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Data quality: How do you quantify yours?

Data quality: How do you quantify yours?

Data quality: How do you quantify yours?

Being able to measure the quality of your data is a vital to the success of any data management programme. Here, Peter Eales, Chairman of KOIOS Master Data, explores how you can define what data quality means to your organization, and how you can quantify the quality of your dataset.

In the business world today, it is important to provide evidence of what we do, so, let me pose this question to you: how do you currently quantify the quality of your data?

If you have recently undertaken an outsourced data cleansing project, it is quite likely that you underestimated the internal resource that it takes to check this data when you are preparing to onboard it. Whether that data is presented to you in the form of a load file, or viewed in the data cleansing software the outsourced party used, you are faced with thousands of records to check the quality of. How did you do that? Did you start by using statistical sampling? Did you randomly check some records in each category? Either way, what were you checking for? Were you just scanning to see if it looked right?

The answer to these questions lies in understanding what, in your organization, constitutes good quality data, and then understanding what that means in ways that can be measured efficiently and effectively.

The Greek philosophers Aristotle and Plato captured and shaped many of the ideas we have adopted today for managing data quality. Plato’s Theory of Forms tells us that whilst we have never seen a perfectly straight line, we know what one would look like, whilst Aristotle’s Categories showed us the value of categorising the world around us. In the modern world of data quality management, we know what good data should look like, and we categorise our data in order to help us break down the larger datasets into manageable groups.

In order to quantify the quality of the data, you need to understand, then define the properties (attributes or characteristics) of the data you plan to measure. Data quality properties are frequently termed “dimensions”. Many organizations have set out what they regard as the key data quality dimensions, and there are plenty of scholarly and business articles on the subject. Two of the most commonly attributed sources for lists of dimensions are DAMA International, and ISO, in the international standard ISO 25012.

There are a number of published books on the subject of data quality. In her seminal work Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (Morgan Kaufmann, 2008), Danette McGilvary emphasises the importance of understanding what these dimensions are and how to use them in the context of executing data quality projects. A key call out in the book emphasises this concept.

“A data quality dimension is a characteristic, aspect, or feature of data. Data quality dimensions provide a way to classify information and data quality needs. Dimensions are used to define, measure, improve, and manage the quality of data and information.
The data quality dimensions in The Ten Steps methodology are categorized roughly by the
techniques or approach used to assess each dimension. This helps to better scope and plan a project by providing input when estimating the time, money, tools, and human resources needed to do the data quality work.

Differentiating the data quality dimensions in this way helps to:
1) match dimensions to business needs and data quality issues;
2) prioritize which dimensions to assess and in which order:
3) understand what you will (and will not) learn from assessing each data quality dimension, and:
4) better define and manage the sequence of activities in your project plan within time and resource constraints”.

Laura Sebastian-Coleman in her work Measuring Data Quality for Ongoing Improvement, 2013 sums up the use of dimensions as follows:

“if a quality is a distinctive attribute or characteristic possessed by someone or something, then a data quality dimension is a general, measurable category for a distinctive characteristic (quality) possessed by data.

Data quality dimensions function in the way that length, width, and height function to express the size of a physical object. They allow us to understand quality in relation to a scale or different scales whose relation is defined. A set of data quality dimensions can be used to define expectations (the standard against which to measure) for the quality of a desired dataset, as well as to measure the condition of an existing dataset”.

Tim King and Julian Schwarzenbach in their work, Managing Data Quality – A practical guide (2020) include a short section on data characteristics, that also reminds readers that when defining a set of (dimensions) it depends on the perspective of the user; back to Plato and his Theory of Forms from where the phrase “beauty lies in the eye of the beholder” is derived. According to King and Schwarzenbach quoting DAMA UK, 2013, the six most common dimensions to consider are:

  • Accuracy
  • Completeness
  • Consistency
  • Validity
  • Timeliness
  • Uniqueness

The book also offers a timely reminder that international standard ISO 8000-8 is an important standard to reference when looking at how to measure data quality. ISO 8000-8 describes fundamental concepts of information and data quality, and how these concepts apply to quality management processes and quality management systems. The standard specifies prerequisites for measuring information and data quality and identifies three types of data quality: syntactic; semantic; and pragmatic. Measuring syntactic and semantic quality is performed through a verification process, while measuring pragmatic quality is performed through a validation process.

