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Summary of the articles related to topic 3 of the course Collab. in SCM and Adv. Topic s
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Summary of the articles related to topic 3 of the course Collab. in SCM and Adv. Topics
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The adoption of prognostic technologies in maintenance decision making: a multiple case study Prognostic techniques can be used to reduce business and safety risks caused by unexpected failures
of critical systems and reduce life cycle costs.
Many companies applying these techniques experience a gap between potential and realized
benefits and therefore rate their current success as only 'satisfactory'
Prognostic techniques enable asset owners to predict the future state of systems including health
assessment, detecting incipient failure and predicting remaining useful life (RUL).
As opposed to prognostics, diagnostics is retrospective by nature
Detection is closely related to diagnostics and aims to detect anomalies in the system. It is binary by
nature, indicating either a healthy or a faulty system.
Many prognostics and health management methods are introduced and applied to solve specific
problems without much explanation or documentation given as how or why these methods have
been selected
It is important to guide the asset owner through the process of making the optimal maintenance
decision based on the right collection of data and assist in selecting the type of prognostic
technology applicable to his situation
This paper introduces a framework which combines and links elements discussed in current
literature and guides users of prognostic technologies through the steps from data collection to
maintenance decision making for life cycle management decision support.
o Multiple routes can be followed through the proposed framework.
o Boundaries are created by internal and external laws and regulations
STEP 1: Monitoring and data gathering
Two main categories of asset data can be distinguished:
(i) event data (collected via historical records and ERP systems), and (ii) condition monitoring data
(collected via monitoring sensors and systems)
o POSTULATE 1: The collected data is often not useful for advanced maintenance analyses
The more mature companies tend to have more structured and accessible data. Especially the less
mature companies seem to experience difficulties with fractured data, e.g. stored in multiple
systems, local desktops and data which is difficult to access, e.g. data in legacy systems, data stored
in text format (i.e. word or pdf), or incomplete data
For the higer mature companies, the postulate is rejected, for companies with a lower maturity the
postulate is accepted.
o POSTULATE 2: The selection of parameters to monitor is not well motivated (essential quantities are
missing and non-relevant parameters have been monitored. This is often discovered when the data
is interpreted after a certain period of data collection)
When monitoring systems are purchased or installed, it is not exactly known what parameters
should be measured
This postulate can be accepted
STEP 2: Advanced maintenance analyses
Little consensus as to what classifications of prognostics are most appropriate.
(i) Using a framework of five maturity levels of prognostics.
(ii) Classifies the maturity levels on the type of input data, knowledge or a physical model
o POSTULATE 3: Higher maturity levels of maintenance analyses result in higher value analyses (more
mature analyses are often more difficult and require more effort to develop, this implicitly suggest
that these analyses result in outcomes with a higher added value for the decision maker)
This postulate can be rejected, it is not true that higher mature analyses always result in higher value
for the company
o POSTULATE 4: The predictive performance of the prognostic systems improves in time, they are
evolving systems (Prognostic systems can be validated and improved during their lifetime because
more and more data, for example failure or costing data, is collected during its utilization)
, Postulate can be accepted
o POSTULATE 5: The selection of the type of advanced maintenance analysis is not well motivated.
(Most Prognistics and Health management approached are application or equipment specific.
Moreover, many PHM methods are introduced and applied without much explanation or
documentation given as how or why these methods have been selected
Postulate can be accepted. Companies do not know beforehand what they should measure and
therefore ad-hoc choose a suitable method. In hindsight the motivation seems marginal since a trial
and error process is followed.
STEP 3: Technical results and life cycle management decision making support
When dealing with complex systems with interrelating failure models, human made decisions are
often not sufficiently reliable or accurate. Decision support systems (DSS) can be used in step 3b to
aid this decision making process; model based decision making is superior to human based decision
making
o POSTULATE 6: The quality level of current analyses is not sufficient to improve maintenance
decisions (To be able to use the analyses effectively for decision making, the remaining useful
lifetime forecast, the prognostic distance, should be equal to or larger than the lead time for the
decision-maker to take preventive actions prior to a failure. This is called the flexibility phenomenon.
Too little prognostic distance prohibits users to schedule preventive maintenance and wait for
spares.)
We could only find limited supprt for this postulate, it seems that the horizon of the maintenance
decision is of influence for the acceptance of the postulate
Companies experience multiple difficulties in selecting and applying the right prognostic technique
for their situation, this is especially true for the implementation of more mature maintenance
analyses. It seems that companies often start with experience based methods which are enriched
with data from historical failure records. (post 1)
Companies aim to develop more mature analyses with a trial and error approach which is started
with collecting data of many different parameters. For the companies it seems very difficult to
determine beforehand the relevant methods and parameters (post 2,5) and they therefore
experience a long and costly implementation process
Reversing a relationship spiral: From vicious to virtuous cycles in IT outsourcing
Outsoucing of IT processes is a common business practice in contemporary businesses, but many of
these outsourced IT relationships are not functioning well.
IT service providers and their clients appear to be caught in a vicious cycle of low trust, mediocre
performance, and high costs on both sides.
Such a vicious cysle in the context of buyer-supplier relationships is also called a relationship spiral.
Sometimes, this relationship spiral is initiated by a price-driven initial contractual arrangement, after
which the IT provides finds itself very little financial room to manouvre. As a result, its service level is
bound to deteriorate, which leads to even more cost-driven and detailed supplier management from
the customer. This cocept is known in the It outsoucring (ITO) literature as the 'Winner's Curse'
Prefer to escape out of this downward relationship spiral but cannot see any option other than
changing the contract.
Often, even terminating the ITO contract appears to be the only effective one-time solution to end
the client-supplier conflict and, hence, the deteriorating relationshop.
There may exist possible mechanisms to reverse such a vicious relationship spiral
A static view of ITO relationships limits our understanding of how these relationships evolve into a
spiral, what are the potential mechanisms to reverse this downward spiral and what are the
boundary conditions of the reversal mechanisms
RQ: How can companies engaged in ITO find a way out of the gridlock situation of a vicious
relationship spiral setting?
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