Introduction Figure. The accuracy from the AI system is

Introduction

Product
design

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Product Design

Product design in product development creates a design in
accordance with the technical, manufacturing, ergonomic, market-related and
functional requirements and specifications. Dependent on the company this is
achieved through different procedures and systematic checks to ensure the
product meets these requirements and specifications to ensure their success.
Computer Aided Design (CAD) and Product Data Management (PDM) have led the way
in the advancement of Product Design in recent years with the requirement of
more integrated design. These methods however still use traditional design
methods and are not fully integrated.

https://pdfs.semanticscholar.org/ad48/d28bb04c196036f743c89f5e519ff7b74924.pdf
1/1/2018

https://link.springer.com/chapter/10.1007%2F978-1-4020-7829-3_32
1/1/2018

https://uxdesign.cc/how-ai-will-impact-your-routine-as-a-designer-2773a4b1728c  1/1/2018

Artificial
Intelligence Impact on PD

There is currently
some AI integration in product design. Artificial Intelligence is applied to
product design in Monozukuri, the Japanese way of manufacturing. The Monozukuri
AI framework was developed on the cloud as a system to facilitate efficient
collection of product development data and for managing and leveraging learning
models from such data. With the Flexile Technical Computing Platform (FTCP),
Fujitsu’s integrated development platform, the Monozukuri design development
environment will provide various design tools with new enhanced
design-assisting features. Using this system, there is the potential to
estimate the number of layers in PCBS. This is done using an algorithm
optimised based on PCB designer data. Ordinarily this would be a difficult and
time consuming task even for skilled designers. The learning model uses the
data in a regression analysis method from the circuit design stage only which
is refined down to similar product and PCB type before an algorithm is
optimised. This allows for an estimation to be made on the number of layers
other than signal layers. This allows for an evaluation of the accuracy of the
algorithm made by the AI system to be evaluated as seen in the Figure.  The accuracy from the AI system is comparable
to the accuracy of the worker but is derived in a faster time. This use of AI
is a good example of how the integration into the product design field is time
saving as well as resource saving as the designers who would have previously
had to solve this would’ve spent their time solving the issue.

https://pdfs.semanticscholar.org/ad48/d28bb04c196036f743c89f5e519ff7b74924.pdf      1/1/2018

Although we have seen successes, we have also seen failures
in terms of Artificial Intelligence’s involvement in design.  The Grid was a website development and design
system powered by AI where the website was built without the need of a
designer. The AI aspect is mainly in the presentation of content. A custom
display form is selected for the content to be published in automatically by
the AI segment. This is done by analysing pictures from which the colour values
and extracted, texts placed and teasers produced, ensuring the design rules are
met. The limitations of the system are vast as the system remains basic. There
was large disapproval after the first websites were released with a general
consensus that designers should not fear for the jobs as of yet. The use of AI
here shows the limitations at present with its use and that further development
of the use of AI in product design will be positive but also that the
commercialisation stage of its integration is not yet here.

http://goati.info/

https://www.denkwerk.com/en/blog/designing-websites-with-ai-thegridio.html

Future
AI in PD

Conclusion

Market Introduction

Market Introduction is the introduction of new products into
the market place. Market introduction is a careful balance of understanding the
market place and ensuring the products strategic placement in the market is
profitable. An understanding of the competition the product faces can be
established through a strengths, weaknesses, opportunities and threats (SWOT)
analysis to decipher how the competition have approached their product release.
This is important even if the product is entirely unique as it also includes
comprehending what the prospective customer is anticipating. Consideration of
the ideal target market, unique advantages of the product, marketing strategies
and tactics as well as the approach should be undertaken to ensure the best
possible introduction of a successful product into the market.

https://www.entrepreneur.com/article/76364  1/1/2018

The revolution of Artificial Intelligence in Marketing has
been encouraged due to the availability of affordable and accessible data
analysis tools and extensive datasets. With these new tools available, a new
data-driven approach to marketing decision making is thought to be within reach
over the next few years. Although currently such tools are available to
marketing analysers, AI is required to help integrate between the marketing
platforms, datasets and available tools. There is also the difficulty of
integrating a vast amount of different tools, sources and platform packages to
provide an insight but with the influence of AI it’s believed that changes will
be made to incorporate these into a more manageable system. AI will take the
outputs from the various data sources and provide an analysed integrated view
of marketing positions. This will be a time saver for large corporations.

We do have examples of AI Marketing available to us
presently. Netflix using predictive analysis to finesse recommendations for its
account holders. This is based on clustering algorithms to continually improve
suggestions based off of the customer’s choices so that they can make the most
of their subscription. This ensure that the customer is continually satisfied
as new products are made available to them so their subscription will continue.
The choices, however, based off this algorithm aren’t always correct and the
customer is provided with products that don’t satisfy their need. This shows
that although AI is integrated into our current services, further work on the
analysis of human preferences is required.

The future of AI in Marketing is more integration of the use
of algorithms to save time and efforts which are utilised elsewhere in areas
not yet integrated. AI based sales and marketing automation are also set to
accelerate in the future. AI will help companies build a corporate culture with
customer focus at the forefront which will help optimise marketing goals such
as personalisation, customer behaviour and engagements as well as increasing
accuracy over predictive analyses.  Some
applications that are being investigated within the next 10 years include
further development of the virtual assistant such as siri to become more
intelligent and interactive, more personalised marketing campaigns as well as
more natural language processing into gauging customers moods and intents.

