Management by objectives in industrial SMEs: from paper to ERP

In a meeting room near Guimarães, the map of the year's objectives was pinned up on an A2 card, written in blue marker. Five targets for production, three for sales, two for quality. The card was three months old. The production director looked at it and admitted, without drama: "This hasn't moved since February. Nobody knows where we stand." The problem wasn't a lack of objectives. It was that the objectives lived in one place — the card, the CFO's Excel, the owner's head — and the data lived in another: the ERP, the shop-floor terminals, the WMS. Between the two, a chasm of manual work.

The thesis of this article is simple and uncomfortable: the card doesn't die for want of will — it dies because nobody designed the data flow between the objective and whoever executes it. It's a problem of architecture, not discipline. As long as the objective is on the card and the result is in the ERP, the link between the two depends on a person who matches them by hand — and that person is always behind, because matching two systems manually is work nobody wants to do twice.

Put another way: management by objectives in a Portuguese industrial SME doesn't fail for want of method, but for want of piping between the number one wishes for and the number the machine produces. The method has been in the books for seventy years. What's missing is the plumbing — the architecture that makes the data rise from the production terminal to the supervisor's screen without passing through human hands. Fix the architecture and the method takes care of itself.

1. The real operational problem

Every industrial SME sets objectives. The owner wants to grow turnover by 8%. The CFO wants to lower the average collection period. The plant director wants to raise efficiency. None of this is controversial. What collapses is the link between the intention and the daily measurement — and that link is a matter of design, not effort.

There's a structural reason for this, and it's nothing romantic. The Portuguese industrial SME almost always grows up around one person — the founder who knows every customer, every machine and every margin of colour like the back of their hand. For fifteen, twenty years, that knowledge lives in their head and it works. The problem begins when the company grows faster than the capacity of a single head. From then on, objectives stop propagating by osmosis and need to be written down, measured and reviewed. It's precisely at that transition point that most industrial companies in the North stumble — because what existed was the founder's head as data architecture, and that architecture doesn't scale.

The card that dies in February

The typical cycle in a garment factory in the Vale do Ave is this: in January the plan is defined, an objectives map is set up, distributed by department. For six weeks there's energy. Then the order peak from a parent house arrives — Inditex, Decathlon, Tom Tailor — and the objectives map stops being a priority. Nobody goes back to it because updating it takes work. The numbers are in the ERP, but nobody has time to extract, process and pin them up. And the numbers are only in the ERP if the company has an ERP: according to INE, in 2025 only 53.7% of companies in Portugal with 10 or more people used enterprise management software (ERP). Close to half of the business fabric doesn't even have the place where the objectives' data should be born — the January card is, for many, the only system that exists.

The result is management by objectives that in practice is management by memory. Ask a section supervisor what the month's efficiency target is and they'll answer "keep pushing forward". This isn't laziness. It's the natural consequence of separating the objective from the system where the work happens — of never having designed the channel between one and the other.

Look at the calendar of a garment maker working for major clothing houses. The workload isn't linear across the year — it's a wave. The spring-summer collection peak crushes January to March; the autumn-winter one crushes July to September. In these months, the shop floor works at the limit, with extended shifts and subcontracting bursting at the seams. Nobody has the head to transcribe numbers onto a card. And it's precisely in those peak months that the objectives matter most — because that's where the whole year's margin is won or lost. The paradox is cruel: the manual system fails exactly when it's most needed.

The footwear case: 800 SKUs and no common denominator

In Felgueiras, a sample collection has between 800 and 1200 active references, crossed on three axes — colour, size and last. When the CEO defines "we want to improve the sample-to-order conversion rate", the next question is: convert what, measured how? If each salesperson keeps the numbers in their own way in their own file, the objective has no ground. There's no common denominator. The footwear sector is particularly cruel here: international buyers visit twice a year (men's in August, women's in February) and the decision window is short. An objective that can't be measured in real time during the sample season is decorative.

