Govern maintenance before it stops production
LOG iN Maintenance is a CMMS designed for manufacturing: it plans preventive maintenance, manages breakdowns, controls spare parts, supports calibrations and compliance and connects maintenance, production and warehouse in a single system. The result is more traceable, coordinated and measurable maintenance, with a direct impact on the plant’s operational continuity.
In every production plant, three ways of managing maintenance coexist. Understanding the differences is not an academic exercise: it is the basis for deciding which approach to apply to each asset and which tool is needed to do it.
Managing maintenance reactively means chasing breakdowns, working in a rush and losing control over assets, spare parts and priorities. LOG iN Maintenance was born to overcome this logic: it centralizes data, maintenance plans, work orders and intervention history in a single system, making maintenance more plannable, traceable and governable.
For each asset, LOG iN Maintenance defines intervention plans on a time, consumption or event basis. At the deadline, the system automatically generates work orders and assigns the activities to be performed. This way preventive maintenance does not depend on Excel sheets or people’s memory, but on structured and controllable rules.
The maintenance technician receives the assignment, performs the activity and records times, materials used and actions taken. At each intervention, the system updates the machine history and makes visible the status of planned, ongoing, completed or overdue activities. The result is more orderly, more reliable maintenance, less exposed to oversights or informal steps.
On the most critical machinery, LOG iN Maintenance also supports predictive logics integrated with sensors and IoT devices. When vibrations, temperatures or other parameters signal a degradation, the system generates an alert and allows planning the intervention before the problem becomes a plant stoppage. The intervention does not start from an emergency, but from evidence.
LOG iN Maintenance makes indicators such as MTBF, MTTR, availability and maintenance costs visible in real time. This allows better evaluation of priorities, identification of the most critical assets and more solid decisions on reliability, replacements and investments. This way maintenance stops being an opaque cost center and becomes a concrete lever for the plant’s operational continuity.
Maintenance has a direct impact on production continuity, but is often still managed with poorly structured tools.
Automatic work orders, tracked by execution, times and responsible parties
Complete digital flow: opening, assignment, cause, closing
Complete machine sheet with intervention history, costs and performance
Stock, reorders and requirements based on maintenance plans
Real-time dashboard on interventions, breakdowns and plant status
Updated KPIs: MTBF, MTTR, availability, cost per asset
The result is more measurable maintenance, more coordinated with production and more capable of preventing problems, instead of just managing them when they occur.
It is not a problem of competence, but of information architecture. When data is not shared, every function works well within its own perimeter, but the system as a whole loses continuity. To avoid this you need a platform that makes maintenance, production and stock visible and coordinated.
The maintenance plan is visible to production scheduling and vice versa. When an intervention is planned on a machine, production sees it and reschedules in advance; when production detects an anomaly or repeated micro-stoppages, maintenance receives the signal in real time. The coordination is structural, not based on phone calls.
When a work order is generated, the system checks the availability of the required spare parts on LOG iN WMS in real time. If the part is in stock, it is reserved; if not, the system signals it in advance to order it. After the intervention, the used parts are unloaded automatically and the stock updates without manual steps.
A preventive plan reaches its deadline: LOG iN Maintenance generates the work order, verifies the spare parts on the WMS, coordinates the stoppage window with MOM scheduling. The technician intervenes, records times and materials, the stock updates, the cost goes to the ERP. A single flow, one database, no manual reconciliations between separate systems.
The digitalization of maintenance creates value when it makes concrete results visible: more reliability, less lost time, greater plant availability and more readable costs. LOG iN Maintenance turns maintenance into operational indicators that help measure performance and make better decisions.
MTBF measures the average time between two breakdowns and helps understand whether asset reliability is improving.
MTTR highlights how much time is needed to intervene and restore the machine, revealing inefficiencies and delays.
Plant availability shows how much time a machine actually remains operational compared to the available time.
The maintenance cost per machine makes visible how much each asset weighs and supports more informed decisions.
The traceability of interventions, causes and recurrences allows identifying the most critical assets and preventing new stoppages.
These results depend on the software, but also on how it is implemented. This is why LOG iN is built with the AlfaProject.net Method.
FAQ - Frequently asked questions about maintenance software
CMMS stands for Computerized Maintenance Management System. It is the software that centralizes and structures all the maintenance activities of a plant: from planning preventive interventions to managing breakdowns, from asset registry to spare parts control, from KPI monitoring to reporting.
