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Running head: ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
Name
Institution
Date
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ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
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Table of Contents
Research Concept…………………………………………………………………………………………………………………. 3
I.
Overview ………………………………………………………………………………………………………………………………… 3
Methodology …………………………………………………………………………………………………………………………… 3
Journal Summaries ……………………………………………………………………………………………………………….. 5
II.
A.
Summary of Article: AI and Professional Roles ……………………………………………………………………. 5
B.
Summary of Article: Big Data for SCM ………………………………………………………………………………. 6
References:…………………………………………………………………………………………………………………………………. 9
Appendix A: Copies of Articles …………………………………………………………………………………………………… 11
ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
I.
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Research Concept
Overview
Supply chain management is an essential integral part in almost every firm today, but it is
yet to receive much support from artificial intelligence as compared to sectors such as finance
and healthcare. Artificial intelligence is the application of computer-aided technology for the
performance of various tasks just like human beings would do (Michalski, Carbonell & Mitchell,
2013). In supply chain management, artificial intelligence is used in planning and making realtime recommendations or decisions based on the current and historical available data.
Organizations which have embraced artificial intelligence techniques reap the benefit of its
ability to do automated internal research and provide answers regarding procurement functions.
It would include supplier selection, placement of purchase request, invoice and payments
documentations among other.
Artificial intelligence is increasingly becoming a concern in logistics, especially in
shipping companies within supply management function. It is inferred that autonomous vehicles
can transport goods in double the duration it would take a truck-driver to do so. This application,
in turn, would reduce the lead time, transport costs, labor costs. This research will explore the
various artificial intelligence techniques which are used in supply chain management.
Methodology
In investigating the techniques of artificial intelligence that are used in the supply chain
management field, the study will use an online survey method to collect primary data. The other
method is the use of publications to collect secondary data. Data from the secondary sources will
be relevant in the assessment of the techniques which are already being used by some firms and
ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
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their applicability to supply chain management. This study will analyze the following distinct
techniques of artificial intelligence; machine learning, agency systems, fuzzy logic and artificial
neural networks.
Machine learning technique is designed to give computers power to learn, investigate a
problem and provide a solution to that problem (Liu et al., 2013).. It collaborates information
between concerned parties to enhance supply chain visibility.
An agency system in artificial intelligence is a technique which the ability to distribute a
problem within itself and then solve them independently. It is a useful technique is solving
complex supply chain management functions particularly in logistics planning, forecasting of
aggregate demand, monitoring of orders, evaluation of bids, procurement of services, and
information tracking across diverse supply chain partners.
Fuzzy logic, on the other hand, is a technique with the ability to make a system-generated
opinion though with the input of expert knowledge. In most cases, it is used to provide an
opinion based on whether the variables under consideration are “good” or “bad” (Ngai et al.,
2014). In supply chain management, fuzzy logic could be used for evaluating the bullwhip effect,
evaluation of supplier performance, supplier selection, inventory control and fulfillment of
orders.
The last technique to be focused on in this study is artificial neural networks. According
to Zhong (2016), this system uses an interconnection of computers to learn from experience,
generate patterns, and process extreme information. In supply chain planning, artificial neural
network technology could be used to determine the time frame for setups, approximating the
batch or order sizes between successive orders, making inventory decisions, and scheduling
production in higher levels.
ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
II.
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Journal Summaries
A.
Summary of Article: AI and Professional Roles
Artificial intelligence and professional roles. Business Information Review
According to the author of this business information review, most businesses today have
focused on artificial intelligence to handle tasks more robustly as a means of supplementing
human effort. Current capabilities of artificial intelligence and machine learning are well
exhibited in autonomous cars. In this case, driving these cars takes place in a controlled
environment and in accordance with the defined rules. Occasionally, in driving, it is hard to
either predict or repeat situations hence the need for fuzzy knowledge in artificial intelligence to
make some decisions. The application of autonomous vehicles makes use of Google Now to
predict where one is likely to travel to without requiring any human assistance.
Artificial intelligence has gained usage in various workplaces such as in the field of law
to mine data and generate law reports. The impact of automation and artificial intelligence is
getting inevitable in many professional and commercial sectors, a move which is seen as an ideal
replacement for human skills. Implementation of learning technology in supply chain
management is transforming the supply chain functions from the traditional way to a
technological approach. It has become more accessible to handle complex tasks in estimating the
unpredictable control of inventory and use of system-generated reports to make supply chain
decisions in real-time
The author concludes by indicating that the changes in technology would soon become a
long-term issue to a level that professional duties will be replaced by artificial intelligence and
machine learning. Besides, the identity and credibility of expert knowledge will be undermined.
