Impact of Machine Learning on Supply Chain Management

Running Head: IMPACT OF MACHINE LEARNING ON SUPPLY CHAIN
MANAGEMENT
Impact of Machine Learning on Supply Chain Management
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IMPACT OF MACHINE LEARNING ON SUPPLY CHAIN MANAGEMENT
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Introduction
Currently, business environment is facing stiff competition calling for great demand
uncertainty, greater risk of supply and increasing competitive intensity thus negatively impacting
the supply chain management of organisations. SCM is a concept that entails provision of the
accurate product, to the correct clients at the correct time, price and best property (Connelly,
Ketchen & Hult, 2013). Meeting all these requires planning, developing and disseminating
information across all the stakeholders : suppliers, manufacturers, retailer, transporters and
consumers. The association with all the stakeholders thus makes SC more information-intensive
requiring various key technologies for seamless flow of product and information. Thus
professionals have explored various ways of machine learning on the impact of the SC. The
current paper thus is an exploration of how ML can transform the supply chain.
Machine learning
Learning refers to the ability to gain knowledge through understanding particular skills
and following instructions. In regards to machines, learning is also a process that enables them to
perform better. Machine learning is a typical non-natural intellect that enhances the algorithm or
the software to learn and adjust without explicitly programmed to do so thus making the
technology to teach itself to improve operations (Bottou, 2013). Machine Learning is continual
of the Traditional Programming that entails both facts and database are fed on the processor to
harvest the outcome, unlike the current that requires both the facts and outcome run on the
processor to generate syllabuses. Each of the current machines contains different components
such as: Representations that shows how knowledge is represented such as in the tree decision

IMPACT OF MACHINE LEARNING ON SUPPLY CHAIN MANAGEMENT
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tree Evaluation that enables the machines to make predictions and the optimisation that enables
the programs to generate processes.
Figure 1: Shows comparison between Traditional programming and Machine Learning,
obtained from (Bottou, 2013).
Types of Machine Learning
Machine Learning types are categorising into different categories:
Supervised and Unsupervised learning- the supervised learning refers to a situation where
computers are provided with examples of inputs that are marked with the required outputs to
enhance the capability of the computer to learn (LU, 2013). Thus supervised learning entails the
use of patterns to enhance the prediction of the values of the additional unlabelled data. The
common used supervised learning in organisations in the capability of the machine to filter
emails out of spam emails (Iosifidis, 2015).

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While unsupervised machine learning, refers to the situation whereby the computer is left to
identify the commonalities among the fed data. The unsupervised learning process aims to allow
the machine to find the desired pattern among the dataset and enable it to classify the identified
data accordingly. The unsupervised data thus is essential for the transactional data such as in
customers purchasing behaviour, essential in the Supply Chain Management.
Decision Tree Learning- Decision Tree Learning refers to the machine learning process that
entails the application of the visually represented decisions. Decision Tree Learning aims to
establish a predictive model that will predict the future targeted value based on the current inputs
variables that are determined through observation (Shere, 2017).
Deep learning process -aims at imitating the capability of the human brain in processing light
and sound stimuli into vision and hearing. The process is inspired by the biological neural
networks that consist of numerous artificial neural in layers that are composed of hardware and
GPUs (Jiung', 2017). The Deep Learning thus allows the machines to extract data through a
cascade of the nonlinear processing unit to provide artificial intelligence space, thus applicable I
recognising speech and images.
Reasons driving for Machine Learning
Numerous reasons require machine learning in current technology. Some of the reasons
are:
When the tasks are too complex to the program-In organisation, numerous activities such as
production are carried out by human that always have insufficient information on how to do the
same tasks. Some task such as speech recognition, driving and image understanding require the
art of machine (Wang, 2018).