. Digital technology does not eliminate them and risks replicating them.
The 8 V’s are Volume, Value, Veracity, Visualisation, Variety, Velocity, Viscosity and Virality. They describe the characteristics that an information asset must have in order to be useful: quantity, value, reliability, readability, variety of sources, speed, ease of extraction and the rapidity with which an alert reaches the right people.
There are six steps: defining the objective, collecting the data near the point where the event occurs, cleaning and validating it, exploring it to find correlations and anomalies, modeling it with predictive analytics or AI, and finally presenting it and automating it with dashboards, alerts, and workflows that guide action.
AI enhances a mature Lean system: it learns patterns, anticipates failures, improves scheduling and forecasting, and accelerates root cause analysis. It does not replace the method and requires clear processes, reliable data, standards, roles, and routines. Without a routine that manages forecasts and alerts, and people trained to interpret them, the transformation remains incomplete.
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