The True Cost of Downtime 2024 report prepared by Siemens shows that every hour of unplanned downtime in a large automotive plant now costs USD 2.3 million—twice as much as in 2019—and as much as 11 % of Fortune 500 companies’ annual revenue “evaporates” through downtime alone. Production executives are therefore turning to PdM: real‑time data analytics reduces downtime by 35‑50 % and extends asset life by 20‑40 %. Below we describe the eight most expensive challenges that the ConnectPoint platform addresses in practice, supported by data, concrete examples and case studies from Polish and European factories.
Why does Predictive Maintenance top the priority list for CIOs and production directors?
Although the motivation varies by industry, three numbers appear most often on management slides when maintenance is discussed:
- Fragile spare‑parts supply chains increase downtime risk. Deloitte’s 2025 Manufacturing Industry Outlook reports that, although average lead times for production materials have improved since the pandemic peak, they remain several percent longer than before 2020, and more than 35 % of manufacturers cite rising transport costs as a key operational challenge.
- Predictive analytics tangibly reduces failures. The Nucleus Research study Quantifying the Value of Predictive Maintenance shows that PdM programs can cut unplanned downtime by 35‑50 % and extend asset life by 20‑40 %.
- Return on investment arrives faster than in classic IT projects. According to the US DOE’s Operations & Maintenance Best Practices Guide, companies achieve a 10× ROI while cutting maintenance costs by 25‑30 %.
Experience in Poland and Central Europe – among others, from projects delivered by ConnectPoint – confirms that combining IIoT, AI/ML and sound engineering practice allows companies to move from a pilot proof‑of‑concept to measurable cost reduction across the entire machine park within a few quarters. Below we examine the eight most expensive problems that Predictive Maintenance effectively addresses.
The most common maintenance challenges in manufacturing
1. Distributed and inconsistent sources of operational data
In a typical plant, machine‑condition data are generated simultaneously in SCADA systems, PLCs, vibration loggers, CMMS solutions and Excel spreadsheets. Each environment assigns its own tag names, stores history in a different format and releases data at a different cadence. This fragmentation blocks rapid operational decisions, because every production meeting requires manual report consolidation.
Fragmentation is not merely a technical issue – it is also a business risk. Without a single “source of truth”, the production director may see a different failure count than the maintenance manager, while the IT team holds a third version of the story. Action plans can end up based on perception rather than fact. Predictive Maintenance eliminates this problem during its consolidation phase: raw signals are streamed into a shared real‑time data lake, tag names are normalized and failure histories unified. Only on such a foundation can reliable forecasts be built.
2. Delayed access to critical operational data
Project data collected by ConnectPoint and client interviews show that, on average, engineers spend 80% of their time gathering data and only 20% analyzing it. In practice this means hours of CSV exports, files copied to USB sticks and late reactions to process deviations. In a world where an hour of downtime in automotive already costs USD 2.3 million, every minute of delay carries a hefty price tag.
Predictive Maintenance systems invert this ratio. Machine data streams are ingested within seconds, and a ready‑made API makes them available simultaneously to maintenance, technology and IT. The business effect is a step‑change in work culture: engineers spend their time interpreting root causes, not hunting for numbers.
3. Late anomaly detection and lack of automatic alerts
Without a predictive analytics layer, a plant finds out about a problem only after the line has already stopped. Most often, the absence of automatic alerts on deviations from the norm leads directly to costly breakdowns. Considering the downtime costs mentioned above, even a few minutes’ delay in response can mean tens of thousands of dollars lost.
Machine‑learning models in Predictive Maintenance build a “health signature” of the asset—a mathematical description of vibration, temperature and energy consumption during stable operation. When any parameter drifts from the pattern, the system issues an alert before the deviation becomes visible to the operator. This early signal is not an abstract IT benefit but a real saving: it is far better to replace a bearing during a planned daytime window than halt an entire line at 3 a.m. on Sunday.
4. Unstructured data and lack of standardization
Even if data land in a single database, they can remain useless when tag names reveal nothing and process attributes are incomplete. In this case, semantic chaos prevents analytics from scaling. Imagine two identical packaging lines where a sensor in the same station is called “T‑123_TEMP” on one line and “P3‑Temp_A” on the other. The algorithm will not recognize these as twin signals and will lose them in mapping.
Standardization within a Predictive Maintenance strategy involves three steps:
- A tag dictionary describing exactly what each signal represents.
- A process ontology defining how elements connect into a whole.
- Data‑quality validation (ranges, units, precision).
Only after these stages can one expect reliable failure‑prediction models.
