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Practical experience. Implementation of an asset management system

This is the first post in a series where we will talk about our projects that we have encountered in life. In them we will share the essence of the project, what problems we encountered and what recommendations we can give.

For those who don’t like to read, below is the video version (but the most valuable recommendations are only in the text version)

The essence of the project

Implementation of a comprehensive asset management system IBM Maximo. The final goal is to minimize equipment maintenance costs while ensuring reliable operation of the entire facility.

Project Description

The IBM Maximo system is a great concept in its idea and functionality. It includes all the main business processes for organizing maintenance and repairs into its circuit:

  • Asset Management

All the features and tools you need to closely track and effectively manage corporate asset data and deployment throughout its lifecycle.

  • Management of scheduled and unscheduled asset maintenance work

Automation and digitalization of processes from the generation of applications and work orders to the registration of actually completed activities. Including recording of all defects for subsequent retrospective analysis.

  • Service management

Define service offerings, establish service level agreements (SLAs), more proactively monitor service levels, and implement escalation procedures.

  • Full support for contract work

Contractors, budgets, links to work, personnel, and so on

  • Inventory management

Access to complete information about asset-related inventories and their use.

  • Procurement management

Support all enterprise-wide procurement activities, including direct purchasing and inventory replenishment.

What are the overall benefits of this system?

Sources of return on investment in the first year after the project

Description of efficiency improvement

Result

Reducing the time spent searching and waiting for the required spare parts to be issued from the warehouse for MRO due to high-quality planning of material requirements

Time

Reducing the time required to prepare for work (searching for instructions, issuing work orders)

Time

Reducing the cost of purchasing materials and components for MRO - choosing the optimal supplier, manufacturer, optimizing the logistics supply chain

Money

One-time reduction of warehouse stocks, disposal and write-off of unusable or unused stocks, increase in warehouse turnover

Money

Sources of return on investment after accumulation of statistics (2–3 years of operation)

Description of efficiency improvement

Result

Reducing the time for “redoing” already completed MRO work through optimization of procedures for monitoring the execution of work, using more efficient contractors

Labour, Time, Money

Elimination of costs for unscheduled work by increasing equipment reliability achieved by improving quality and timely completion of work

Labor, Time, Money

Optimizing the timing of scheduled maintenance and repairs based on statistical data

Labor, Time, Money

Optimization of maintenance and repair technology based on data on work performed and corresponding costs

Labor, Time, Money

Reducing the time to find and eliminate failures by accumulating a history of failures.

Labor costs, Time

By maintaining data on faults and the time at which these faults become failures, you can move on to servicing some objects based on condition, which will provide significant savings on maintenance and repair and increase equipment reliability

Labor, Time, Money

By maintaining data on actual labor costs for MRO, you can optimize the staffing schedule of your own MRO employees and more clearly control the cost of work performed by contractors

Money

By reducing the share of urgent purchases (forecasting emergency stock), it is possible to achieve a general reduction in costs for the purchase of materials and spare parts for maintenance and repair.

Money

Thus, according to the AT Kearney agency, specializing in research in the field of production management, the implementation and use of EAM systems allows for:

  • 25–30% - reduce equipment maintenance costs

  • 31% - reduce the cost of emergency work

  • 29% - increase repair productivity

  • 21% - reduce the level of excess inventories

  • 17% - increase equipment availability rate

  • 29% - Reduce inventory shortages

  • 22% - reduce the share of overtime work

  • 29% - reduce the waiting time for materials needed for work

  • 29% - reduce the number of urgent purchases

  • 18% - save by getting better prices, as you have the opportunity to choose a supplier

As a result, the implementation of this system pursues the following goals:

  • building and complying with technical policies. maintenance and repairs (where it is necessary to carry out planned and where condition-based repairs);

  • digitize and make transparent the entire MRO process: planning and carrying out work (create standard templates, avoid the human factor), attracting employees, necessary competencies, purchasing components;

  • consider the economics of an object or asset, make decisions and determine strategies: where is maximum reliability and where is maximum profit.

But each system has both pros and cons. In short, there are two main disadvantages of this system:

  • the first minus is born from its plus - scale.

  • the second minus is the complexity of the interface and speed of operation.

Let's take a closer look at these disadvantages.

1. Scale

To effectively implement this system, you will need to include a large number of departments: maintenance and repair, operation, industrial safety, logistics, personnel and payroll.

Not all of them are specialized, not all report to one top manager. Do you think they are eager to take on work in another IT system?

That's right, no. You, of course, can include administrative resources, but there is a huge amount of manual work in this system. The consequences can be very sad.

The second consequence of this disadvantage is that you will need to work with people of completely different levels of digital maturity. Accordingly, there will be different reasons and levels of resistance . This means a different approach to overcoming it. We had to start with the basics - teach people to type.