Let's dig into the conversion problem. A footwear factory in S. João da Madeira produces, for a collection, several hundred sample pairs that cost as much to make as if they were one-off pieces — because they are. Each pair is a prototype, with no economy of scale. If out of 1000 SKUs presented to buyers only 180 are ordered in series, the factory invested in 820 samples that yielded no direct return. Improving that conversion rate by five percentage points materially changes the year's result. But to improve the rate you first have to measure it rigorously — by buyer, by product line, by last family — and cross it with the real cost of each sample. No salesperson does this in their head, nor with an Excel that gets rewritten each season.

In footwear, the most important commercial objective — converting a sample into an order — is the one almost nobody can measure. It's estimated at the end of the season, from a distance, when there's nothing left to be done.

Textiles and the traceability that objectives ignore

In textiles, the common denominator is even more slippery. A dye works in the Vale do Ave manages dye batches, baths, colour recipes and shade corrections that rarely enter the company's formal objectives — but which are the real source of cost and waste. An objective of "reducing scrap" only has ground if the factory can trace each non-conformity to the batch, the bath and the machine that produced it. Without batch traceability embedded in the production system, the quality objective is a pious intention. And there's a new pressure rising: the EU Strategy for Sustainable and Circular Textiles will require, over the course of this decade, that brands demand documentary proof of the origin and processing of each piece. The dye works that can't prove its process loses the customer — regardless of the quality of the fabric.

The warehouse manager and the radio

In distribution, in the Lousada–Paços de Ferreira corridor, the objective "reduce picking errors" hits a human wall: the warehouse manager who won't let go of the radio for more than two hours and sees any new system as a threat to his rhythm. The objective is good. Implementation without instrumentation turns it into just another manual log sheet that he fills in badly, in a rush, at the end of the shift — if he fills it in at all.

This man has been with the company for fifteen years and knows the warehouse blindfolded. He knows where every pallet is without consulting any system. That's precisely why he resists: any system that forces him to stop to log something is, from his point of view, a loss of productivity — and he's not entirely wrong. The lesson we've learned in many distribution projects is concrete. In a distribution centre in the Paços de Ferreira corridor, each picking line was logged by barcode scan with the operator stopping, aiming the scanner and confirming on the screen — several seconds per line, multiplied by thousands of lines a day. We replaced the optical scan with automatic capture at the point of collection, embedded in the workflow the operator was already doing: the line logs itself without him stopping to log it. The warehouse manager stopped resisting when he grasped one simple thing — the new method didn't steal time from his shift, it gave it back. His team's throughput went up, and a warehouse manager who sees his own productivity rise goes from opponent to defender of the system. The rule we drew from it: when data capture doesn't add a single second to the work of whoever executes it, resistance evaporates. When it adds one, the system dies — and the fault isn't the warehouse manager's, it's that of whoever designed the flow.

An objective that requires manual work to be measured is a doomed objective. It measures itself or it isn't measured.

The invisible cost of not measuring

There's an argument we hear frequently: "we've always worked this way and we've always made a profit". It's true and it's irrelevant. The problem isn't the profit the company makes; it's the profit it stops making without knowing. A factory that doesn't measure its efficiency by line doesn't know how much the dead setup times between orders cost it. A distribution business that doesn't measure the cost per picking line doesn't know whether it's losing money on small orders from customers it thinks are profitable. These costs don't appear on the income statement as a line item — they dilute, hidden in the overall margin. Well-instrumented management by objectives isn't about controlling people. It's about making visible the money that's already being lost.

2. What exactly is management by objectives

Management by Objectives (MBO) is a management method in which the organisation defines concrete, measurable objectives, breaks them down across levels (company → department → team → individual) and evaluates performance by the distance between the result and the target. Peter Drucker formalised the concept in 1954, in the book The Practice of Management. The central idea: people work better when they know what's expected of them in numbers, not in adjectives.

