Mass Customisation & Big Data

Question 1

Wha is mass customisation?

Answer 1

Ahlstrom & Westbrook (1999)

“Production of items at a speed, quality and cost associated with mass production”

Question 2

Product change / process change diagram

Answer 2

Boynton, Victor & Pine (1993)

• Look in diagrams booklet

Question 3

Types of Mass customization

Answer 3

Gilmore & Pine (1997)

Collaborative
- Customer collaborates with org to identify needs and construct a suitable product

Adaptive:
- Customer adapts the product post sale to suit their own needs

Cosmetic:
- Standard product presented differently to different groups of customers

Transparent:
- Observe customers behaviour and adapt product offering to their needs without telling them.

Question 4

Order point penetration

Answer 4

the point at which a customer order will influence the production process

Question 5

Benefits of mass customisation to the producer

Answer 5

• Early feedback on shifting customer preferences and fashions
• Ideas for new products
• Less waste
• Build strong loyalty with customers

Question 6

Risk to the customer

Answer 6

• Customer might not know exactly what they want, this uncertainty about the design process might translate into production uncertainty.
• Products can only be assessed virtually.
• Long lead times
• Pay in advance

Question 7

Consumer generate IP

Answer 7

Berthon et al. (2015)

• Consumers are taking product and modifying them in such ways that they’re creating their own value.
• This blurs the line of intellectual property.
• Result is conflict

Question 8

Use of big data in car insurance

Answer 8

E&T Magasine (2016)

• Use of telematic devices
• Identify fraudulent claims
• Reduce the cost of processing claims
• Encourage safer driving
• pay-as-you-drive insurance

Question 9

Big Data Risks

Answer 9

Security:
- Data thefts

Privacy:

• reveals a lot about your personal behaviour that can be instrumental in crime & theft
• Information can be used to determine personal characteristics that may be used against you in insurance / job opportunities.

False assumptions & errors:

• boston street bumps.
• Any data analysis is subject to selection bias, or OVB

Information Asymmetries
- Can be used to price discriminate, against the interests of the customer

Civil Rights:
- Data mining may inadvertently focus on variables that are strongly correlated with (race).

Question 10

Boston Street Bumps

Answer 10

Boston mayor decided to crowdsource road quality analysis, and urged citizens to install their ‘street bump’ app on their phones, in order to individual track, using their accelerometers, the road quality.

Ingenious idea which takes advantage of the movement of people already, and their will to help the community.

HOWEVER, it revealed that all the potholes were in middle class areas, where there was more of a trust of government and a will to download the app, whilst poorer areas went un-repaired.

Question 11

Big data in healthcare

Answer 11

Improvement in

• Clinical decision support
• Disease surveillance
• Population health management

Kenneth Cukier
- Data mining of cancerous breast samples revealed 12 common trends, only 9 of which were previously known to doctors.

Google can be used to predict flu outbreaks 2 weeks faster than national health services.

Question 12

Internet of things

Answer 12

Xia et al. (2012)

“Networked interconnection of everyday things that are equipped with ubiquitous intelligence”

• Leads to risk of systems breached (EG: the Wannadie ransomware that hit the NHS prevented emergency services because their operations were too dependent on the internet of things