- Lauren Johnston is working in the Engineering department of Vascutek, a medical device company, on a project investigating product yield loss. She recorded 6 months of manufacturing data, conducted testing, and led a project team. - She analyzed the manufacturing data using pivot tables and charts, identifying the dates, product sizes, materials, and geometries with the highest failure rates. Comparing interviews with operators performing quality tests allowed her to determine if technique differences affected results. - Trends in the data will impact future activities - samples will be sent for external testing, an experimental protocol will be conducted, and tank cleaning will be enforced and monitored for improvements in yield.

1. Lauren
Johnston

2.  I am working in the Engineering department of Vascutek, a
medical device company which specialises in grafts, heart valves
and conduits.
 One of my projects involves investigating product yield loss. I
have recorded and analysed 6 months of manufacturing data,
conducted testing and led a project team.
 My overall objective is to review manufacturing processes with
the aim of improving productivity and yield.
OVERVIEW OF MY PLACEMENT

3.  “Data is a mass of disordered, raw material from which
information is abstracted to provide evidence to support
argument and conclusions”
 fitness for purpose
 Validity, Reliability & Accuracy
1. CRITERIA USED WHEN SOURCING QUALITATIVE AND
QUANTITATIVE DATA

4. Validity
“the extent to which a measure, indicator or method of data collection
possesses the quality of being sound or true as far as can be judged”
 its relevance to the research question and the directness and strength
of its association with the concepts under scrutiny.
 It may be the case that the best available information has the weakest
validity.
 It was important that I confirmed the data was valid.
1. CRITERIA USED WHEN SOURCING QUALITATIVE AND
QUANTITATIVE DATA

5. Reliability
“Consistency is the main measure of reliability”
 Extent to which we can rely on the source of the data and, therefore,
the data itself. Reliable data is dependable, trustworthy, unfailing,
sure, authentic, genuine, reputable.
 I confirmed the data was reliable through various checks.
1. CRITERIA USED WHEN SOURCING QUALITATIVE AND
QUANTITATIVE DATA

6. Accuracy
“Accuracy is the closeness of results of observations to the true values or
values accepted as being true”
 Accurate information must be the right value and must be represented in a
consistent and unambiguous form.
 I made sure that all the data was accurate by cross-referencing with
various documents.
1. CRITERIA USED WHEN SOURCING QUALITATIVE AND
QUANTITATIVE DATA

7. Quantitative data sources
 Things are either measured or counted, or questions are asked
according to a defined questionnaire so that the answers can be
coded and analysed numerically.
 Results are typically presented using statistics, tables and
graphs.
 Examples include surveys, questionnaires, archival records,
databases, budget statements, price lists, timetables and sales
figures.
 The manufacturing data that I have sourced for my investigation
into product yield loss is quantitative data as it contains dates
and numerical results.
1. CRITERIA USED WHEN SOURCING QUALITATIVE AND
QUANTITATIVE DATA

8. Qualitative data sources
 Qualitative methods help build an in-depth picture among a
relatively small sample of people on a specific issue.
 Results are typically presented in the form of case studies and
summaries rather than lists of numeric data.
 Examples include SWOT analysis, interviews, focus groups and
market research reports.
 During my investigation into porosity yield loss, I spoke to the
Lead operators who perform the porosity test. This is a
qualitative data source as it was an informal interview.
1. CRITERIA USED WHEN SOURCING QUALITATIVE AND
QUANTITATIVE DATA

9. Data specifically related to the work activity data
 When collecting data specifically related to the work activity,
relevance is the most important criteria. Relevance is: “Data
which is applicable to the situation or problem at hand that can
help solve a problem or contribute to a solution.”
1. CRITERIA USED WHEN SOURCING QUALITATIVE AND
QUANTITATIVE DATA