The implementation of such a system, if we look at the Kinevin model , is a complex task. This means that it is extremely demanding to use the correct project approach, comply with basic requirements and work with risks .

In addition, such global automation creates a need for internal competence centers at each site. You need people who know the entire system and can train and advise all employees. A single centralized support service will not work. People won't call or write. They will get tired of formal, scripted answers. And there will be many such requests.

Plus, the implementation of such a system will lead to the restructuring of a huge number of processes. Otherwise, it will only create additional work when people will be forced to fill out “classic” paper documents and then work within the system. The result of this approach is sabotage. This means that two more competencies are needed: skills in modeling business processes and optimizing them in accordance with the principles of lean manufacturing

And one more disadvantage of such systems that everyone faces is the complexity of working with regulatory and reference information. Simply put, with reference books . No industry, no company has been able to completely solve this problem.

As practice shows, at one point you will decide to introduce the “other” item. For example, in the list of defects. Where will 90% of defects end up? Will you be able to analyze them later and make quality decisions?

What if you have several objects with the same equipment, but different people? The same pump will have completely different names and reference books of different levels. You will critically need data normalization. What if you have hundreds of thousands of records?

In our case, a similar system was implemented simultaneously at several facilities belonging to one company. And just imagine, in one of the workshops there were more than 2,000 technological positions and 6,000 assets. But this is only a small part of one object, of which there were 11 in total in our project.

Can you imagine how much it would cost to contract with a contractor who would agree to process all this manually, and then how much time it would take to negotiate to come to a common decision?

In total we have:

  • the need for valuable people who know how to communicate with both production and IT, and who thoroughly understand the entire system. Does this automation reduce your costs? Doubtful;

  • staff turnover. If the project is implemented with errors, with negativity and use of administrative resources, duplication of work on paper and in the system, people will start to leave. You will be forced to look for new ones, teach and so on in a circle;

  • inability to use end-to-end and high-quality analytics for decision making;

  • the need for high competencies in the field of project management.

2. Interface

Unfortunately, all systems of this class and from giants are practically not developed: from 2014 to 2022 the system has not changed, it has not become more pleasant ( UI design also affects staff acceptance), it has not become more convenient ( UX is generally a key parameter, but later). She was frozen in time.

Let's look at a typical work screen interface that your staff will interact with. Below is an example of a work plan.

https://soware.ru/products/ibm-maximo

Do you think the staff will be able to accept him easily? Does this look like the interface of an iPhone or VK?

How long will it take to train 40-50 year old employees so that they quickly navigate this system? In all these magnifying glasses, double arrows, status postings?

But at the same time the system is flexible. We ourselves wrote technical specifications for working modules and screens. The more intuitive you make them, the easier it will be for your employees to work. This means that the system will be filled with data of a higher quality.

And minimize the amount of manual data entry. First, workers will spend less time working, which means there will be less resistance. Secondly, there will be fewer errors, and the data will be more structured, of higher quality and suitable for analysis. Ideally, you need a system that will collect big data and then automatically generate defect records with all the attributes, answering the questions “what, where, when.”

Unfortunately, the level of digital literacy among site employees leaves much to be desired. We, as we said earlier, generally taught employees how to print from scratch. Imagine what such users will do in the system.

3. Speed

Unfortunately, if you implement data storage in a centralized center that is quite remote, or the Internet channel is overloaded, you will face one big problem - the speed of the system.

The problem begins with the fact that the system takes a long time to process user requests. What will be the reaction of the foreman or head of the maintenance and repair department to the fact that the system thinks for 1-2 minutes after pressing, even if this is one request out of ten?

But we think you're wondering what problems we had. Is that project ultimately successful?

Let's start with the second question and smoothly move on to the first. We think not. Why? Here you can only give a personal opinion, which may be incorrect. Over the 3 years of the project, we have not seen any changes in the approach to planning maintenance and repair. They did not happen even 3 years after we completed our work. Many changes, as often happens, remained only on paper. And the reason for this is the lack of competencies in project management: work on success criteria, planning, work with communications, risks, synchronization with the regulator’s plans and further digital initiatives.

What we mean by this:

  • At the initiation stage, project goals and success criteria are not initially defined

Result: revision of KPIs during project implementation and their uncontrolled growth. During weekly meetings we discussed more than 20 indicators and it lasted 3-4 hours. Do you think anyone was able to think throughout this entire process and take something useful out of it?

  • Project risks not assessed

For example, the need to keep a journal of orders only on paper. As a result, the operational staff manually typed everything on a PC and then wrote it in a journal. We fight for automation and cost reduction, but get double work.

  • At the planning stage, it was not clearly defined what stages we were going through, how we would train users, when, where. How we pilot, how we scale

The result is that plans were constantly changing, and no one understood what the next step was, why all this was needed, how many people needed to be allocated and for how long.