In summary, there is plenty of resource out there that can help you with understanding how to measure the quality of your data, and at KOIOS Master Data, we are experts in this field. Give us a call and find out how we can help you.

Contact us

In summary, there is plenty of resource out there that can help you with understanding how to measure the quality of your data, and at KOIOS Master Data, we are experts in this field. Give us a call and find out how we can help you.

+44 (0)23 9387 7599

info@koiosmasterdata.com

About the author

Peter Eales is a subject matter expert on MRO (maintenance, repair, and operations) material management and industrial data quality. Peter is an experienced consultant, trainer, writer, and speaker on these subjects. Peter is recognised by BSI and ISO as an expert in the subject of industrial data. Peter is a member ISO/TC 184/SC 4/WG 13, the ISO standards development committee that develops standards for industrial data and industrial interfaces, ISO 8000, ISO 29002, and ISO 22745. Peter is the project leader for edition 2 of ISO 29002 due to be published in late 2020. Peter is also a committee member of ISO/TC 184/WG 6 that published the standard for Asset intensive industry Interoperability, ISO 18101.

Peter has previously held positions as the global technical authority for materials management at a global EPC, and as the global subject matter expert for master data at a major oil and gas owner/operator. Peter is currently chief executive of MRO Insyte, and chairman of KOIOS Master Data.

KOIOS Master Data is a world-leading cloud MDM solution enabling ISO 8000 compliant data exchange

International trade and counterfeiting challenges: a new digital solution that will traverse the borders – Part 2

International trade and counterfeiting challenges: a new digital solution that will traverse the borders – Part 2

International trade and counterfeiting challenges: a new digital solution that will traverse the borders – Part 2

Part 2 – Introducing K:blok – the digital solution to international trade and counterfeit challenges

Introduction

In February 2019, we (KOIOS Master Data) embarked on a successful year long research and development project focusing on “Using ISO 8000 Authoritative Identifiers and machine-readable data to address international trade and counterfeiting challenges”. This project was funded by Innovate UK, part of UK Research and Innovation. ISO 8000 is the international standard for data quality.

Part one of this article explains the challenges HMRC and the UK PLC face due to counterfeiting and misclassification when importing into the UK, and outlines a digital solution to solve those challenges. Upon which we won our Innovate UK grant.

This part of the article (part two) outlines the development progress made towards building a digital solution, how machine learning and natural language processing techniques were used during the year-long project and how the project can move forward.

K:blok – technology to traverse borders

To tackle the challenges outlined in part one, we developed a new software product, K:blok.

K:blok is a cloud application that allows importers to create a digital contract between the parties involved in the cross border movement of goods from the manufacturer to the importer/buyer. These parties can include: manufacturers, shippers, freighters, insurers and lawyers, amongst others.

The contract brings together, in a single source, various pieces of data that are required to successfully and efficiently import a product into the UK and data that is not currently captured in any software system:

  • ISO 8000 compliant, machine readable, multilingual product descriptions produced by the manufacturer of the products;
  • ISO 8000 compliant Authoritative Legal Entity Identifiers (ALEI’s) for each organisation that participates in the trade;
  • Accurate commodity codes for each product, the quantity of products, serial numbers and anti-counterfeit information (only visible to the manufacturer, the buyer and HMRC) to help validate the authenticity of the product;
  • Trade specific information required for insurance and accountability, for example: the trade incoterm;
  • Licensing and trading information about the parties in the contract, for example: Economic Operators Registration and Identification (EORI) number;
  • Information regarding the route the product is taking, for example: the port of import into the UK, port of export from the original country of export, vessel/aircraft numbers and locations of the change of custody of the consignments.

The contract is digital, machine readable, can be exchanged without loss of meaning and is suitable for interoperating with distributed ledger technology, like blockchain.

This data can be accessed and used by any of the participants of the contract and analysed by HMRC. All of this data is captured before the goods are moved which, in turn, provides an intelligence layer and pre-arrival data on goods for HMRC analytics, to enable resources to be targeted at consignments deemed high risk.