In practical terms, a CMMS for manufacturing serves to answer questions that without a structured system remain unanswered, or receive approximate answers. When was the last maintenance done on machine 12? How many breakdowns did line 3 have last quarter? Is the spare part for the press in stock? How much are we spending to maintain the painting plant? Are we respecting the preventive plan or sacrificing it to emergencies?
The value of a CMMS is not only organizational. It is economic: it reduces unexpected stoppages (because preventive maintenance does not get skipped), reduces spare parts costs (because they are bought in advance, not in an emergency), reduces the time dedicated to emergencies (because the process is structured) and provides the data to decide whether to repair or replace an asset: with numbers, not with opinions.
LOG iN Maintenance is the CMMS module of the LOG iN platform. Unlike a standalone CMMS, it is natively connected to production (LOG iN MOM) and the warehouse (LOG iN WMS). This means the maintenance plan is visible to production scheduling, spare parts are verified in real time against stock and every maintenance data point is part of the plant’s operational picture, not information closed inside the maintenance department.
The difference between preventive maintenance software and predictive maintenance software lies in the criterion by which you decide when to intervene.
Preventive maintenance intervenes at scheduled intervals. The criterion is time-based or usage-based: every X weeks, every X machine hours, every X cycles an intervention is performed: component replacement, inspection, lubrication, calibration. It does not matter if the component is still in good condition: the calendar says it is time and you intervene.
The advantage of preventive maintenance is predictability. You know in advance when the machine will be down for maintenance. You can coordinate stoppages with production. You can prepare the spare parts. The limit is that you intervene even when it is not needed (waste of time and materials) and you do not intervene when real wear is faster than expected (risk of breakdown between one intervention and the next).
Predictive maintenance intervenes based on the real conditions of the asset. The criterion is not a calendar: it is the data. Sensors installed on the machine collect parameters continuously, vibrations, temperatures, electrical absorption, pressures. A predictive maintenance software analyzes this data to identify degradation trends: when a parameter deviates from the norm progressively, the system signals that the component is approaching the failure point with enough advance notice to plan the intervention.
The advantage of predictive maintenance is precision: you intervene when it is really needed, neither before nor after. Both useless interventions and unexpected breakdowns are eliminated. The limit is that it requires sensors, historical data and a system able to analyze trends, an investment justified on critical assets, not necessarily on every machine in the plant.
In manufacturing practice, the two approaches coexist. Preventive is the base: applied to most assets with structured and traceable plans. Predictive is the higher level, applied to critical assets where the cost of the stoppage justifies the investment in monitoring. LOG iN Maintenance manages both approaches in the same platform, from a single interface.
Predictive maintenance in a manufacturing plant works in four steps.
First: asset selection. Not all machines justify predictive monitoring. You select those where the cost of the unexpected stoppage is highest: bottlenecks, expensive plants, machines in sequence where the stoppage of one blocks all the others. The selection is based on the breakdown history (if available) or on the analysis of the AlfaProject.net Method.
Second: sensor installation and data collection. For each selected asset, the parameters to monitor are defined according to the most likely failure modes. Vibrations and temperatures for rotating machines. Electrical absorption for motors and drives. Pressures and flows for hydraulic systems. The sensors transmit data continuously to LOG iN Maintenance. In many cases, the data is already available from the machine PLCs, without additional sensors.
Third: threshold definition and trend analysis. For each parameter two levels are configured. Attention threshold: the parameter is deviating from the norm, monitor more frequently. Intervention threshold: degradation has reached a level that requires action, the system generates a work order. The thresholds are calibrated on the basis of experience and historical data, and are refined over time as new data accumulates.
Fourth: intervention planning. When the intervention threshold is exceeded, LOG iN Maintenance generates a work order, with the same information as a preventive intervention: suggested activities, required spare parts, estimated time. But with a decisive advantage: the intervention is planned days or weeks in advance of the breakdown. The spare part is verified on LOG iN WMS. The stoppage window is coordinated with LOG iN MOM scheduling. You intervene in a controlled way, not in an emergency.
The measurable result is the reduction of unexpected stoppages on monitored assets. The reference KPI is MTBF (Mean Time Between Failures): if it increases after the introduction of predictive maintenance, the system is working. If the cost of avoided stoppages exceeds the cost of monitoring, the investment has paid for itself.
The integration of the CMMS with the MES and the WMS is the requirement that separates an effective maintenance system from an isolated one. In the manufacturing context, maintenance cannot live on an island: it must coordinate with production (to plan stoppages) and with the warehouse (to verify spare part availability).