10.  There are legal implications for storing data which are governed
by the Data Protection Act 1988.
 This act is important as it safeguards the use of personal
information protecting against the misuse or abuse of
information. It states the basic rights, special exemptions and
legal interpretations.
2. ACCESSING DATA IN LINE WITH LEGAL &
ORGANISATIONAL REQUIREMENTS

11. Examples
 Everyone in Vascutek has the duty to ensure that the customers’
personal data remains secure even if the partnership is
dissolved.
 As a manager, I have the duty to treat my assessment of an
employee’s performance during their initial probationary period
as personal data. I must therefore have legitimate grounds for
using this data, provide the individual with appropriate notice
when collecting data and be transparent about the use of such
data.
2. ACCESSING DATA IN LINE WITH LEGAL &
ORGANISATIONAL REQUIREMENTS

12. If the data is in numerical form(quantitative), then we typically start
by working out some descriptive statistics to summarise the pattern
of findings.
 The mean, median and mode tell us how the data cluster together
around a central point.
 The range and standard deviation indicate whether the scores in a
given condition are similar to each other or whether they are
spread out.
 Graphs and charts allow the data to be clearly presented. Any
trends/patterns in the data can then be rapidly identified.
I analysed the manufacturing data I collected using pivot tables and
pivot charts. This allowed me to identify the dates, product size,
material and geometry with the highest failure rate.
3. IDENTIFYING TRENDS & PATTERNS FROM DATA

13. If data is not in numerical form then qualitative analyses, based on
the experiences of the individual participants, can still be carried
out.
 Summarising what people have said will reduce the amount of
information and allow trends/patterns to become more obvious.
 Comparing passages of texts and interviews will ensure each part
is given a fair, balanced and equally thorough analysis
I compared my interviews from the Lead operators who perform
the porosity test. This allowed me to determine whether there
were any differences in the technique used by the different
operators when performing the test.
3. IDENTIFYING TRENDS & PATTERNS FROM DATA

14. Predictive analysis
Trends and patterns identified from data can also be exploited to
predict future outcomes. The core of predictive analytics relies on
capturing relationships between identifiable variables and
predicted variables from past occurrences
.
For example, using strategic cost management models, financial
trends can be identified. The cost drivers linking activities to the
companies products, services and customers are identified and
used to predict future trends in product sales.
3. IDENTIFYING TRENDS & PATTERNS FROM DATA

15. Data analysis has become so advanced that trends discovered will
strongly impact the activities of the team involved.
 Most importantly, the data will be used for decision making. Trends
drawn from the data may be used as evidence to back up a decision or
may prove a decision to be incorrect.
 The team may be required to continually record and analyse the data
if the trends are inconsistent or unexpected. This task may be
allocated to an individual or spread across the team.
 Data trends may lead to actions or testing being required. The data
may show that current operations are not producing the expected
results and so testing may be carried out to investigate this. If the
solution is simple then testing would not be required and actions
would be enforced as soon as possible.
4. HOW TRENDS WILL IMPACT THE TEAM’S FUTURE
ACTIVITIES

16. Once I had analysed the data from the porosity yield loss
investigation, I had identified trends that would then impacted my
future activities.
 The data showed a trend in the protein content of the tanks where
the product is processed. I will send samples for external laboratory
testing to confirm this trend.
 The data shows a link between how the grafts are processed and
the porosity test results. I will carry out an experimental protocol
to prove/disprove this trend.
 Observations of the tanks suggest that there is a rust coloured
deposit in the liquid. I will enforce regular cleaning of all the tanks
and monitor if there are any improvements in product yield.
4. HOW TRENDS WILL IMPACT THE TEAM’S FUTURE
ACTIVITIES

17. In order to draw conclusions from my data, I will follow these
steps:
1. Hold a discussion to interpret and explain my results.
2. Compare my results with the control group.
3. Determine the impact of my results on the subject I am
investigating – in this case the porosity test.
4. Summarise my results in a written report and issue this to the
appropriate employees.
5. DRAWING CONCLUSIONS BASED ON DATA