Seventy years on, Drucker's mechanics remain correct and poorly applied. He warned of something most SMEs forget: MBO isn't a top-down control tool. It's a pact. The objective comes down from management, but the commitment comes up from whoever executes it. When the supervisor takes part in defining the target they're going to meet, they take ownership of it. When it's imposed on them by decree and without giving them the instruments to track it, they meet it on paper and ignore it in practice. The distinction seems subtle but it separates the systems that live from those that die.

MBO, KPI, OKR — what's different

Three acronyms are constantly confused. It's worth distinguishing them precisely:

• MBO — the overall method of defining and cascading objectives and evaluating performance against them. It's the umbrella.
• KPI (Key Performance Indicator) — the indicator that measures progress. It's not an objective; it's the reading instrument. "OEE" is a KPI. "OEE of 75% by June" is an objective.
• OKR (Objectives and Key Results) — a variant popularised by Intel and Google. A qualitative objective ("dominate the production of technical knitwear") anchored in 3-5 quantitative key results. Quarterly cycles, deliberately high ambition.

For a Portuguese industrial SME, the practical distinction is this: OKR works well in contexts of growth and product, with young teams used to reviewing objectives every three months. Classic MBO, anchored in operational KPIs the ERP already produces, fits better in a factory with an experienced workforce and annual cycles of commitments to parent houses. Don't force quarterly OKR on a garment maker where the collection rules the calendar.

A warning about OKR linked to a pay bonus: when an ambitious key result comes to determine the bonus, people stop being ambitious. They lower the bar to guarantee the bonus. That's why Google and Intel deliberately separate OKR from remuneration. In a Portuguese SME, where the production bonus culture is ingrained, this is a frequent trap: a continuous improvement system turns into a game of negotiating low targets.

What makes an objective real

An objective only exists operationally if it meets four conditions. It has a single, automatic data source (not a sheet someone fills in). It has a named owner — a person, not "production". It has a defined reading cadence (daily, weekly, monthly). And it has a consequence: someone acts when the number deviates. Fail one of these and you have a wish, not an objective.

The SMART criterion and where it fails in industry

Everyone knows the SMART acronym — objectives that are specific, measurable, achievable, relevant and time-bound. It's useful as a checklist, but incomplete for the shop floor. It's missing the dimension that matters most to an industrial SME: observability. What use is a perfectly SMART objective if the number that measures it is only available fifteen days after the month closes? A monthly OEE objective read on the 15th of the following month is technically measurable and operationally useless — because by the time the number arrives, the month can no longer be recovered. Observability in near real time is what separates an objective that changes behaviour from one that only serves the management report.

Lag objectives and lead objectives

There's a distinction that almost never reaches the SME and that changes everything: result indicators (lag) against effort indicators (lead). Turnover is a lag — it measures the past, and by the time you see it, you can no longer influence it. The number of samples sent to buyers this week is a lead — it measures an activity that has yet to produce a result. Objectives anchored only in lag indicators are frustrating: people see the bad number but don't know which lever to pull. Objectives that come down to lead indicators give teams something they can control today. A factory that sets "convert 22% of samples" (lag) without setting "respond to each buyer request within 48 hours" (lead) is asking for the result without managing the cause.

3. The landscape in Portugal today

The figures on the Portuguese business fabric explain why management by objectives rarely comes off the page. It's not a matter of will. It's a matter of digital foundations that still don't exist in half of companies.

According to INE, in 2025 only 53.7% of companies in Portugal with 10 or more people used enterprise management software (ERP). Almost half of the business fabric doesn't even have the system where objectives' data should live. If there's no ERP, there's no single source of truth — and without a single source, any objectives method rests on spreadsheets that diverge between departments.