With a standalone CMMS, these integrations are separate projects, connectors to develop, flows to synchronize, data to reconcile. With LOG iN Maintenance, the integration is native.
With production (LOG iN MOM). The maintenance plan is visible to production scheduling. When an intervention is scheduled on a machine, production sees it and reschedules in advance. When production detects an anomaly, a performance drop, repeated micro-stoppages, maintenance receives the signal in real time. When a breakdown occurs, the stoppage is recorded immediately in both systems. The coordination is structural, not based on phone calls.
With the warehouse (LOG iN WMS). When a work order is generated, the system checks the availability of the required spare parts in real time. If the part is in stock, it is reserved. If not, the system signals it in advance to order it. After the intervention, the used parts are unloaded automatically. If the stock drops below the reorder level, the purchase signal starts without manual intervention. Spare parts are managed like any other material in the WMS, with the same traceability, the same rules, the same visibility.
With the ERP. LOG iN Maintenance communicates with the company management system through the standard bidirectional connectors of the platform (SAP, Oracle, Microsoft Dynamics, AS/400). The ERP receives the final-balance data: intervention costs, spare parts used, maintenance hours per asset. Industrial accounting updates without manual entry. The CMMS does not replace the ERP: it feeds it with the operational data of maintenance.
Times depend on the scope: number of assets, complexity of maintenance plans, state of existing documentation, level of integration with production and warehouse.
As a reference, implementing LOG iN Maintenance on a medium-sized manufacturing plant typically takes 3-6 months from kick-off to go-live.
The initial analysis takes 3-5 weeks. Assets are inventoried (many plants do not have a structured registry: this phase creates it), existing maintenance plans are collected (even if fragmentary), the breakdown history is analyzed (if available), the target KPIs are defined. It is the phase of the AlfaProject.net Method applied to maintenance: mapping the current state, identifying critical issues, defining the operational model on which to configure the CMMS.
Configuration takes 4-8 weeks. Asset registration in the system, definition of preventive plans (frequencies, activities, spare parts, skills), work order configuration, dashboard and alert setup, ERP integration. If LOG iN WMS is already active, the spare parts integration is automatic. If LOG iN MOM is active, the link with production is immediate.
Assisted go-live takes 2-4 weeks. The system goes live, the technicians start recording interventions, the maintenance manager verifies the plans and the KPIs. The AlfaProject.net team supports the internal team to calibrate the rules and parameters based on the real data of the first weeks.
The path is progressive. You can start from a subset of critical assets, the machines with the highest breakdown rate or the highest stoppage cost, and then extend to the entire plant fleet. The registry grows over time. The plans are completed. The history is built. After six months, the maintenance manager has a database that did not exist before, and on which to build decisions.
The return on investment of a CMMS in manufacturing is measured on five impact items.
Reduction of unexpected stoppages. It is the main item. Every hour of machine downtime has a calculable direct cost: idle plant, personnel waiting, lost production, late order. Structured preventive maintenance reduces the frequency of breakdowns. Predictive anticipates them. Coordination with production reduces the impact of planned stoppages. The value is calculated by comparing the downtime hours before and after the introduction of the CMMS.
Reduction of spare parts procurement costs. Without planning, spare parts are bought in an emergency: at full price, with urgent shipping. With a CMMS connected to the warehouse, requirements are known in advance. Purchases are planned. Costs are reduced, typically by 15-25% on the annual spare parts spend.
Increase in plant availability. Fewer unexpected stoppages + fewer unnecessary preventive interventions (thanks to predictive) = more hours of actual production. Availability is the KPI that directly links maintenance to production capacity. One percentage point of additional availability can be worth tens of thousands of euros on a critical machine.
Reduction of management time dedicated to emergencies. With a structured process, the maintenance manager spends less time managing emergencies and more time planning improvement. The time saved does not appear in the income statement, but its impact on decision quality is significant.
Reduction of non-compliance risks. In regulated sectors, an untracked intervention or an expired calibration is an audit risk. Digital management eliminates the risk: every deadline is monitored, every intervention is documented, every certificate is archived. The cost of a non-compliance (penalty, recall, imposed stoppage) is typically a multiple of the investment in the CMMS.
The AlfaProject.net Method estimates the payback before implementation. The order of magnitude for medium-sized manufacturing plants with predominantly reactive maintenance is a payback between 6 and 18 months. After go-live, LOG iN Maintenance returns the same KPIs in real time: the ROI is a verifiable figure, not an estimate.
An integrated approach that combines data analysis, scenario design and real-time control to turn operations into a strategic lever and competitive advantage.
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