Solutions & Services

Ramentor – Services

With our solutions and services you can increase productivity, quality and cost-efficiency. By applying advanced RAMS methods and tools you achieve understanding about the complex and dynamic relations between failures and key performance indicators (KPI). With ELMAS and StockOptim we offer efficient simulation and analysis of dependability, and optimization of maintenance and spare part storage. This software family includes a wide variety of solutions for decreasing risk and improving overall efficiency.

Ramentor's expertise combines deep theoretical background of RAMS methods and tools with experience in challenging industrial risk assessment applications. Together with our customers we have analyzed RAMS and risks of, for example, paper and pulp mills, lifting equipment, data centers, pharmaceutical production lines, steel mills and processing lines, tyre production process, district cooling and heating plants, maintenance outsourcing, veneer production, propulsion equipment, nuclear plant, final disposal of spent nuclear fuel, particle accelerator, power transmission lines, material handling solutions and telecommunication networks. Cases have been made in design, realization and operation & maintenance stages to, for example, increase availability, decrease life-cycle costs, optimize maintenance plan, and assess the components' criticality.

Risk management

Risk management process and methods

Ramentor provides systematic risk management solutions for assessing the criticality of component failures and mitigating their overall effects. In the evolving operating environment it is necessary to perform risk management as a constant process. The enhanced process for recognizing and coping with the everyday risks is an essential asset for equipment manufacturers and process plants.

Risk Management
  1. ELMAS software provides a single user interface for various risk assessment tools. They support the methods for recognizing and managing the risks hiding in the operating environment. The risk management process contains following phases, which require different methods and tools:
  2. Risk identification
    1. Recognition of problems
    2. Modeling of event chains
    3. Estimating probabilities of events
    4. Estimating severities of consequences
  3. Risk analysis: Analytical calculation and stochastic simulation of risk
  4. Risk evaluation: Estimation of risk significance and analysis of alternative scenarios
  5. Risk treatment: Action planning and execution -> Risk reduction

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Expert services

Ramentor expert services

Ramentor expert services include (i) application and customization solutions related to ELMAS and StockOptim tools, (ii) modelling and analysis services, (iii) guidance and consultation of reliability theory and RAMS methods, and (iv) customer projects, such as root cause, criticality and dependability analyses together with deployment of ELMAS and StockOptim tools. Ramentor offers RAMS audit and RAMS analysis projects.
RAMS audit

RAMS audit

RAMS analysis

RAMS analysis

ELMAS solutions

ELMAS risk management solutions

Ramentor Presentation
The powerful ELMAS software and Ramentor's wide-ranging expertise encompasses various RAMS methods for risk management: Fault Tree Analysis (FTA), Life-Cycle Cost (LCC), Criticality classification, Failure Mode and Effects (and Criticality) Analysis (FMEA/FMECA), and Reliability Centered Maintenance (RCM). Ramentor provides trainings of the RAMS methods, and expert services to apply them for solving practical needs.

FTA – Fault Tree Analysis

Fault Tree Analysis (FTA) is a structured top-down method for the identification of causes, which lead to a system fault or other undesirable consequence. A fault tree is a graphical presentation of the logical links of component and subsystem failures that lead to an unwanted event. Quantitative FTA calculates explicit dependability parameters for the analyzed item.


LCC – Life-Cycle Cost

Life-Cycle Cost (LCC) includes all costs starting from the definition of the item to the moment of taking it off the use and wrecking or relocating it. Different costs can cumulate from manufacturing, use, education, maintenance and eventual end-of-life actions, and also indirectly from the downtime or losses caused by the item failures. Sophisticated LCC analysis should be performed as early as possible, as the majority of the costs will be determined based on the design decisions made in the start of the life-cycle.


Criticality classification

Criticality classification can be applied for estimating the significance of the functions and the equipment in the system. Criticality classification produces important information to support the operations of maintenance, design and acquisition sections. Ramentor has included a criticality classification tool in the ELMAS software.