The broader digital picture confirms it. According to the European Commission (Digital Decade, 2025), only 53.6% of Portuguese SMEs had a basic level of digital intensity in 2024, against the European target of 90% by 2030. And SMEs aren't a niche: they represent 99.9% of companies in the non-financial sector, 77.9% of employment and 58% of turnover (INE, 2023). The digital deficit of SMEs is the country's digital deficit.

The asymmetry by company size

The average of 53.7% ERP adoption hides an important reality: adoption isn't evenly distributed. Large companies in Portugal use ERP almost universally; it's the micro and small companies that pull the average down. This has a direct practical consequence for management by objectives. A footwear factory with 40 people is often in a dangerous limbo: it's already too big for the owner to have everything in their head, but it doesn't yet have a mature digital system to sustain formal objectives. It's precisely this band — the small growing industrial company — where the pain is greatest and where instrumentation has the highest relative return. Micro companies survive in the founder's head; large companies have already solved the problem; small industrial companies are in the middle, bleeding efficiency without knowing how much.

Productivity: the number that hurts

Productivity per hour worked in Portugal corresponded to around 67% of the EU average in 2022 (Eurostat). This structural deficit is precisely the terrain where management by objectives, well instrumented, has a direct impact — because objectives linked to real operational data push the efficiency that aggregate productivity reflects. A factory that doesn't measure its WIP, its setup times or its efficiency by line doesn't know where it's losing the hours the national statistic exposes.

It's worth dismantling a common misconception about this number. The Portuguese productivity deficit isn't primarily a problem of people working less or worse — the Portuguese work, on average, more hours than most EU countries. It's a problem of value generated per hour, and that value depends on organisation, invested capital and decisions that avoid waste. This is where management by objectives comes in: it doesn't make anyone work faster, but it makes the organisation stop wasting the hours it already pays for. Reducing a setup from 45 to 25 minutes, eliminating the rework of a poorly matched colour, cutting the time an order spends idle waiting for a decision — each of these gains is productivity recovered without asking anyone to run harder.

Textiles: stable exports, margins under pressure

Exports of the Portuguese textile and clothing industry reached €5,499 million in 2025, with a variation of -0.8% against 2024 (INE, provisional data, via ATP). Stability in volume, but with margins squeezed by Asian competition and by brands' sustainability pressure. In this context, management by objectives ceases to be a management luxury: it's the mechanism that separates the factory that optimises margin from the one that merely turns over orders. Anyone wanting to dig deeper into the sector's competitive context should read the challenges of textile industry 4.0.

Portuguese textiles are going through a strategic transition that makes objectives even more decisive. Reshoring — European brands bringing production back closer to home, fleeing the fragility of Asian chains and long lead times — is a real opportunity for the Vale do Ave. But that opportunity is only captured with fast response, flexibility in short runs and proof of compliance. A brand considering producing in Portugal instead of Asia wants, in exchange for the higher price, agility and traceability that Asia doesn't provide. The Portuguese factory that can prove, in numbers, that it meets deadlines, controls quality and traces batches has a commercial argument worth more than price. And proving in numbers requires, first, measuring in numbers.

Footwear and dependence on the season

Portuguese footwear, concentrated in Felgueiras, Guimarães and along the S. João da Madeira axis, exports the overwhelming majority of its production and enjoys a reputation for medium-high quality that distinguishes it from Asian volume production. But the structure of the business — collections presented twice a year to international buyers, series that depend entirely on those orders — creates a load volatility that punishes any amateur objectives management. The factory goes from underloaded to overloaded in weeks. Without a system that measures capacity, order book and conversion in real time, planning is guesswork, and guesswork in a sector of thin margins is expensive.