This single source of data also provides buyers with an audit trail for their purchased products, which begins with the original manufacturer which assists with the authentication of the product received and can form the basis of an efficient global trusted trader scheme.

Natural language processing will help avoid misclassification

As discussed in part one, misclassification leads to the UK losing billions in tax revenue. Misclassification is both intentional and unintentional. Reducing the unintentional misclassification could save the UK millions in tax revenue.

There is a fundamental flaw in the current process of tariff code assignment. The party that currently assigns the tariff code is not usually the manufacturer of the product. Therefore, the party does not have the technical knowledge to classify the product correctly. This party also rarely has a full description of the product and resorts to using a basic description from an invoice to assign the code.

Currently, HMRC provides an online lookup and email service to enable UK businesses to assign the correct tariff code. However, there are concerns that the service is not time efficient. This concern will only get worse as more companies may have to classify their goods once the UK leaves the European Union (EU).

Therefore, as part of our project, we worked with two students from the University of Southampton, studying Computer Science with Machine Learning, to create an additional application programming interface (API) that links with the government tariff code API and uses natural language processing techniques to score a similarity between an input product description and the potential mapping to the correct tariff code.

This is accessible by manufacturers using the KOIOS software to link their ISO 8000 compliant product specifications to the correct commodity code for trading with the UK.

Techniques such as term frequency-inverse document frequency (tf-idf) and K-means were integrated into this API. Support Vector Machine (SVM), Random Forest and a Deep Neural Network (2 layers) have also been explored to improve the accuracy of the algorithm.

The API successfully improves on the searching capabilities of the government online lookup service within the product areas explored in this project – which were bearings and couplings.

KOIOS are uniquely positioned to continue the development of digital solutions for the UK PLC

Our Innovate UK project provides a foundation to achieve more efficient, cost-effective, cross border trading and to reduce counterfeit activities. We believe that data standards, including ISO 8000 can play a huge part in digitising and automating this process further.

We are ideally suited and uniquely positioned to continue the research and development of both the K:blok platform and the machine learning tariff classifier.

We also believe there is an opportunity to digitise the outdated, human readable tariff classification into a digital classification, using the international standards ISO 22745 and ISO 29002. These data standards sit at the core of all of the products in the KOIOS Software Suite. A digital version of the tariff classification will improve the accuracy, speed and reliability of computer automation.

Join us in our vision

Our successful Innovate UK project was a step in the right direction to improving international trade and reducing counterfeiting. Brexit also provides a great opportunity for the UK to become a world leader in using technology across borders and to set the standard for countries to follow.

In the coming months, we will continue to engage with the UK Government/HMRC and continue to look for opportunities to fund our research and development.

If you think that you can add value to this project and would like to explore how we could collaborate then please get in touch at info@koiosmasterdata.com

Contact us

If you think that you can add value to this project and would like to explore how we could collaborate then please get in touch.  

+44 (0)23 9387 7599

info@koiosmasterdata.com

International trade and counterfeiting challenges: a new digital solution that will traverse the borders – Part 2

International trade and counterfeiting challenges: a new digital solution that will traverse the borders – Part 1

International trade and counterfeiting challenges: a new digital solution that will traverse the borders – Part 1

Part 1 – The cost of counterfeit goods and misclassification to the UK

Introduction

In February 2019, we (KOIOS Master Data) embarked on a successful year-long research and development project focusing on “Using ISO 8000 Authoritative Identifiers and machine-readable data to address international trade and counterfeiting challenges”. This project was funded by Innovate UK, part of UK Research and Innovation. ISO 8000 is the international standard for data quality.

This part of the article (part one) explains the problem counterfeit goods and misclassification of products has on the UK PLC and the proposed solution which won us the Innovate UK Government grant.

A GBP 11 billion impact: and poor data exchange is the root of the problem

Counterfeit products and misclassification of products, when importing into the UK, cause major challenges for commercial organisations and the economy in the UK. These challenges increase a business’s exposure to risk, including consumer health, safety and well-being.