Read more about ELMAS Criticality classification (unfortunately available only in Finnish): ELMAS-Kriittisyysluokittelu.pdf


FMEA/FMECA – Failure Mode and Effects (and Criticality) Analysis

Failure Mode and Effects Analysis (FMEA) is one of the first structured techniques for systematic failure analysis. FMEA can be extended to Failure Mode and Effects and Criticality Analysis (FMECA) by defining criticalities for the identified failure modes. Ramentor has included a FMEA/FMECA tool in the ELMAS software. This combination of FMEA/FMECA and several supporting modeling and simulation functions provides a novel perspective for applying the method.

Read more about ELMAS FMEA (unfortunately available only in Finnish): ELMAS-FMEA.pdf


RCM – Reliability Centered Maintenance

Ramentor has included a Reliability Centered Maintenance (RCM) tool in the ELMAS software for supporting an effective RCM that leads to improvements in maintenance operations. Together with the visual modeling and versatile simulation functions, ELMAS RCM is a flexible and user-friendly tool.

  1. The following list presents the seven basic questions that the RCM analysis aims to answer:
  2. What are the functions and performance standards of the object in its current operational environment?
  3. What will happen if the object fails (which functions will not be available)?
  4. What causes the lack or insufficiency of each function of the object?
  5. What happens when each failure occurs?
  6. What damages will each failure cause?
  7. What can be done to detect each failure early enough or to prevent it from happening?
  8. What must be done if a suitable preventive task cannot be found?

Read more about ELMAS RCM (unfortunately available only in Finnish) from a document that explains thoroughly how the RCM steps are performed with the ELMAS software in an effective and user-friendly manner: ELMAS-RCM.pdf

Internet of Things

Internet of Things (IoT)

IoT allows collecting various types of measurement data from the devices and store it to a certain predefined data storage. The collected data is then refined in a way that it can be used to provide useful feedback for single devices and also for the operation of the whole system. Enabling this IoT also includes different analytics that make it possible to analyze large amounts of data and to create practical actions based on the achieved results.
Creating basis

Creating basis for IoT

The startup phase of a new IoT system can take years until the system is properly defined and have collected enough event data to support all of the analytics. This phase of waiting for new data streams doesn’t though mean that advanced analytics can’t be applied. For example, ELMAS can use the already existing imperfect data with the help of local expert resources. This way it is possible to create the same improvement alternatives for the equipment as with the completed IoT systems, but in addition to this the analysis also provides important feedback for the ongoing IoT development.

With the results of ELMAS analysis it is for example possible to define which devices should be monitored to ensure the system operation and how much should be invested on the diagnostics on each device. Going through the old event history with the expert resources presents another major advantage by providing valuable information for the upcoming IIoT system. The information includes for example facts about the most problematic areas with the previous event data and gives understanding on what kind of data to collect in the future and in which form it should be transferred into the data storage.

All in all ELMAS brings more intelligence for the management and processing of the large data sets in the IIoT world. The analytics it provides allow to maintain and develop the reliability of the equipment and processes and therefore reducing the overall costs by improving the process efficiency. ELMAS software has a strong history in refining imperfect data to support the decision-making of Finnish industry organizations. The new data streams and the increased attention on data quality coming with the IIoT world further strengthens the possibilities ELMAS can provide for the development of operations.


Advanced analytics

ELMAS is a modelling and simulation software specialized in reliability management and it operates in the field of advanced analytics when it comes to Industrial Internet of Things. Thanks to IIoT the amount and especially the quality of the collected data will be increasing and therefore significantly reducing the work effort required to use advanced analytics. Also ELMAS will benefit from the increased quality and accuracy of the available data as the need for reviewing the imperfect history data using various expert resources is no longer as critical as before.

Advanced analytics allow organizations to prepare for future events in a preventive manner (predictive analytics) and to create versatile calculation models for different improvement alternatives and use the results acquired from these calculations to develop the overall operations of the organization (prescriptive analytics). When operating with completed IoT systems ELMAS provides assistance in understanding the consequences of different equipment events and event chains for the operation of the whole system. The cause-consequence models describing the equipment operations are created into the software and they utilize the history event data collected into the data storage. By simulating the models together with the event data ELMAS can provide a clear view on the expectable future behavior of the equipment considering their reliability and life-cycle costs. This way the available raw data can be processed into important knowledge about the most significant equipment risks and used to create profitability calculations about different investment options considered for the equipment or the whole process.