The funding exists — but it doesn't pay for the method

There are funding instruments flowing towards industrial digitalisation: the RRP in its Companies 4.0 component (€650 million reserved to directly support companies' digital transition, according to the Government of Portugal), COMPETE 2030, Norte 2030. Many factories look at these funds as the answer to the problem. It's a partial reading. The funds pay for software, hardware, implementation consultancy — the technical foundation. They don't pay for the management discipline that makes objectives live on after the consultant leaves. We see companies that applied for and received support for an ERP, installed it, and continue to manage by card, because the money bought the tool but didn't change the habit. Funding is a useful lever and should be taken advantage of — but with the clear awareness that the return depends on what the company does with the tool, not on the tool itself. And there's an operational detail that stalls many applications: the technical report. Technically valid projects get stuck for months in justifying result indicators — precisely because the company doesn't know how to measure them. Well-set-up management by objectives is, ironically, also what unlocks the bureaucracy of the fund itself.

The European fund buys you the ERP. It doesn't buy you the habit of looking at the numbers every Monday. That doesn't come in any application.

4. The implementation models

There are five ways to operationalise management by objectives in an industrial SME. They aren't equally good. They range from the worst to the best in terms of sustainability, but the right choice depends on each company's digital maturity.

From paper to instrumentation: five steps

The first three models share a vice: they depend on someone transcribing numbers by hand. That someone is absent, falls ill, changes roles or simply doesn't have time at month-end. The fourth reduces the problem but keeps latency. The fifth is the only one that solves it structurally, because the objective reads the data where it's born — in the financial ERP, in the production terminal, in the WMS. The difference between the steps isn't one of effort; it's one of architecture. Each step you climb is a design decision that takes a human hand out of the middle.

If your objectives system needs a hero to feed it every Monday morning, you don't have a system. You have a dependency.

Why the spreadsheet is a trap, not a solution

It's worth lingering on the second step, because it's where most Portuguese industrial SMEs that already "do management by objectives" live. The shared spreadsheet seems the rational, cheap solution: everyone knows how to use Excel, it costs nothing, it's flexible. And it's precisely that flexibility that corrodes it. Someone adds a column. Someone else changes a formula. A third saves a local copy to work offline and never returns it. After three months there are four versions of the same sheet and none of them is the truth. When the CFO and the production director argue in a meeting with two different numbers for the same indicator, the meeting stops being about management and becomes about reconciling sheets. The spreadsheet doesn't scale with the organisation — it degrades with it.

The mistake of buying the OKR app first

A pattern we see repeating: the company gets excited about a pretty OKR tool, subscribes to it, and three months later abandons it. Why? Because the tool can't read the ERP. Someone has to manually enter the key results. And manual entry in a Portuguese industrial context, with lean teams, is the kiss of death. The correct order is the reverse: first ensure the operational data is clean and accessible in an ERP; then build the objectives layer on top. Never the other way round.

Latency: the silent vice of the fourth step

The fourth model — KPIs extracted from the ERP through periodic reports — is where many companies stop, satisfied that they already have numbers "from the system". It's better than the sheet, but it has a vice that only appears over time: latency. A report that runs at month-end gives you a snapshot of a past that can no longer change. An efficiency objective that's only read on the 5th of the following month doesn't influence any shift's behaviour during the month that passed. The difference between the fourth and fifth steps isn't technological for the sake of fashion — it's the difference between managing while looking in the rear-view mirror and managing while looking through the windscreen. In production, where an efficiency problem detected on the day is corrected the next day, this difference is worth real margin.

5. How to assess whether your company needs it

Not every SME needs a formal management-by-objectives system tomorrow morning. A 12-person company where the owner sees everything can live well without cascading objectives. The turning point is the size at which the owner can no longer keep everything in their head — typically between 30 and 50 employees, or when there's more than one shift, or when there are branches.

Signs that you already need it

• Nobody in the factory can say, without consulting the computer, what the month's efficiency target is.
• Management reports arrive a week late and nobody acts on them any more.
• Each department has its own Excel with numbers that don't match between them.
• The CEO makes operational decisions by intuition because the data always arrives late.
• New people have been hired and there's no way to explain to them what's expected in numbers.
• The company loses parent-house orders because it can't prove, with data, its performance on deadline and quality.
• In management meetings, people argue about which number is right, instead of arguing about what to do with it.