5.Lack of a digital twin of the production line
Linking signals to specific machine components is essential for predicting failures and benchmarking similar devices. A Digital Twin is a virtual replica of a physical object or system that is continuously updated with real‑world data. In Predictive Maintenance, a digital twin makes it possible to simulate scenarios, analyse machine condition and predict potential failures, enabling maintenance to be scheduled before downtime occurs.
For a production line, the digital twin is an interactive replica: every gear motor, gearbox and actuator has its virtual “socket”, and associated sensors feed data in real time. Engineers can see not only a raw vibration of 14 mm/s² but the vibration of that specific bearing in the tension section—complete with service history, load and oil temperature. This allows a degradation model for a “family” of devices to be created, and solutions developed on line A to be transferred to line B without laborious remapping.
6. Imprecise data‑access management
A common paradox in manufacturing: permissions that are too narrow block critical information from the line crew, while permissions that are too broad risk losing control over system changes. A mature Predictive Maintenance program builds a role matrix – operator, maintenance, planner, data scientist, management—and assigns each a precise scope of visibility and actions (view, comment, model edit). Every intervention is also logged, creating a digital audit trail.
In the long term, such discipline translates into model stability: configuration changes are intentional and verifiable, and knowledge of who, when and why made a decision is not lost in email chaos.
7. Difficult interpretation of operational data
Collecting data is only the beginning—the equal challenge is how to present them. Experience shows that the lack of intuitive visualizations lengthens reaction time to anomalies. Humans excel at trend graphs and colour cues but struggle with raw numbers scrolling like “Matrix rain”.
Modern Predictive Maintenance dashboards, including the one we offer in the Smart RDM platform, combine dynamic KPIs (MTTR, MTBF, OEE) with a line map and the predicted time to failure. Process engineers see red risk spots, while managers see the financial impact of each alert. This is not mere UX cosmetics; shaving a few minutes off reaction time at the costs mentioned above translates into a six‑figure annual benefit.
8. Operational data isolated from business systems (ERP/CMMS)
The final link in the chain of challenges is the separation of production from finance: a failure “lives” in SCADA, while its cost “lives” in ERP. This separation prevents full TCO (Total Cost of Ownership) calculation and hinders spare‑parts budgeting. Without integration, process and cost optimisation is obstructed.
When a PdM system posts an event directly to CMMS, which then feeds the ERP purchasing module, a closed loop is created: prediction → work order → spare‑parts reservation → invoice → ROI report. Every dollar spent on maintenance thus has its context in production data, and decisions on modernisation or supplier changes can be backed by hard analytics.
What do the numbers say? — benefits of implementing Predictive Maintenance
Before you look at the figures, consider the leverage involved: every hour of recovered availability or every percent shaved off maintenance costs flows straight to operating margin and delivery performance. The table below shows how quickly and tangibly PdM can impact a plant’s financial results.
| KPI (2025) | “Before” baseline | Result with active PdM* |
| Annual cost of unplanned downtime (UK + EU) | > £80 bn lost revenue | Potential 30–40 % reduction through failure elimination |
| Unplanned downtime (hours / year) | 100 % (reference line) | ↓ to 40 % of baseline hours |
| Total maintenance costs (labour + parts) | 100 % | ↓ 15–40 % |
| Spare‑parts inventory (value) | 100 % | ↓ ≈ 25 % via better demand forecasting |
| Production capacity lost to failures | 5–20 % of annual throughput | ↓ to 1–5 % (after model stabilisation) |
| Investment payback period (ROI) | Typical IT projects: 24–36 months | 6–18 months with PdM |
*Averages from industry‑reported deployments; the actual effect depends on line criticality, data culture and maintenance‑process maturity.
Results you can reliably estimate with Predictive Maintenance
The eight barriers described above form a coherent “hidden cost web” in a manufacturing organization. Implemented methodically—from data consolidation through standardization and digital twins to ERP integration—Predictive Maintenance cuts through this web.
The benefits are threefold:
- Financial – reducing unplanned downtime, measured in millions of dollars per hour in large plants.
- Operational – faster, more accurate response to events thanks to automatic alerts, visualizations and a data‑driven culture.
- Strategic – building a digital organizational memory that progressively strengthens the algorithms and eases knowledge transfer between shifts, lines and plants.
If even one of the challenges described is an everyday reality in your factory, it is worth treating PdM not as an IT project but as a pathway to resilient, predictable and scalable production—which we will be happy to support.
See how to boost KPIs from OEE to ROI—learn how to move from the first measurement to a fully‑fledged Predictive Maintenance system in just three months and materially raise your plant’s efficiency.
