Terotechnica Asset Management College - Study Information

Diploma Ceremonies

Diploma Ceremonies

Diploma Ceremonies

Diploma Ceremonies

Diploma Ceremonies

Diploma Ceremonies

Diploma Ceremonies

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We understand that the cost of study may cause many students to rather forego their ambitions. We thus provide you with possibilities of obtaining study loans and getting discount for study.

1. Study loans

Disclaimer: The content below regarding study loans are obtained from Fundi. We made sure that we copied it correctly to this website page for the convenience of students, but we do not take responsibility for any difficulties that you encounter with that institution.

Study loans may be obtained from Fundi (https://fundi.co.za)

To get such a loan, the following criteria should be abided by. Please take careful note of the following below…..

To qualifying for a study loan and/or a loan to purchace a study device (such as a laptop) with Fundi:

• you need to be permanently employed.
• you need to be accepted or registered with a training institution.
• you need to earn at least R2500.00 of disposable income after deductions.

Required documents that should be submitted with the application:

• Recent payslip (not older than 3 months).
• In the case of applying for a study loan, a statement of fees or quote from Terotechnica.
• In the case of a device loan, current proof of registration with Terotechnica.
• Copy of ID document.

Application forms can be downloaded here:

1. Fundi loan application form
2. Fundi dependent affidavid

Completed application and documents needs to be send directly to This email address is being protected from spambots. You need JavaScript enabled to view it.

NB: You have up to 36 months to pay back the educational loan. 

Devices can be obtained from https://shop.fundi.co.za/

2. Discounts

Private students are eligible for discounts under certain circumstances. 

There are two ways that a private student can receive discount from Terotechnica: Immediate Payment and Placement Payment.

1. Immediate Payment
In a case of an immediate full settlement of the total amount of the quotation, the student is entitled to 30% discount. The “immediate full settlement” is then the full amount of the quotation, less 30%. This offer is only valid for five (5) days from the date of the quotation.

2. Placement Payment
If the student for some reason cannot do immediate/full settlement payment, as described above he/she does not qualify for the full 30% discount but can still receive a discount according to the following schedule below, on payment of a R2000 placement fee.
The discount that can be secured is as follows:

• 25% if the placement fee is paid within the first five (5) days of the date of the quotation.
• 20% if the placement fee is paid between day six (6) and day ten (10) of the date of the quotation.
• 15% if the placement fee is paid between day eleven (11) and day ten (15) of the date of the quotation.
• 10% if the placement fee is paid between day sixteen (16) and day twenty (20) of the date of the quotation.
• 5% if the placement fee is paid between day twenty-one (21) and day twenty-five (25) of the date of the quotation.
  

Nevertheless, the total price of the specific course, less discount secured, must be paid before course attendance.

N.B the discount is only available on short and comprehensive courses (diplomas are already priced at a discount).

Privacy Policy

We at AM-Tech Consulting Engineers and the Terotechnica Asset Management College are serious about our responsibility in securing the integrity and confidentiality of your personal information in our possession. We have taken all reasonable and technical measures to prevent the:

1. Loss of and/or the unauthorised destruction and/or
2. Unlawful access to and/or processing of your personal information.

We will in no way provide others with the confidential information you share with us.

You can navigate to our Home Page if you wish.

A PDF Copy of this page is also available.

The analysis of data is one of the most important activities in modern asset management. It can make the difference between success and failure.

1. Introduction

The Data Analytics in Maintenance course aims to create an appreciation of analytics in the context of a rapidly increasing data driven world. The South African mining industry was historically characterized conventional labour-intensive mines. Technology has been widely adopted and in 2021, most new mining projects are heavily mechanized and rely on various technologies, albeit with different levels of adoption.

This wave of mechanization has drastically increased complexity in terms of:

• supply chain systems: machines have thousands of parts and components
• training and skill development: the variety of assets are increasing fast
• cost: managing costs and operational efficiency has become more critical
• operational visibility: identifying operational bottlenecks in an environment with many complex coupled variables will need more methodical approach

These demands are not only applicable to maintenance aspects in mining, but also in any capital-intensive industries where equipment downtime and marginal changes in the operational expenditure can threaten the bottom line.

The course is a jargon-free practical guide to creating valuable and actionable insights to optimize the asset management effort. We have intentionally elected to create content that will accommodate learners with limited statistical training while limiting the primary tool of analysis to Microsoft Excel. The course will cover the whole analytics process using practical hands-on examples.

2. Course Duration

 The material is taught in 5 days with a daily 8 hour per day schedule.

3. Prerequisite

Learner should have a working knowledge of computers ( internet, Microsoft Excel and email).

4. Course Content

4.1 Module 1: A brave new world

Data analytics and concepts are introduced. The learner is exposed to how world-class maintenance professionals are using data strategically in business. The data science analytics process is explained and contrasted to how the course aims to address the various stages of the analytics process.