Why AI is the Future of Marketing


 

Integration of AI comes at a risk as data is never perfect
and all interpretations of patterns and datasets, done by either marketers or
AI, will still be subject to bias and could be misled if data is incorrectly
analysed. And although AI powered tools and devices will save time, the human
part of a company’s relation with a customer is also important so spending time
with customers will become more valuable the more digitalised the process
becomes. As the process becomes more digitalised, the marketing workforce will
have also have to have a higher level of digital literacy to compensate.

https://econsultancy.com/blog/67745-15-examples-of-artificial-intelligence-in-marketing
1/1/2018

https://www.forbes.com/sites/andrewstephen/2017/10/30/ai-is-changing-marketing-as-we-know-it-and-thats-a-good-thing/#72518951dc40
1/1/2018

In terms of Marketing, the effect of the AI integration can
be seen at present but there are vast improvements and various other means of
AI that will be integrated into marketing in the next 10 years to improve the
efficiency and accuracy of the data analysis available.

Manufacturing Functions

Manufacturing Functions has been ever expanding since the
first industrial revolution. The first, second and third revolutions saw the
introduction of mechanical production, mass production and automated production
respectively. Now the fourth industrial revolution is upon us, building upon
the third revolution which aim to fuse the physical, digital and biological
advancements.

Through the use of the internet it’s expected that the manufacturing
processes will revolutionise the way they make and ship goods.

Current
AI in Manufacturing

Future
AI in Manufacturing

Overall

https://www.engineering.com/AdvancedManufacturing/ArticleID/15670/Bringing-Artificial-Intelligence-to-Manufacturing.aspx

https://www.forbes.com/sites/mariyayao/2017/08/08/industrial-ai-factories-of-future/#5a13bf84128e

https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/

https://www.forbes.com/sites/bernardmarr/2016/04/05/why-everyone-must-get-ready-for-4th-industrial-revolution/#48d7252e3f90

http://www.telegraph.co.uk/business/social-innovation/artificial-intelligence-future-of-manufacture/

https://mitpress.mit.edu/books/artificial-intelligence-applications-manufacturing

Increasing Effect of Artificial Intelligence

Operations Management is concerned

Effect on Operations Management Positions

Operations Management is concerned with planning, organising
and supervising production, manufacturing and services.  It focuses on delivery of plans and ensures
that inputs such as resources are turned into outputs as planned.

https://www.readitquik.com/articles/off-premises/how-ai-is-changing-the-face-of-operations-management/

https://www.topmba.com/mba-programs/what-operations-management

Conclusion

anasin

 

 

 

 

 

 

 

 

 

Give
consideration towards the impact upon the individual in the working environment
as well as the end-user consumer of the manufactured product or service. 25
marks

Introduction

The
organisation of work

Work Organisation

The organisation of work will be impacted as for economic
reasons numerous jobs will be carried out by intelligent software or machines
rather than humans. The reasoning behind the replacements is more between
routine and non-routine work rather than physical and cognitive work as AI has
the ability to be developed for decision making but cannot reason like a human
so will only be able to perform routine work. 
There are differences in opinion about how much of an impact these
changes will have on company structures.

Restructure of Workers Alongside AI

The
jobs at risk are primarily labour driven. Routine jobs would easily be replaced
by those Replacement of Labour – Less work for labourers as AI would be capable

Retraining
of Labourers and Re-evaluation of Education System

Wouldn’t
affect skilled thought driven jobs – Quantative Analysis would be available to
AI but not qualitative analysis such as emotions, experiences, trend etc. Would
be difficult for AI to make decisions in times of uncertainty.

High risk jobs will not replace completely, even if the
technical advances would allow the replacement as there are further
implications as well as benefits to those introductions. An example of this is
the role of a bartender whose job is at 87% chance of being replaced as a
machine could mix drinks and perform the other roles of a bartender. The
implications of this, however, is the human interaction of the bar environment as
this will be removed if humans are replaced.

In view of the occupational work structure and the legal,
technical, ethnical and social barriers, only 9 to 12 per cent of employment
will be at risk of being completely replaced. Other studies expect that AI and
robotics are not job killers with the eliminated jobs being compensated for by
newly created jobs. For example, the German government assumes that the
introduction of Artificial Intelligence will create about 390,000 new jobs over
the next ten years in Germany.

Newly created jobs will require the retraining of workers.

Jaimovich and Siu, The Trend Is the Cycle: Job Polarization
and Jobless Recoveries (2012) 8 ff

Bonin, Gregory and Zierahn, Übertragung der Studie von
Frey/Osborne auf Deutschland (2015) 18. Ibid at 14 www.rolandberger.com/publications/publication_pdf/roland_berger_amcham_business_barometer_2.pdf,
15 (last accessed on 22 September 2016).

 www.buendnis-fuer-industrie.de/unsere-themen/digitalisierung
(last accessed on 26 April 2016).

Impact on Products

Quality – less variable and higher

Price is cheaper due to labour cost decreases – more is
within reach of the common purchaser due to price decrease, upgrade market
would have to change/turnover rate of new products would increase. A lot of
obsolete tech would be with consumer. Phones, cars

Fake products to be made

Less personal touch

Less variety/customisation

Impact on Consumer Groups

Demands would change/new consumer group – New capacity
planning would be required.

Impact on Customer

Quicker delivery

Wider malicious use of AI – burglaries/drugs to prisons –
need more protection

Glitches/Issues – TFL – Whole system goes down