The impact of global counterfeiting on the UK economy is increasing. The Organisation for Economic Co-operation and Development (OECD) states that forgone sales for UK companies due to infringement of their intellectual property (IP) rights in global trade amounted to GBP 11 billion and at least 86,300 jobs were lost due to counterfeiting and piracy in 2019.

Protection from counterfeiting could save some organisations £000’s: for example, Greek customs seized 17,000 bearings, purporting to be from SKF, worth €1m in a single anti-counterfeiting operation.

When importing into the UK, importers are required to declare a commodity code for the products being imported. The commodity code is used to collect duty and VAT and dictates the restrictions and regulations, including the requirement for licensing, when importing or exporting the product.

Often the importer of the product does not have the technical knowledge to classify the product correctly. This, in combination with the complexity of the tariff code system currently adopted by the European Union (EU), and subsequently by the UK, causes many cases of misclassification. These cases are both intentional and unintentional.

Misclassification causes incorrect duty and VAT ratings to be applied to companies importing products, and also distorts trade statistics. Fraudulent misclassification leads to the UK losing billions in tax revenue.

Importers currently make customs declarations using the Customs Handling of Import and Export Freight (CHIEF) system, with some importers transitioning to the newer Customs Declaration Service (CDS).

The current importing process is not stringent enough and information is declared too late in the process. This results in a lack of transparency of the origin of products and a lack of quality data supporting the import and trade.

Therefore, Customs have the near impossible task of identifying and intercepting counterfeit or misclassified products. Customs activities increase spending by the UK Government on customs checks and delay trading activities.

International trade and counterfeit challenges: there is a digital solution

We believe that the challenges facing HMRC and the organisations that suffer from counterfeit goods can be solved with a stringent digital solution. A digital solution that captures:

  • A quality description of the products in a consignment;
  • The regulatory/licensing requirements on the products and the importer – for example: the commodity code of the product and the Economic Operators Registration and Identification (EORI) number; and
  • The parties involved in the trade – for example: manufacturers, shippers, freighters, insurers and lawyers, amongst others,

in a timely manner (pre-arrival to the UK border). This assured, single source of data can then be used by all parties in the supply chain, including HMRC and border forces. HMRC will then be able to use this trusted data to better target resources on more risky consignments and the platform can be a requirement for inclusion in a trusted trader programme.

This digital solution can be taken further so that the importer using the platform can establish a purchase order with the seller.

We also believe that we can help to reduce the misclassification of products by:

  1. Putting the responsibility of classifying the product on the manufacturer of the product, rather than the importer; and
  2. Assisting the manufacturers with classifying the product by using ISO 8000 compliant, machine readable product specifications and machine learning techniques to search the current human readable tariff classification.

Without a digital data solution for the automating of tariff code assignment and the provenance of products, no significant improvements to the current state of play can be achieved.

The proposed solution would enable HMRC to: 

  • reduce administration; 
  • eliminate errors; 
  • restrict growing levels of fraud in the digital economy 
  • target resources effectively through collecting pre-arrival data on goods 

This proposal formed the fundamental basis of our successful Innovate UK grant application.

The next part of this article outlines the development progress made towards building a digital solution, how machine learning and natural language processing techniques were used during the year-long project and how the project can move forward.

Contact us

If you think that you can add value to this project and would like to explore how we could collaborate then please get in touch.  

+44 (0)23 9387 7599

info@koiosmasterdata.com

What is K:spir and how can it revolutionize the SPIR process?

What is K:spir and how can it revolutionize the SPIR process?

What is K:spir and how can it revolutionize the SPIR process?

The SPIR process urgently needs to enter the 21st century

At KOIOS Master Data we have a unique understanding of the difficulties caused by the current SPIR (Spare Parts Interchangeability Record) process. Through our team’s years of MRO consultancy work, we have first-hand experience of how damaging the poor-quality data supplied in SPIRs can be to oil and gas projects. It can have a profound effect on cost, time and resource – cost, time and resource that could be spent innovating and developing a competitive advantage. Not to mention, the unnecessary wastage it can lead to, in an industry that can hardly accommodate it in the current climate. In this age of Industry 4.0, digital transformation and international data standards such as ISO 8000, the question begs – why is data quality consistently letting the side down? When we struggled to find an effective SPIR solution, KOIOS Master Data was born and we set out to create one.