  • Centralized implementation and directive management style

A typical reaction is that people go on the defensive and begin to perform tasks formally. In the end, everything is great on paper, but in reality it’s as always.

  • No attention was paid to working with data quality, a unified model

Here are 3 specific examples:

  • Even at the stage of data collection and digitization, it became known that the Russian Ministry of Energy was going to introduce a new approach to assessing the technical condition - through dividing equipment into functional units. And, unfortunately, this fact was ignored. As a result, the description of the equipment within the company did not correspond in any way to the calculations using the regulator’s methodology. At the same time, let me remind you that here it is the regulator who sets the rules for how and what equipment you must repair;

  • In the middle of the project implementation, it was decided to launch another project - the Mobin software and tool complex. It was supposed to become a data source for predictive analytics in Maximo. But the initially adopted data model in Maximo did not allow the creation of technological routes to Mobin;

  • there was no MDM solution and/or data policy. As a result, the same equipment was called by different combinations, entries in the defect logs did not help the analysis in any way.

  • People, leaders in the Societies, were not taught either project management or the psychology of management and overcoming resistance

  • The wrong organizational structure was selected

Despite the fact that this was a large project requiring complex development with a large number of participants, a loose matrix structure was chosen.

  • Simultaneous implementation at all sites aggravated the situation

Despite the fact that pilot sites for implementation were also selected, this did not help in any way to avoid mistakes and accumulate best practices.

And this is one of the key requirements. In such projects we must remember about Moore's abyss

There are always 10-15% “pioneers” in a company; the rest should be given already proven solutions

Personally, as a result of the project, we did not see any associated effects that would bring at least some benefit. They say that in that project people still convince themselves that the system helps them.

Recommendations for similar projects:

  • Spend 30–40% of the project’s time at the initiation and planning stages to clearly define the project’s goals, success criteria, boundaries, risks and strategies for dealing with them, defining stages, required resources, and distribution of authority. It is cheapest to fix errors in the initial stages.

  • Train those responsible for each facility in the basics of project management and working with resistance

  • Train people to work with data so that they understand its importance from the very beginning, from the moment of collection, the basics of lean manufacturing , so that processes can be restructured

  • Take an inventory of the main business processes and describe them. Find where there is duplication of information, how it can be removed, and processes simplified

  • Determine what data you need and for what decisions. Define your data model.

Example. Even focusing only on money, and without other systems that collect data from equipment, you can make effective decisions. But based not on meeting the costs of the repair fund, but on comparing the cost of ownership of two identical assets, but with different maintenance policies. We service one properly, the other we operate to capacity without any maintenance. service and then repair. Then we compare the results and ask ourselves, what is more profitable for us?

And if you add data from Mobin, which we’ll talk about later, introduce big data collection and add predictive analytics, you’ll get a truly new approach to planning maintenance and repair, and not an imitation.

Now, if you count how much money endless meetings eat up, how much was spent on training, business trips, licenses... you can build a new facility or pay for a repair fund for several years.

  • Review current regulatory regulations. Their plans. Synchronize your plans and data model with them.

  • Start implementation in small stages and at individual sites. Practice standard approaches to identify all problems, and then implement them using proven solutions.

  • Create a team of methodologists that will become a knowledge center and will be able to solve all the problems of the new stage, record them, and train the support group at the facilities to scale the pilot.

But in addition to all the recommendations above, there is a more radical approach - the implementation of several more targeted systems with a common database, a system bus for interaction and a BI system with customized dashboards and reporting for analytics and management decision-making.

The smaller the project, the higher the chance of success, this is also confirmed by project management statistics from the Standish Group.

You will need a simpler organizational structure for implementation, less resistance from staff, and less cross-functional interaction, and all this leads to less dependence on project competencies and improved planning quality. In addition, further development, modernization or replacement of individual systems will be easier and cheaper, and the cost of an error will be lower. It will be much easier and faster to cancel or change the project. The key challenge in this approach is to work out the target data model and collect big data from the equipment, with a minimum amount of manual input and work. The fewer people you need to connect, the better.

What is needed to implement such an approach? You must first determine:

  • key task. What we want?

  • list of responsible persons based on org. structures

  • what decisions do they need to make?

  • what data do they need?

Based on this, work out the data model and configuration of solutions / modules of the complex system. Moreover, this system may consist of different solutions.

Summary

The advantage of implementation is the ability to create a single information loop for asset management:

  • definition and compliance (reliability, profit throughout the entire life cycle) of the asset management policy

  • management of scheduled and unscheduled asset maintenance work

  • full support for contract work

  • inventory and purchasing management

Disadvantages and bottlenecks:

  • Demanding requirements for the implementation methodology and its implementation, due to the large number

  • Complexity of interfaces and work for operational personnel and engineers

  • Demanding architecture – risks of slow work


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