[Reprinted] exploring the elements of Maximo data collection

 

The soul of the Maximo system is data. Without comprehensive and accurate data, all functions of the Maximo system cannot be discussed. Therefore, data collection is the top priority of the Maximo system. How to scientifically and efficiently collect Maximo system data has always been our primary task of thinking and practice. Today, we will share some of our experiences and experiences in data collection with you, hoping to give you some reference and hope that experts can give us great support, point out our shortcomings so that we can improve. Some people say that building a good asset management system is like making a work of art. It can only be achieved through the meticulous work of skilled artists, one thing we want to do is to make this work a standardized job, just like a set of automatic processing pipelines, people who do not need high skills, and do not need many innovative ideas, to produce qualified products efficiently. Therefore, based on the actual work process, we mainly defined seven elements in the data collection process. These seven elements are the main indicators that determine the success or failure of data collection, data collection will go smoothly. Next, I will introduce these elements one by one. I, Data research. Data research is a process of understanding customer needs. Only by fully understanding the customer's needs can we collect data in line with the customer's requirements. What is a good asset management system? The customer's satisfied asset management system is a good system. Only by understanding what the customer wants can we create data that can meet the customer's requirements. Of course, data research is also a process of communication and communication. It is not just about understanding the customers. Some customers do not know the Maximo system very well, so some of the requirements that we take for granted will follow, in the process of data investigation, we also need to fully analyze the needs of these customers, and sincerely point out to the customers that the requirements are unreasonable and those are unscientific, the unreasonable and unscientific requirements will affect the application of the system and the system after the system is launched. Then, we will give reasonable suggestions for the customer and for the successful implementation of the system. There is a big difference in the quality of the data survey that has not been fully prepared. Some experienced engineers may find many problems during the survey and the two sides have reached many reasonable agreements, it lays a solid foundation for future work. Otherwise, if a person is not a very suitable person to conduct research, it will cause a lot of inconvenience to future work, in order to ensure the quality of data collection, eliminate the impact of human factors on our work, and turn data collection into a workflow that can be standardized, we have created many research templates in our database. These templates are obtained after discussion and demonstration. Using these templates for research can effectively improve the quality of data research, avoid missing key points for human reasons, which may affect future work. II, We know that everyone has different personalities. The data collection workload of the Maximo system is quite large and cannot be done by one person. It will always be the work of a team, how can we coordinate and unify the work style of each member in the team so that different members can work in a unified and coordinated manner? How can tens of thousands of pieces of data of an implementation object be collected without repetition or omission? This is also an important issue in data collection. It is also the reason why we must repeatedly emphasize the importance of standardization. It is only under the guidance of a scientific, reasonable, and detailed standard, different people can collect data in a uniform manner and ensure data quality. Our specifications are comprehensive and applicable. We have done a lot of basic work in this regard. I still remember that when we first started to contact data collection a few years ago, the specifications were incomplete and unreasonable, many of us have been working overtime to produce Specifications. Today, through continuous modification and improvement, our database contains all the standard specifications required for Maximo system data collection, including location, professional system location, equipment, PM, standard operation plan, and inventory items, when taking over the new project, we modify these standards and specifications based on different implementation objects and customer requirements, it only takes two or three days to come up with a scientific, reasonable, and practical specification, which greatly improves the implementation efficiency and quality. Of course, it is not all right to have a specification. With these things, we also need to organize team members to seriously learn these things and truly grasp the connotation of these specifications, ideologically aware of the role of these norms and consciously abide by them. Our requirement is that these norms should be truly integrated into their own, from nothing to nothing, remember it in your mind, instead of checking it out when you do it. 3. Data Template data templates are used to record data during data collection. On the basis of analyzing, summarizing, and classifying the data required by the Maximo system, we have developed a complete set of data collection templates. During the data collection process, collection Based on the project specified by the template not only ensures the accuracy and integrity of the data imported into the system, but also facilitates the use of data collection personnel during the data collection process, during the data review process, the data collection personnel can easily review the data, with clear layers and clear logic. The data import process is simple and easy to operate. IV, The data collection process of data organizations is a time-consuming, heavy-workload, and boring task. Many people do not want to do it. In fact, data collection is not a non-technical task. We always think that people who do the system can not understand the data, but those who do the data must understand the system very well, only those who understand the underlying structure of data and the role of data in system applications can collect good data. For example, data collection in Maximo systems, such as preventive maintenance plans and standard operation plans, requires considerable field work experience. The essence of Maximo systems is maintenance management, I think the standard operation plan made by people who have not done repairs may not be so practical. Therefore, in the data collection process, we make full use of the functions of field personnel with rich maintenance experience to create a complete set of common standard operation plans and preventive maintenance plans, it covers the maintenance and repair of the vast majority of common equipment in the oil and gas