Supply Chain Management Online Tutoring
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 logistics and 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.
Supply Chain Management Online Tutoring from Experts
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 minimize 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 meters 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.
Optimization of Speed
Mitchell (2017) argues that machine learning is capable of analyzing the timing and the ‘hand-overs’ as the products are transferred through the supply chain. It is also potentially able to compare the data not only with the historic performance of the supply chain but also with the standards or the benchmarks set for this purpose. In this way, bottlenecks in the operational processes of the organisation maybe minimized and recommendations may be made to enhance the speed of the supply chain.
Movement of Goods on The Basis of Consumer Demand
It has been argued in the work of Sanders (2016) that effective supply chain practices heavily rely upon keeping the right product at the right place on the right time. In the contemporary business landscape, machine learning can potentially evaluate the requirements of the consumer, thereby giving the business an opportunity of optimizing the upstream supply chain. This is achieved by matching the timely goods supplier with the demands of the marketplace.
How to make a Perfect Supply Chain Management Project
Keeping the suppliers in loop, according to Wamba (2016, January), is a much-dreaded challenge in the contemporary business world. This has been referred to as one of the most challenging tasks of the supply chain management. However, if machine learning is adopted by the conventional business organisation, it can massively reduce the burden on part of the organisation. Machine learning, according to Mishra et al. (2018), can potentially analyze the contract types and documentation with the suppliers to use the same as the foundational basis of any future administration or agreements with the suppliers.
Quality Assurance from Suppliers, Assets and Products
The quality of the supplies is one of the most important characteristics of an effective supply chain, primarily due to the fact that products which are faulty increase the cost of production and sometimes call for unnecessary rework. Machine learning, according to Meredig (2017), monitors the way in which quality of a certain supply varies with time, thereby by suggesting improvements when needed. However, it is worth mentioning that this does not apply to only the products and the materials involved in the supply chain; machine learning can also potentially track the third-party quality, shipping and even the suppliers of the business.
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 optimizing 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.
Improvement in Supply Chain Management Performance
Machine learning, according to Bumblauskas et al. (2017), combines the strength of supervised learning, unsupervised learning and reinforcement learning. Using this combination, it essentially proves itself as a remarkable technology which continuously aims to target those key factors which are of critical importance to the conventional supply chain of the business. This has empirically improved the performance of the supply chain management of the conventional business organisation tenfold.
Opportunities of Physical Inspection and Maintenance of Physical Assets
According to Ivanov (2019), machine learning has been designed using algorithms which are quick in seeking compatible patterns in an eclectic range of data sets. In this way, machine learning has revolutionized the supply chain by automating the process of inbound quality inspection with the help of isolating product shipments that are characterized by damage and wear. For instance, the algorithms related to machine learning in the platform of IBM Watson were potentially able to find out if a shipment container had any damaged goods or items, classify the same using damage time as a parameter, and then recommend a corrective strategy so as to repair the asset. This particular software integrates the systems-based data with the visual-based data to track the physical assets of the supply chain and make recommendations regarding any corrective action in real time.
Using Contextual Intelligence Following Inventory and Operations Costs
The businesses in the contemporary corporate landscape are using machine learning, according to Bumblauskas et al. (2017), to gain a greater contextual intelligence. This contextual intelligence comes with the combination of machine learning with technologies that are related to the operations in the supply chain sector. The ultimate payoff of this is in the form of lower operations and inventory costs and a faster rate of response to the customers. Ivanov (2019) argues that machine learning is now finding its adoption in the logistics control tower operations so as to provide an insight into the way in which every aspect of the supply chain practices, warehouse management and logistics might as well be improved.
Forecasting Demand for New Products
One of the most important areas of focus of machine learning is that it has the potential of forecasting demand for new products. This sort of demand forecasting, according to Mori (2016), includes the causal factors which are the drivers of new sales. Many businesses in the contemporary business landscape use pragmatic approaches of asking direct and indirect sales teams and channel partners about the number of products they are willing to sell. At the same time, there are other businesses operating in similar industries which use complex statistical models to predict the same. In either of these cases, machine learning, according to Sokolov (2019), has proved to be a valuable resource for the consideration of the causal factors which potentially influence the demand but had never been discovered before.
Extending the Life of Key Supply Chain Assets
Businesses are now focusing upon extending the life of their key assets of the supply chain. These key assets include machinery, warehouse and transportation equipment, and engines. The underlying mechanism of this process of extension of the life of supply chain assets is characterized by the determination of new patterns in the data which has been collected using sensors related to the internet of things. This is particularly the case in manufacturing industry; according to Addo-Tenkorang & Helo (2016), machine learning has proven itself to be one of the most important and perhaps the most valuable means of analyzing data of a machine-derived nature in the manufacturing industry. This has helped businesses find out as to which causal factors significantly influence the performance of the key machinery and assets of the supply chain. In this way, machine learning is potentially leading the way to a more accurate metric of ‘Overall Equipment Effectiveness (OEE)’ which has gained a critical importance in the manufacturing and supply chain operations of the industry.