Spare part storage sizing is a part of risk management that is closely connected to dependability concept through the supportability of the system equipment. The storage optimization aims to minimize the costs coming from storing while making sure that the supportability of the critical equipment will stay on a adequate level. A StockOptim tool enables spare part consumption modelling and mass optimization.
Spare part consumption

Spare part consumption modelling

Spare part consumption

When defining the spare part consumption of a certain equipment entity it is possible to take advantage of an ELMAS model created of this entity. The root cause events (e.g. 'Pump X fails') in the ELMAS model can be inputted with a spare part consumption information that defines which spare parts are needed and with what probability, if the event is fulfilled. Normally the spare part need is defined to the root cause events of the model which makes it easier to define what spare parts are needed when the event comes true.

  1. Operations model:
  2. Creating an ELMAS model of the subsystem e.g. for a RAM analysis
  3. Defining the spare part needs for the root cause events in the ELMAS model
  4. Recognizing the critical events in the model with ELMAS software (event criticality defines the needed spare part criticalities)
  5. Simulating spare part consumption with ELMAS software
  6. Transferring the simulated consumption data to StockOptim software and inputting the rest of the required information for the optimization (i.e. item delivery time, purchase price and other common cost factors)
  7. Performing a StockOptim optimization for the item so that the storage requirement for the service rate equals the criticality of the event in the ELMAS model
  8. Going through the optimization results and checking unclear results with a more accurate simulation
  9. Taking into use the optimized storage strategies.

When performing the item optimization based on the ELMAS model it is important to check if the items consumed by the model have consumption also in some other locations excluded from the model. The consumption coming from these locations has to be added to StockOptim software so that the item storage strategy can be optimized with the true consumption data.

Mass optimization

Mass optimization – Optimize your whole storage

Mass optimization

When companies are expanding their processes also the need for more spare parts arises. As a result of this the amount of stock items in the CMS grows easily very large, and so does the storaging costs. Cutting down these costs without affecting production has proven to be a challenging task to most.

StockOptim software allows the effective optimization of a large stock item groups that provides information about the possible hidden problems among the items including larger than needed item amounts in the storage and risks coming from shortage situations.

Item optimization projects collect the history data of stock item usage from the storage CMS. In order to create approximate calculations based on the history data it should include a sufficient number of storage visits per each stock item. The possible flaws in the data are compensated by performing an overview for the optimization results. The overview is focused on the items that show in the optimization results that the current storage strategy (item reorder point and order size) does not fill the required service rate requirement (probability that the storage client gets all the needed spare parts from the storage).

  • Project goals:
  • Locating stock items that contain risks in their current storage strategies and to correct these strategies in a cost-optimal way
  • Locating stock items that has the potential to decrease their overall storing costs (lower stock levels, larger order sizes)
  1. Project phases:
  2. Transferring stock item history data from storage database to Excel documents
  3. Optimizing stock item storage strategies in StockOptim software
  4. Reviewing the optimization results together with the storage personnel and concentrating on the possibly located problematic storage strategies
  5. Changing the problematic stock item storage strategies to the storage system
  • The reviewing process takes into account that the required service rate can be lowered with some items, or the service rate value calculated by StockOptim from the item usage data is too low compared to the reality, if:
  • Item is non-critical and it is considered acceptable that the storage cannot serve all the storage clients perfectly (if item ABC classification is used, it might be considered to lower the current classification)
  • Item is usually taken from the storage by a storage client as a larger amount as is needed, and some of the items are stored to some unofficial local storage that cannot be seen in the system (true service rate is higher that the calculated one)
  • Item is ordered from the suppliers in different and varying order sizes than what the official storage strategy requires as the 'order size' input (true service rate is higher than the calculated one)