K:spir is the only SPIR software designed this century using ISO 8000 standard data. It creates machine-readable data that retains quality throughout the chain, enabling accurate decision making and resulting in reduced cost, time and resource.

Here, we look at the importance of master data management, the challenges created by the SPIR process, and how K:spir is uniquely positioned to resolve those challenges.

Why is data management so important to the SPIR process?

In this age of ‘data explosion’, most businesses are aware of how poorly-managed data can put them on the back foot. In Experian’s 2019 Global Management Data Research, they found that 95% of organizations surveyed see a negative impact from poor data quality.

Similarly, the Aberdeen Group’s Big Data Survey in 2017 found that the biggest challenges for Executives arise from data disparity, including inaccessible data, poor quality data informing decisions and the growing need for faster analysis. 

The overall effect is a lack of trust in data, to the great detriment of strategic decision making. And when you can’t trust your data to inform business decisions, then cost, time and resource will inevitably suffer.

In the context of the SPIR process, accurate decision making is everything. The SPIR exists as a tool for forecasting spares requirements for the life of a project, its sole purpose being to assist the Owner Operator (O/O) to make accurate decisions. Yet, as many will attest, the data supplied is often inaccurate, hard to access and sometimes supplied by the Engineering Procurement Contractor (EPC) at handover, by which time it is often too late to inform anything at all. 

Experts have raised the question – if you can’t trust SPIRs to make accurate procurement decisions, then are they worth the paper they’re written on?. The process is clearly out-of-date, yet it continues to blight the efficiency of many oil and gas upstream projects.

SPIRs dissected 

The shortcomings of the antiquated SPIR process can be summarised into three key areas:

1. DATA IS INACCURATE AND OVER-SIMPLIFIED

SPIRs are generated from paper forms and are transcribed many times, so part descriptions become distorted. Often, parts have multiple descriptions.

Solution: K:spir locks in data quality right at the start of the process, using ISO 8000 standard data. Part descriptions are consistent and safe from misinterpretation, providing confidence in forecasting and reordering. 

SPIRs are usually completed by an Original Equipment Contractor (OEM), who is not necessarily aware of the O/O’s operating and maintenance procedures. Therefore, they do not take into account equipment criticality or maintenance capability.

Solution: K:spir uses the maintenance and repair strategy to determine the spares requirement, reducing wastage and taking cost off of the bottom line.

2. DATA IS INACCESSIBLE AND DIFFICULT TO ANALYZE

SPIRs often provide information in spreadsheets or pdfs, which are impossible to extract data from quickly, if at all. To extract anything meaningful is very cost and time-intensive, and relies on support from IT specialists.

Solution: K:spir provides instant reporting on the completeness and cost of spares, allowing for accurate decision making. The information is fully configurable to the requirements of the O/O. It can also create a Maintenance Bill of Materials (BoM) and is interoperable with maintenance systems.

Information is not portable and has to be re-entered for different systems.

Solution: K:spir generates portable (machine-readable) data saving significant time spent re-keying information and unnecessary data handling costs.

Data exists on many platforms and is not available to all stakeholders, all of the time.

Solution: K:spir is cloud-based, providing simultaneous access to all stakeholders in the chain. This allows for more transparency and accountability at all stages of the project lifecycle.

3. DATA IS SUPPLIED TOO LATE

Sometimes even as late as handover, by which time it’s too late for the O/O to minimize the operating risk. There is no opportunity to make informed decisions, such as ordering spares with long lead times, or calculating warehouse space. This can lead to unnecessary wastage and operational difficulties along the line.

Solution: K:spir provides transparency right from the beginning of the project, allowing for critical decisions to be made early on. 

With its unique set of features and benefits, it’s clear that K:spir can relieve the symptoms of the current SPIR process with immediate effect, saving valuable cost, time and resource.

A SPIR – this is not what efficiency looks like!

SPIRs and effective MDM – who is responsible for getting it right?

As confident as we are in the KOIOS software suite to advance the world of Master Data Management (MDM), there are clearly other factors that need to be addressed, most notably, ownership. It is a thorny area, and one that is being more keenly contested as digital transformation rattles on apace. As the Aberdeen Group puts it, there is a “growing urgency for better data management”, as businesses see the shortfalls of their inability to harness data. 