Topics:

• The analytics process
• Business case studies
• The changing maintenance landscape
• Maintenance 4.0 technologies
• Organizational data analytics maturity
  

4.2 Module 2: Performance Measuring

This module discusses how and where an organization can begin its digitalization journey. The performance measurement is a natural starting process in starting up a concerted analytics process. The themes covered outline how an organization can effectively use performance measurements to identify risks, opportunities, foster innovation and drive continuous improvement.

Topics:

• Maintenance and operational indicators
• How maintenance key performance indicators (KPIs) influence strategic outcomes
• How to make winning KPIs
• How to make winning KPIs
• How to dig deeper in the KPIs to understand what is driving them
• Understanding historical and predictive indicators
• Planning for effective performance measures
• Cascading strategic objectives through performance measures

4.3 Module 3: Data Sourcing

This module introduces the Extract, Transform and Load tool through Power Query. This tool is used to automate much of the data sourcing process and to a extend the data cleaning process. We use hands-on practical data to work on.

Topics:

• Various types of structured data and how to digitalize them
• Data definitions through the knowledge pyramid
• Meta data management
• How to use Power Query to;
  • Connect to various sources (online, folders, worksheets and database)
  • How to transform data and automate the data preparation
  • How to reduce manual operation when merging and appending data
  • How to make regular reports to self-update and safe valuable time

4.4 Module 4: Cleaning Data

Most collected digital data that is not analysed is in the format that is not easy to manipulate. It could be some parts are missing, aggregated, the format is not usable to the analyst and no consistency in how the records are captured. This chapter gives the learner tools to make the data usable for the analytical process.

Topics:

• Dealing with the top 5 data quality dimensions:
  • Accuracy
  • Completeness
  • Consistency
  • Validity
  • Uniqueness

Practical examples on MS Excel and understanding functions to clean data

4.5 Module 5: Data Discovery and Analysis

Analysis is the heart of the analytics process. In this chapter we use methods to make the data to tell a compelling story. This module uses a practical examples where various data sources are integrated and prepared to solve real maintenance problems. Although many operations have some form of visualization reporting, the student is introduced to diagnostics and predictive analytics processes.

Topics:

• Framing maintenance questions into analytical projects
• Creating focus to find maintenance insights
• Data aggregation techniques
• Creating visuals that tell a compelling story
• Going beyond descriptive analytics
  • Diagnostic analytics and finding relationships within the data
  • Prescriptive diagnostics: how to create data models
  • Introduction to regression
• Time-series analysis
• Developing hypothesis
• Practical exercises to find meaning in otherwise latent data

4.6 Module 6: Trend Analysis Framework

This framework demands all the skills picked up from Module 3 to Module 5. It covers a novel method to track the consumption of 1000s of parts in a non-graphical method. This ingenious trending method can be used to answer questions such as: what is pushing the cost curve, which failure modes are on the rise and which parts must we reduce or increase in the warehouses

Topics:

In an operation that uses 10,000+ individual coded items such as spare parts, raw material and fuel, an engineering manager is interested in understanding what is causing maintenance costs to rise in spite of many initiatives by the team. He ropes in an analyst to assist the reliability engineer to conduct a spend analytics project.

Tracking an individual item using time-series graphical plots may be adequate, however, when looking at 1000s of items, a graphical approach becomes rather tedious because;

• Creating 1000s of graphs is not practical to do or interpret
• Graphical information still needs further analysis to determine the trend
• Graphical information methods cannot be filtered or manipulated

This analysis framework opens up many possibilities in analysing trends in a powerful method.

4.7 Module 7: Formulating a Data Strategy

Data analytics is prevalent in some form in every enterprise. However, the maturity and benefits derived are not consistent. In the maintenance realm, there are countless amount of data generated within the CMMS system, the ERP and the production environs. This module shortly provides a brief overview on how an organisation as a whole or a maintenance department can take intentional steps to make the most of the data in the organization through crafting a Data Strategy. Aspects of the Data Strategy are discussed in a jargon-free manner. The discussions are aimed at maintenance professionals and not IT professionals.

Topics:

• What is a data strategy
• Benefits of conceptualizing a data strategy
• Data strategy formulation steps
• Questions to create a data-driven operation vision
• Reviewing the current data utility
• A brief overview of what good data maturity looks like
• The four types of analytics
• Practical maintenance data considerations
• Data governance and how to leverage ICT models
• Creating a roadmap
• Typical Data Analytics jobs
• Typical Data Analytics software and tools

Who Should Attend

This course is intended for anyone who has to work with data and understand the fundamental meaning of data. These include Asset Managers, Maintenance Managers, Maintenance Engineers, Managers in control of Asset Management, Managers in control of Maintenance Stockkeeping and Purchasing, Maintenance Supervisors, Maintenance Planners, and many others. It can play an important role to prepare maintenance and operational personnel to understand the fundamental asset management/maintenance organisation's problems and enable them to manage and direct the organisation successfully.