The trio that decides: CEO, CFO and the IT hero

In a Portuguese family SME, the decision to instrument objectives isn't a committee's — it's three people in a room. The CEO, who is often the founder or the founder's child. The CFO, who controls the money and is suspicious of anything that promises a return without a number. And the IT lead — who rarely has a computer engineering degree, but has fifteen years of knowledge of the business and is the one who really knows where the data lives and why that ERP field was never filled in correctly. Ignoring any of the three dooms the project. The CEO gives the vision and the mandate. The CFO gives the budget and the rigour. The IT lead gives the technical feasibility and, above all, the memory of every previous attempt that failed and why. A vendor who talks only to the CEO and ignores the IT hero will hit the wall at the implementation stage.

Step by step

To diagnose whether your company is ready to instrument management by objectives, follow this sequence before buying any tool:

1. Inventory the existing data sources. List where the numbers that matter live today — financial ERP, production terminals, warehouse sheets, CRM. Mark which are automatic and which depend on manual entry.
2. Identify the five to seven KPIs that really move the business. No more. If you list twenty, you don't have focus, you have noise. For a typical factory: OEE, on-time delivery, WIP, defect rate, average collection period.
3. Check that each KPI has a single, reliable source. If the same number has two origins that diverge, resolve that first. Objectives over dirty data generate distrust and the system dies.
4. Assign a named owner to each objective. A person with a name, not a department. "Production" isn't responsible for anything; supervisor António is.
5. Define the reading cadence and the consequence of deviation. Who looks, how often, and what happens when the number strays. Without a consequence, the objective is decorative.

Complete these five steps and you'll know clearly whether your problem is one of method (objectives need to be defined) or of data architecture (the objectives exist but can't be measured without manual work). In the overwhelming majority of Portuguese cases, it's the second.

Data quality comes before everything

There's a mistake that invariably appears at the third step and that deserves its own emphasis: the temptation to build pretty dashboards over dirty data. If the ERP item records are incomplete, if operation times aren't parameterised, if work orders are closed in a rush with estimated quantities, then any objective built over this data is born rotten. And worst of all is the erosion of trust: it takes one supervisor seeing on the screen an efficiency number they know to be wrong to mentally discard the whole system for good. The first time the data lies, buy-in is lost. It's better to start with three KPIs of clean, reliable data than with ten KPIs of which half are suspect. The credibility of the system is built on the accuracy of the first number people see.

Prefer three objectives with data everyone trusts to ten objectives where half the numbers are debatable. Trust is lost at the first wrong number and doesn't come back.

6. What to choose and why (decision by company size)

The right recommendation depends on size and complexity. There's no single answer, and be suspicious of anyone who gives you one.

The question of vertical fit

An expensive mistake we see: buying a generalist ERP and then trying to mould into it the industrial objectives it can't represent. An ERP that doesn't natively model the three axes of colour-size-last in footwear, or the batches and dye-works traceability of textiles, forces you to work around the system with parallel sheets — and you're back to square one. Before signing, assess the vertical fit with the rigour described in how to assess vertical fit before signing a contract. A vertical ERP MULTI models these structures from the ground up; a generic one forces you into customisations that age badly.

The cost of these customisations is systematically underestimated. A generalist ERP forcibly adapted to represent a colour-size-last matrix accumulates bespoke developments that nobody dares touch again for fear of breaking them. When the manufacturer's update arrives, those customisations either prevent the update or force them to be redone — and the cost of maintaining them over ten years often exceeds what it would have cost to start with a vertical system. The footwear or clothing matrix isn't a configuration whim; it's the fundamental structure of the product. A system that treats it as an exception is permanently swimming against the current.