Experian’s report shows that in 84% of cases, data is still managed primarily by IT departments. Revealingly, 75% of their sample thought that ownership should lie within the business, with support from IT. They conclude that organizations should develop their MDM strategy to fulfill the needs of a much larger group of stakeholders, who wish to harness the power of their data to improve decision making and efficiency.

In the context of SPIRs and oil and gas projects, we believe that O/Os should become more demanding over the quality of data supplied to them by manufacturers. It is unrealistic for their IT experts to have sight of the broader operational requirements, with their own priorities being diverse and demanding. It is the Executives who suffer the consequences of the risk taken by ignoring poor data, and the operations and maintenance departments that will experience the pain. Clearly, they need to make their voices heard much earlier in the process. That said, manufacturers and EPCs also need a better understanding of the challenges faced by O/Os, and in our view should share the responsibility for getting the data right from the start.

It is, as previously stated, a tough subject, but we are constantly encouraged by the conversations we have with manufacturers and O/Os alike. More and more key stakeholders are waking up to the power that effective MDM can have in driving business forwards, by freeing up cost, time and resource and supporting strategic decision making. Not just to their own ends, but for industry as a whole to fully realize its digital transformation goals.

Join us in our vision to revolutionize the SPIR process

A radical change to the SPIR process and MDM as a whole is on the horizon. While there may be no silver bullet, we firmly believe that the right software is an essential move forward. The KOIOS software suite is geared towards this larger shift in MDM, but in the case of K:spir, the results can be felt immediately.

Our hope is that O/O’s and manufacturers alike will unite in becoming more discerning and demanding about data quality, working as one to create harmony along the chain. At KOIOS Master Data, we are committed to leading the conversation and driving better data quality.

Contact us

If you wish to become part of the change and join us in our vision to revolutionize the SPIR process, we would love to discuss it further with you. 

+44 (0)23 9387 7599

info@koiosmasterdata.com

SPIRS: Are they worth the paper they’re written on?

SPIRS: Are they worth the paper they’re written on?

SPIRS: Are they worth the paper they’re written on?

I have carried out numerous studies of MRO inventory for companies around the globe, and I question the existence of SPIRs in the new era of digital data.

Peter Eales

Chairman - KOIOS Master Data

Context: Oil and gas upstream projects typically extract material requirements from Spare Parts Interchange Lists (SPILs), sometimes referred to as Spares Parts Lists and Interchangeability Records (SPIRs) or Recommended Spare Parts List (RSPL). These lists are supplied to the owner / operator (O/O) at handover by the Engineering Procurement Contractor (EPC) having been supplied to the EPC by the manufacturer or the vendor of the equipment.

What challenges do SPIRs create?

There are a number of issues for plant operators that arise from the use of SPIR documents in oil and gas projects. The release of these documents by the EPC is often left until the very end of the project, or not at all, despite financial penalty clauses being inserted in the contracts. This is a real challenge to the operator who wants to reduce the operating risk by purchasing long lead items early enough, and those who want to calculate the size of warehouse they require in a greenfield project.

The format of the SPIR is frequently inconsistent; effectively being a paper form that has been recreated onto a spreadsheet and edited many times. In the end it resembles nothing much more than an optimistic vendor order form. Certainly, it is an incredibly difficult document to extract data from, and as no two forms are constructed in the same way and often have merged cells. Extracting a complete project worth of data is a costly exercise in terms of both manpower and time.

Case study: ever decreasing O-Rings…

If we look at these documents, it soon becomes clear that they have many drawbacks: they are not extractable; contain only a brief description of the product, often just a noun; they take no account of the equipment criticality; they take no account of the O/Os maintenance capability, or their spares and repair strategy, such as repair or replace. Data quality is extremely poor in these types of documents. In this example, from a single 62-line SPIR document, O-Ring is described four different ways. The shore hardness is also missing from the details, making it impossible to safely order the part from another supplier. For consumable items, it is common for the original part manufacturers name to be omitted from the document.