Nevertheless, they will provide tremendous opportunities to be exploited, not only in the
ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
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professional sectors but also in commercial setups. To sum up, this article is relevant to this
study because it provides an analysis of how the artificial intelligence and machine language
techniques are applicable in organizations today.
B.
Summary of Article: Big Data for SCM
Big Data for supply chain management in the service and manufacturing sectors: Challenges,
opportunities, and future perspectives. Computers & Industrial Engineering
This article mainly focuses on an investigation of the application of big data from
manufacturing centers and service centers such as tourism, restaurants, healthcare, finance and
economics, and supply chain management. Besides, it reviews the big data analysis, algorithms,
techniques of data visualization, and models. The paper provides a discussion resulting from the
analysis of big data movement for supply chain management in the manufacturing and service
sector of late globally in regions such as Europe, Asian Pacific, and North America. Besides, it
presents the challenges and opportunity experienced today, as well as the future outlook of
aspects such as transmission of data, application of data and the interpretation of the same data.
Besides, this paper entails methods of data collection, transmission, and decision-making models
for big-data in future.
According to the authors, the increased volume of data has been facilitated by gathering
of the same factors such as aerial sensory, bar-codes, sensor networks that are wireless,
microphone among others. Handling of such a big volume of data, as a result, becomes a
challenge to the processing application that is traditional. The vast flow of data in both the
manufacturing and service centre comes along with a lot of problems. These challenges are
regarding the value, verification, variety, velocity, and volume. Big data is experienced due to
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the endless activities and transactions that do take place. Some of the financial institutions have
of late accommodated big data technological initiatives. Big data analytics has of late become an
essential element in the health centers due to the rise of adoption of digital records.
Supply chain management is carried out following the large and ever-growing amount of
big data that ranges from digital machines, Smartphone among others. To enhance competitive
advantage, various companies have taken some steps in big data fields. These companies include
DHL. The manufacturing sector has been known to keep a big range of data as compared to other
sectors in the economy. To optimize their operations and to enhance their competitiveness,
manufacturers ought to use big data (Zhong et al., 2016). The three major stages of big data start
with the pre-processing, and then go to the processing, and finally the post-processing. An added
advantage of big data technology is that it has shared services and converged infrastructure. The
technique of big data visualization is crucial in formatting, standardizing, and interpreting data to
be used by the scientists.
Data analytics assists big firms in analyzing a large amount of data that are obtained
from a variety of sources such as the internet, mobile phones or even database. Tools for
financial risk assessment as well as the fraud detectors ought to be used to ensure market
analytics is achieved. Suitable techniques from the large information technology companies
provide for the more easy analysis of big data. In support of service and the supply chain
management, models for big data have been reported. In ensuring that the data is well presented,
and structured a data models must be used for that case.
In the future, data collection will
mainly be focused on smart technologies and sensors more suitable for data collection. It will
also focus on techniques with the ability to collect data in extreme circumstances and
environments. This paper has outlined the application of big data in fields such as current
ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
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movement globally, supply chain management and service management as well as the
challenges, opportunities, and future perspectives; hence its relevance in this study.
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References
Liu, S., Leat, M., Moizer, J., Megicks, P., & Kasturiratne, D. (2013). A decision-focused
knowledge management framework to support collaborative decision making for lean
supply chain management. International Journal of Production Research, 51(7), 21232137.
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An
artificial intelligence approach. Springer Science & Business Media.
Ngai, E. W. T., Peng, S., Alexander, P., & Moon, K. K. (2014). Decision support and intelligent
systems in the textile and apparel supply chain: An academic review of research
articles. Expert Systems with Applications, 41(1), 81-91.
Tredinnick, L. (2017). Artificial intelligence and professional roles. Business Information
Review, 34(1), 37-41.
Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big Data for supply chain
management in the service and manufacturing sectors: Challenges, opportunities, and
future perspectives. Computers & Industrial Engineering, 101, 572-591.
ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
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ARTIFICIAL INTELLIGENCE TECHNIQUES USED IN SUPPLY CHAIN
Appendix A: Copies of Articles
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