The BI layer: where objectives come to life

Measuring isn't enough — you have to see. An efficiency objective only influences behaviour if the supervisor sees it on a screen, updated, next to the current result. This is where self-service BI changes the game: instead of waiting for the CFO's monthly report, each manager consults their own panel whenever they want. A tool like Qlik Sense transforms the ERP's raw data into dashboards that any supervisor interprets in seconds. On which indicators to show and which to ignore, it's worth reading industrial KPIs in Qlik Sense.

There's a dashboard-design discipline that separates the ones that work from the ones nobody consults: each screen should answer one person's question. The line supervisor's screen answers "am I within or outside the efficiency target right now?" and nothing more. The CFO's screen answers "how is the margin and collection this month?". Cramming forty indicators onto a single panel because "it's good to have everything at hand" produces a screen nobody looks at because they don't know where to look. Less is more, and the right less per person is the secret to buy-in.

An objective the manager doesn't see on a screen every day isn't an objective. It's a number in a report they read once a month and forget.

When predictive AI makes sense — and when it's just marketing

At the top of the table is predictive AI, and here honesty is needed. For the overwhelming majority of Portuguese industrial SMEs, predicting with AI is premature while you still don't measure the present well. It makes no sense to invest in turnover or demand prediction when the base data is still inconsistent. Prediction amplifies the quality of the source data: good data gives useful predictions, bad data gives false confidence in invented numbers. Predictive AI gains real meaning when the company already has years of clean data and a volume that makes human intuition insufficient — large order books, many references, complex seasonality. Before that, the money is better spent ensuring the present is measured correctly.

7. Regulatory framework and applicable compliance

Management by objectives isn't a regulated matter — but the systems that support it move data that is regulated. Ignoring this creates risk where you least expect it.

Financial and tax data

The financial KPIs that feed objectives (turnover, margins, collection periods) come from the same system that issues invoicing. In Portugal, the software has to be certified by the AT (DL 28/2019, ATCUD) and communicate monthly SAF-T (Portaria 195/2020). When you choose the ERP that will serve as the source for objectives, AT Certification isn't optional — it's a legal prerequisite. An objectives system that pulls numbers from an uncertified invoicing system is building on legally fragile sand.

The practical advantage of financial objectives reading directly from the certified system is that the numbers match by construction with what's reported to the AT. There's no classic discrepancy between "what management says we invoiced" and "what the SAF-T shows". When the source is single and certified, the income statement and the objectives dashboard tell the same story — and the CFO stops losing days reconciling two truths that should be one.

Individual objectives and data protection

When objectives come down to the individual level — performance appraisal, targets per employee — GDPR and Law 58/2019 come into play. Performance data is personal data. It requires a legal basis, transparency and purpose limitation. If you use predictive analytics to anticipate turnover or absenteeism, the AI Act (EU Regulation 2024/1689) also comes into play, classifying AI systems applied to worker management by risk. An HR platform like pplPortal has to handle this data within this framework — and the employee engagement measured must be so with clear consent and purpose.

The AI Act deserves special attention because many companies still haven't grasped its reach. AI systems used to evaluate, monitor or make decisions about workers — including performance or departure prediction — are classified as high-risk. This implies concrete obligations: transparency for the worker, human oversight of decisions, technical documentation. A factory that decides to use absenteeism prediction to manage rosters cannot do so as a black box. The worker has the right to know it exists, for what purpose, and that the final decision is human. Ignoring this isn't just a fine risk — it's a risk of labour conflict and of erosion of internal trust that destroys any objectives system.

Security of the systems that hold the data

Objectives dashboards concentrate strategic information: margins, capacity, customers. They're a target. NIS2 (EU Directive 2022/2555, transposed by DL 65/2025) extends cybersecurity obligations to more companies, including suppliers in critical sectors. Certifications such as ISO 27001 and cybersecurity services cease to be a luxury and become a condition of doing business with demanding customers. A data lake with all the company's KPIs is as valuable to you as it is to whoever wants to steal it.