SPIR Document Example 
“O” RING
OD: 18.5, ID: 15.5, t: 1.5
FKM
O RING
OD: 16.6, ID: 11.8, t: 2.4
NBR
ORING
OD: 66.5, ID: 62.5, t: 2
NBR
O-RING
OD: 6.5, ID: 3.5, t: 1.5
NBR

I would also challenge the spares actually listed on the form. Interpretation of what constitutes two years operating spares vary from manufacturer to manufacturer. Some list only basic consumables, as in their opinion, that is all that is required in the first two years, some list a full production BoM (Bill of Materials) that includes such items as pump casing. Neither approach is helpful to the analyst trying to decide what spares to stock in the plant or organize an on-demand local supply for.

The companies that design and manufacture equipment rarely operate them, and EPCs do not always have experience of operating and maintaining plants – so why would they know what spares you need? Asking the vendor how he calculated the failure rates in your application gets an interesting range of answers, although when you ask him if he will take back all his recommended spares that you have not used in five years’ time, you usually get the same answer! To be fair some major manufacturers do track component failure rates in the field, but they are few and far between.

I would strongly challenge the decision to list the “two years recommended spares” on the SPIR. How many plants are designed for a two-year life? As a materials manager working with the maintenance team, I simply want to know all the maintainable items required for the life of the equipment. My task is to determine the spares required to keep the revenue producing assets running for the life of the plant.

When is a spare no longer a spare? When it becomes waste

Commissioning spares is another column frequently found on SPIR documents. Before you buy the commissioning spares check with the EPC, they will probably be responsible for these spares during the commissioning period and will be leaving you a mountain of unused spares, and will usually be asking a hefty price for them on handover. As an owner operator, you will be in danger of overloading your warehouse with spares you might never use or have already purchased.

I do not want to spend subsequent years repeating the exercise to find out what spares I do not have, or which spares I will never use, you know, those spares that were purchased “just in case”.

When reviewing SPIR documents in order to determine the spares required for the operation, the criticality of the equipment, the maintenance capability, and an understanding of the planned consumption also need to take into account. Furthermore, a number of organisations have strategies to run certain non-critical equipment to failure and then replace the complete unit rather than repair the item using the recommended spares. This information is, understandably, not on the SPIR form but is vital in the decision-making process.

It never ceases to amaze me seeing a room full of people analysing SPIR forms and ordering the spares listed – using the column added by the vendor – without taking these factors into account.

I have carried out many studies of MRO inventory for companies around the globe, and the two most frequent causes of non-moving stock is spares purchased for equipment that is no longer exists, and more commonly, spares purchased for equipment where the plant maintenance capability does not exist to repair and item; motor and pump spares are the favourites. It should go without saying that there is no point in keeping a bearing for a motor in a zoned area for your repair shop to use if the repair shop is not approved to complete work to that standard, or does not have a facility to test the repaired equipment. Pump and motor spares are most frequently purchased and remain unused, as most often the maintenance strategy is to send these units out for refurbishment when they fail. So why were they purchased in the first place? Probably because they were listed on the SPIR and the buyer has taken the appealing route of taking the word of the manufacturer regarding the required spares or they have purchased the spares as part of a package.

So, what is the solution? 

In the age of international digital data exchange standards such as ISO 8000, it is frankly mystifying that people are still using these outdated methods of creating and distributing the vast amounts of data required for large projects. 

I firmly believe the answer lies in a simple data exchange service.

There is a paradigm shift using new technology and international standards such as ISO 8000. The antiquated process of buyer-led templates is replaced with supplier-led specification delivered in a computer interpretable format. The success of this new method lies in the provenance of data, eliminating any ambiguity and making data easy to extract.

It can be achieved by a simple clause in the contract with the EPC:

“The supplier shall supply technical data for the products or services they supply. Each item shall contain an ISO 8000-115 compliant identifier that is resolvable to an ISO 8000-110 compliant record with free decoding of unambiguous, internationally recognized identifiers.”

This result? Guaranteed data quality leading to a reduction in costs and increased efficiency.

To find out more about the tools you need to unlock the power of ISO standard digital data, visit KOIOS Master Data.