industry. Our goal is not only to guide the work of on-site maintenance personnel, but also to standardize the work of on-site personnel, to change their outdated maintenance concepts and methods, strengthen their security awareness, and enable them to correct nonstandard maintenance behaviors, while ensuring the safety of equipment and personnel, efficient, reasonable, and scientific maintenance of equipment to minimize costs. Many of our staff who have compiled this set of preventive maintenance plan and standard operation plan come from the site and have worked as excellent maintenance personnel for 10 or 20 years, their actual service experience, along with advanced maintenance concepts and quantified maintenance standards, also proved to have good results in actual use, it can be said that this set of things that combine our painstaking efforts is also a fortune we have created for the enterprise in the process of data collection. Although these things may still have some industry limitations, we found that this idea is correct. informatization requires not only IT technology and theoretical knowledge, but also actual work experience on the site, the value of Maximo lies in its field work. Therefore, Maximo will not have vitality if it is out of practice. During the data organization process, there will certainly be issues that were not imagined during the preliminary formulation of specifications. It is normal that some devices cannot be collected according to the specifications, to solve this problem, we must upgrade the specifications, constantly supplement and improve the specifications, and ultimately achieve the smooth completion of the project. It is best to conduct a concentrated discussion before modifying the specifications, comprehensively consider the situation of the entire project, justify the scientific nature and rationality of the modifications, and then proceed with careful upgrade of the specifications. V, Data review data audit is the last check before data is submitted to Party A. Therefore, it is of great significance. We generally have several stages: self-check, cross-check, and summary check, the content of the check includes all aspects of the data, whether the collected data is comprehensive, whether there is repeated data encoding, and whether the hierarchy is suitable. Because of the large amount of data, therefore, modifications to the data in the later stage may trigger a trigger. If the master location tree changes, then the professional system tree, Device Tree, and standard job plan may all need to be changed, therefore, we must pay full attention to data review. VI, Data Optimization because the industry continues to develop and the actual situation on the site is constantly changing, Maximo system data must also be constantly optimized to meet the actual situation on the site and the changing business needs of the enterprise. Continuous optimization of the Maximo system can ultimately create the unique core competitiveness of enterprises. The principle of data optimization is data-centric, data standardization, and data optimization. Therefore, the Data Optimization content also includes the above three aspects. Data-centric data is to adjust the data according to the actual situation in the Data Optimization process: Ø Add-new data is required for devices added on site Ø Modify the data of the on-site or decommissioned Device Ø Delete -- clear invalid data in the system Data Standardization includes two aspects: basic data standardization and maintenance data standardization. Basic data refers to the data collected before going online, which should be unified. Maintenance Data Standardization refers to the data generated after the system operation, such as the purchase order, procurement application and other data to be standardized, scientific and reasonable. Select, design, or adjust the components and relationships of the standard system under certain restrictions according to specific objectives to achieve the optimal effect, this standardization principle is called the optimization principle. Because of the lack of experience in implementing the Maximo project and the lack of understanding of business process principles, the data structure and related attributes are not perfect. Therefore, it is often necessary to follow the needs of senior leaders, business process changes are optimized for data processing. According to the needs of data organization, data in the system can be divided into static data and dynamic data. Static data reflects the basic attributes of Enterprise Resources, and dynamic data reflects the changes and movements of Enterprise Resources. VII, The application of the information library has already mentioned some of the content of the information library in the previous article. You may not be familiar with it. Next I will introduce the information library in detail. Simply put, the information library is a summary of the results and has been honed to become an information database with implementation guidance. Our Maximo information library is a summary of project implementation, data collection, and system development and analysis performed by the Maximo project team in recent years. It includes: data collection specifications, data collection templates, System Installation manuals, Maximo system implementation specifications, project management, and on-site actual data provide reference and guidance for the future implementation of Maximo systems. The basic establishment of the Information Library has optimized the information resources characterized by information sharing in Maximo implementation, database software and hardware maintenance in the future, and on-site oil and gas production, data uniqueness and accuracy are effectively controlled. The knowledge base in key business and key technical fields has been initially established, so that knowledge management can be standardized. Once enabled, all enterprises (functional departments and level-2 units) can be organically connected through a smooth internal network. The Information Library has the following features: 1, Comprehensive-all aspects related to system establishment 2, Specifications-various templates can be called from them, and the formats and content are unified. 3, Efficiency-improve efficiency and reduce costs 4, Practical -- from reality, the author is a person with rich experience 5, Continuous improvement-spiral improvement and continuous improvement 6, Resource Sharing-online publishing, free browsing from these features, it is not difficult to see that with the information library, it is very helpful for us to implement data collection projects. We can ensure the quality and efficiency of data collection and complete data collection in the shortest time. Of course, there are many other factors in data collection, such as a serious and responsible work attitude, a strict and dedicated work style, a hard-working spirit, data drawings, and other objective conditions, however, by grasping the above seven basic elements, we can lay a solid foundation for smooth and efficient data collection.