Data within AM

Lin et al. (2006) report that organisations are currently generating and cap- turing more data than ever before in corporate history. Neely, Lin, Gao and Koronios (2006) found that in AM environments, and particularly in PAM environments, an enormous amount of data is produced during an asset’s life cycle. The terms data and information are often used interchangeably in ev- eryday terminology and thus some clarification is needed. Brous, Overtoom, Herder, Versluis and Janssen (2014, p. 126) explain the difference:

“Data are facts about objects, subjects or events within or without the organization. These facts generally involve the condition of the object or subject or refer to a transaction involving that object or subject. Data only becomes information once it is given context and presented in a form that people are able to understand.”

According to Neely et al. (2006), the data produced by engineering assets are one of two types, namely: configuration data and transaction data. Con- figuration data pertains to the physical attributes of the assets such as the date of acquisition, the initial cost, the value at year end and the physical location of the particular asset. Transaction data, on the other hand, is generated and collected while the asset is being operated. Transaction data can either be recorded manually by technicians during routine maintenance checks or it can be produced by sensors embedded in the assets to track when maintenance is necessary and when it is completed. Transaction data can also refer to the output of an asset such as the amount processed or the variability in output. Both configuration and transaction data can be used to support management

decisions in a variety of industries. Gao, Lin and Koronios (2006) mention that a few common sources of data in PAM are:

• Inventory data: Information pertaining to the articles, goods and prop- erty owned by an organisation.

• Asset condition data: The degree to which equipment has deteriorated. • Asset and organisational performance data: Historical information de- scribing the availability and reliability levels of assets and the organisa- tion or information comparing the outputs of different assets and systems in an organisation.

• Criticality data: Data about systems or subsystems which are of the greatest importance and which should be the highest priority of the or- ganisation.

• Life cycle data: Information describing a series of changes in the life of an object (often an asset).

• Valuation data: An estimate of the value of an object an organisation owns or plans to acquire.

• Financial data: Information outlining the financial health of a business such as profits, revenues and operating income.

• Risk data: An indication of the uncertainty associated with decisions and assets.

• Reliability data: Probability that a system or an element in a system will perform its intended purpose for a set period of time.

• Technical data: Engineering or scientific information relating to the plan- ning, development, production, creation, operation and maintenance of systems or subsystems in an organisation.

• GPS data: Information showing current and historical physical positions or locations of objects.

McAfee and Brynjolfsson (2012), however, state that even though the po- tential benefits of having all these data are appealing, there are very real tech- nical and managerial challenges to using it. McAfee and Brynjolfsson (2012) explain that data does not always have the expected impact since the people making the decisions may be more interested in using their intuition than the facts provided by data. In cases where data are scarce and difficult to obtain or use, it is reasonable to allow well-placed and experienced people to make decisions. However, McAfee and Brynjolfsson (2012) express their concern

about the number of organisations that rely on the opinion of the most experi- enced and highest placed person when making important decisions, regardless of whether there is factual data available. They conclude by saying that, while some senior executives base the majority of their decisions on factual data rather than their own intuition, most executives in the business world allow their experience and intuition to override the factual data when making deci- sions.

It is thus the responsibility of TMTs in AM environments to ensure that the data which is available to organisations is used to support decision mak- ing across all departments. Zaccaro, Rittman and Marks (2002) found that the biggest contributor to the success of organisational teams is the effective- ness of the leadership team. Recall that in Section 2.1.3, it was mentioned that collaboration is imperative to the successful implementation of AM at an organisation. This means that the data which is available to support de- cision making should be converted to information and shared among different departments as well as management levels in the organisation. ISO 55000 em- phasises that communication in AM should be two-way, with leaders being able to initiate communication with subordinates as well as being open to re- ceive information from other levels of management regarding the improvement of AM systems (BSI, 2014a). The role of communication in AM environments is discussed in the next section.