About the author

Peter Eales is a subject matter expert on MRO (maintenance, repair, and operations) material management and industrial data quality. Peter is an experienced consultant, trainer, writer, and speaker on these subjects. Peter is recognised by BSI and ISO as an expert in the subject of industrial data. Peter is a member ISO/TC 184/SC 4/WG 13, the ISO standards development committee that develops standards for industrial data and industrial interfaces, ISO 8000, ISO 29002, and ISO 22745. Peter is the project leader for edition 2 of ISO 29002 due to be published in late 2020. Peter is also a committee member of ISO/TC 184/WG 6 that published the standard for Asset intensive industry Interoperability, ISO 18101.

Peter has previously held positions as the global technical authority for materials management at a global EPC, and as the global subject matter expert for master data at a major oil and gas owner/operator. Peter is currently chief executive of MRO Insyte, and chairman of KOIOS Master Data.

KOIOS Master Data is a world-leading cloud MDM solution enabling ISO 8000 compliant data exchange

MRO Insyte is an MRO consultancy advising organizations in all aspects of materials management

Standard based disruptive MDM technology

Standard based disruptive MDM technology

Standard based disruptive MDM technology

Question: what do you call a consensus of best practice?
Answer: an international standard

Here I am writing a blog about disruptive and innovative master data management (MDM) solutions, so why I have started my blog defining such a constraining document as a standard?  Ask your MDM software supplier a simple question, which data quality standard is your software based on? Is it the standard that allows you to exchange multiple language specifications, portable data in other parlance?  Is it the standard that allows for interoperability through the exchange of digital data? Is it the standard that enables the semantic web by creating open, computer interpretable data?

Why are these particular qualities important?  The lack of portable digital data is held up as key constraining factors in four key areas:

  1. the move towards interoperability of public authorities according to a recent EU report [1];
  2. the move towards interoperability of smart cities according to the publically available standard produced by BSI [2];
  3. the move towards interoperability in the oil and gas sector according to the (soon to be published) ISO standard on that subject [3];
  4. the move towards industrial interoperability outlined in the Industrie 4.0 (I40) [4] initiative promoted by the German government.

It might seem a paradox that the disruptive solution to these issues is based on the international data quality standard, ISO 8000 [5], such documents are not normally seen as disruptive, the disruption comes because so few MDM solutions conform to the standard.  In addition to the solutions to the issues outlined above, ISO 8000 insists that messages are exchanged in a resolvable format that allows the receiver to be assured it is trustworthy, and also addresses data quality “from the bottom up” concentrating on accurate property values and units of measure.   As all experienced data quality managers know, the overuse of character, string or text fields in systems is the main cause of subsequent data quality problems.

The KOIOS Master Data cloud software conforms to ISO 8000.  The software positively discourages the use of string fields, encouraging the user to create lists of values based on authoritative sources from the global concept dictionary that KOIOS has compiled.  Where that approach is not suitable the software encourages the use of “representation” to constrain the value to its correct syntax, ensuring data quality is locked in at the lowest level. As all properties used to create specifications must have definitions, not user guides, the chances of loss of meaning when data is exchanged is dramatically reduced, this again is one of the key pillars of ISO 8000.

For manufacturers, KOIOS enables you to post a single version of the digital representation of your product specifications in the cloud, and allows you to control how your customers view your brand and product details, you can even control which fields you share with each customer.

For end-users, cataloguing at source (C@S) now comes alive.  As end-users you are able to import product specifications created by the very people that manufactured the item, without third parties manipulating the data.  These descriptions can be imported into your “PO text” field in full, ensuring your PO text is always understood by your supply chain, and enables you to create consistent “short descriptions” for those items ensuring reduced search times for users of your system.

Data cleaning is thus turned on its head by the use of data that is trustworthy.  Now that is disruptive!

Bibliography

[1] New European Interoperability Framework Promoting seamless services and data flows for European public administrations NO-07-16-042-EN-N. 2017
[2] PAS-181 – Smart city framework guide to establishing strategies
[3] ISO/TS 18101 Oil and Gas Interoperability
[4] Digital Transformation Monitor Germany: Industrie 4.0 January 2017
[5] ISO 8000 (all parts) Data quality – Framework and the exchange of characteristic data