There's a dimension of NIS2 that catches many industrial SMEs by surprise: the obligation arrives via the supply chain. A factory may not be directly covered, but if it supplies a parent house or a customer that is, that customer will demand contractual security guarantees. In other words, cybersecurity compliance ceases to be an internal choice and becomes a commercial condition imposed from outside. The company that concentrates all its strategic objectives and KPIs in a single system is creating an asset of very high value — and has to protect it accordingly, with profile-based access control, tested backups and a continuity plan that has already been rehearsed, not just written.

8. How INFOS approaches this

We've worked with Portuguese industry for 36 years and our conviction was formed on the shop floor, not in slides: management by objectives in an industrial SME only works when the objective and the data live in the same technical ecosystem. That's why we don't sell an isolated objectives app. We build the foundation — an ERP MULTI that models textiles, footwear, clothing, metal and plastic in their real structures — and then the layer that turns that foundation's data into visible objectives.

In production, KORA Productivity captures efficiency and OEE directly from the shop-floor terminals, in real time. It's the difference between an efficiency objective that's read by the minute and one that's

Frequently asked questions

How can I tell whether my industrial SME needs an ERP to manage objectives?

If your objectives live on cards, scattered Excels or just in your head, and the real data is somewhere else, you need an ERP. The warning sign is when nobody can quickly answer "where are we on the objective?" without doing manual work. If updating the objectives map takes work because it requires extracting data from several places, it's time to change.

What's the difference between having objectives and having objectives that work?

Having objectives is writing "grow 8%" on a card. Having objectives that work is when each person on the shop floor knows what their target is, sees in real time where they stand, and that information comes automatically from the system where they work — without anyone transcribing it by hand. The difference is the data architecture between the objective and whoever executes it.

Why does my objectives card always die in February?

Because updating a card manually is work nobody wants to do twice. When the order peak arrives, the shop floor is at the limit and nobody has time to extract numbers from the ERP and pin them up. The card dies not for want of will, but because the data flow between the objective and the result isn't automated.

How does an ERP solve the management-by-objectives problem?

A well-configured ERP creates the "plumbing" between the objective and the result. The production, sales or quality data flows automatically from the shop-floor terminal to the supervisor's screen, without human hands. The objective stops being an isolated card and becomes linked to the real numbers, updated in real time.

What should I do if I don't have an ERP yet and can't invest now?

Start by designing the data flow: where the numbers are born (terminals, production sheets), where they live now (Excels, notebooks) and where they should arrive (to the supervisor, to the manager). Then automate that link with simple tools — scripts that extract data, linked spreadsheets, or even basic dashboards. The important thing is to eliminate the manual work between the objective and the measurement.

How does management by objectives work during the order peak?

If the system is manual, it doesn't work — it's precisely at the peak that it fails. If it's automatic (ERP data feeds the objective in real time), it works better than ever, because it's exactly in those months of pressure that objectives matter most. The paradox is that the manual system fails when it's most needed.

In footwear, how do I measure the sample-to-order conversion rate?

You need a common denominator: each SKU presented, each buyer, each product line. If each salesperson keeps numbers in their own way, the objective has no ground. The ERP or a CRM system linked to the ERP lets you trace each sample from production to the buyer's decision, crossing it with the real cost. Without this, improving the rate is impossible.

Sources

• Instituto Nacional de Estatística (INE) — Survey on the Use of Information and Communication Technologies in Enterprises (2025), data on the adoption of enterprise management software (ERP) in companies with 10 or more people
• Standard ISO 9001:2015 — Quality Management Systems: requirements, applicable to Portuguese industrial SMEs
• Standard ISO/IEC 27001:2022 — Information Technology: Information Security, relevant to data architecture and integrated systems
• Confederação da Indústria Portuguesa (CIP) — Sector reports on industrial SMEs, Portuguese textiles, clothing and footwear