Transformation of Supply Chain Using Machine Learning | Assignment Help
Introduction
Gaining a strategic competitive advantage in the industry over the competitors has, according to Mishra et al. (2018), gained a critical importance of the conventional business organisation in the contemporary corporate landscape. At the same time, the level of sophistication of the contemporary technology has also increased to a significant level. A perfect example of this is that of order fulfillment cycles that make use of the post-mortem data analysis specifically for identifying the various gaps that have been left as a consequence of human error. In this way, the advent of machine learning, according to Meredig (2017), has potentially helped different companies analyze data in real time. However, the most important question as to what the needs of the conventional business organisation are that particularly strengthen the concept of technical amendments in business and especially the supply chain management practices still remains unanswered. This particular report serves the purpose of answering this fundamental question; the report has discussed extensively as to how the transformation of supply chain management practices of a business organisation has been carried out by machine learning.
Machine Learning and Supply Chain Management
Bumblauskas et al. (2017) argue that the contemporary corporate landscape has, in the recent times, reached a level of automation within the manufacturing industry where the score of the reliability is quite high. In this connection, there are various companies which are effectively utilizing big data and advanced business analytics so as to give themselves a boost specifically in the area of supply chain management. Machine learning and supply chain management of a business organisation have, consequently, developed an innate relationship which is characterized by a number of factors as being discussed in the following lines.
Accuracy of Demand Forecasting
The advent of machine learning has resulted in a number of applications on the existing factors especially in the production sector – the sector which has been referred to as one of the most dynamic and volatile areas of supply chain management by Ivanov (2019). A classic example of this is that of Lennox; the company has mastered supply chain management by improving its SAP planning system input. Apart from this, the company, according to Sokolov (2019), now enjoys a balance between the inventory cost and the service levels.
Supplier Risk Mitigation and Freight Cost Minimization
This has been regarded as the most needed and anticipated improvement in the supply chain sector of the business. According to Daryanto (2018), the advent of machine learning paves the way for identification of the synergies of a horizontal collaboration nature which exist between multiple networks of the suppliers of an organisation. The development of IBM Watson, Ahler’s Supply Network Innovation and Analytics (ASNIA), and TRANSMETRICS has potentially helped, to date, multiple business organizations mitigate their supplier risks and minimise their freight costs.
Process Transformation
As it can be seen in the work of Mori (2016), in the past, there existed an ambiguity in the interpretation of the various records and orders within the supply chain management. However, with the advent of machine learning, the supply chain management practices have been transformed to an extent that now there is a clear and apt shipment. Also, the pieces-identification has become quite easy, as no non-piece lines exist anymore within the supply chain of a business.
Khalid (2018) compares the clutter in the measurement units which was an important characteristic of the supply chain of the past; according to the author, the supply chain of the historic organisation would entail a clutter of the measurement units. However, in the contemporary era of machine learning, there now exists a complete measurement set, as the volume, surface/pallets and the loading metres can now potentially be defined comprehensively. This idea has been supported by the work of Wu (2018). According to the author, missing information regarding size of the order and the piece-level was a characteristic of the past which has now transformed the supply chain to three-dimensional factors of loading with the help of a complete measurement for each piece.
The process, according to Addo-Tenkorang & Helo (2016), entailed data redundancy issues, such as that pertaining to the suppliers with the same name. Machine learning has paved the way for the categorization or the clustering of the various suppliers so that this data redundancy is minimized. Specially for the linehaul, reliability and the availability of capacity information were two much challenged characteristics of the supply chain of the past. Machine learning has, according to Yan (2017), developed such artificial intelligence algorithms which accurately make reliable predictions about capacity information for the linehaul.
Supply Chain Optimization Using Machine Learning
According to Chae (2015), the contemporary supply chain is characterized by a vast amount of data which is quite complex in nature. Nevertheless, machine learning can fundamentally analyze all of this information along with utilizing the findings in order to optimize the supply chain. Hence, one of the most important transformations of the supply chain using machine learning is in the category of the optimization of the supply chain.
Revolutionizing the Supply Chain Using Machine Learning
Meredig (2017) argues that the essence of using business analytics in order to formulate business strategies is closely related with the discovery of new patterns in the data pertaining to supply chain. This, according to the author, has the potential of revolutionizing the business. This is where the role of machine learning gains a paramount importance; these machine learning algorithms constantly unveil new patterns within the supply chain data without necessarily requiring any sort of human intervention. The practice of optimising the key parameters of the supply chain including the supplier quality, the inventory levels, demand forecasting, order-to-cash and procure-to-pay, transportation management and production planning is now being revealed by machine learning for the good of the business.
Challenges in The Effective Integration of Supply Chain Practices with Machine Learning
From the discussion presented in this very report, it is quite evident that the supply chain management practices are the conventional business organisation are being influenced by machine learning and are being transformed in many ways. However, there are different authors in the contemporary research literature who maintain that certain challenges lie in the way of the complete integration of supply chain practices with machine learning. These particular challenges are important to be discussed if the supply chain management of an organisation has to be fully integrated with machine learning in order to derive the maximum benefit out of this relationship.
