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Technologies for Executing Supply Chain Operations

It is incredible how modern Supply chain management and freight companies are increasingly dependent on advanced technologies. Nonetheless, the sector will be completely different in the next few years as innovation keeps pace. These are the latest technologies that are flourishing that have positively affected supply chain management. In the coming decade, there are more integrated supply chains with improved technology, which will result in more efficient and effective systems. With technology being executed in supply chain management, Professionals will be more efficient and self-driven. This article discusses how technologies have been implemented from a comprehensive supply chain management perspective.

Internet-of-Things (IoT) Technology

IoT is often called telematics in the logistics industry and works on three levels, the hardware infrastructure, which enables data exchange and processing, and the software layer. Internet of Things technology lets companies track and oversee goods in real-time. Real-time information about a shipment’s physical condition serves as a key benefit, as do opportunities for optimization. By using IoT solutions, companies can oversee goods in real-time, ensuring they arrive when and where they should. The logistics industry must maintain both historical and real-time data. IoT computing power has allowed data processing to be relocated closer to networks’ edges thanks to the growth of IoT computing power. It is necessary to capture, analyse, and act on data, with cloud-based services available. Using IoT in conjunction with Robotic Process Automation (RPA), it is possible to eliminate manual inventory processes. Using a network of IoT sensors, it is possible to do predictive analysis, which can lead to contingency plans based on what is detected, allowing for a better understanding of speed, cost, and security of transport.

Artificial Intelligence and Predictive Analytics

In the field of technology, the term “artificial intelligence” is often used interchangeably with automation; however, they have several important differences. Since artificial intelligence (AI) emulates human behaviour, AI is a more considered advanced technology. Despite this, artificial intelligence and automation complement one another. Through prescriptive and predictive analytics, artificial intelligence, for instance, can offer unparalleled benefits to supply chain management. using predictive analytics, freight companies can analyse their supply chain network and offer robust recommendations on how to make it more efficient. By implementing AI, predictive analytics can predict supply chain events even before they occur.

For example, by looking at sentiment analysis, an AI might be able to predict that a particular product’s demand will drastically increase in the next few weeks, allowing businesses to increase their stock on how the anticipation of the product increases in demand. It will be possible for the perfect AI to dynamically adjust supply chain management according to events based on its prediction without any human intervention at all.

Another area in which AI has made significant advancements that affects freight is driverless technology, As a result, AI-powered cars would be capable of navigating without human help. Several sensors collect big data, and it takes this information to translate into its navigation system. In addition, it can get access to information from connected devices and other vehicles. The use of self-driving vehicles would be beneficial to freight companies from a labour distribution standpoint since no drivers are needed, and since the vehicles are made designed to use fuel efficiently. In general, AI-powered analytics systems can analyse what the competitors are doing and this will be very beneficial to the supply chain industry.

Machine Learning System

Machine learning is a process, system, or software program to adjust and learn without being explicitly programmed for this. This allows technology to improve itself over time by learning from past experiences. By analysing the data pattern and predicting the outcome, machine learning enables the technology to improve performance. With new information that the technology is exposed to, this cycle repeats, refining it further. Various sources can be used to gather the data. Machine learning can be used to predict demand for specific goods and help manage the production of those goods based on this data. By taking advantage of machine learning in the supply chain, companies need to hold less inventory since products flow from one place to another more efficiently. With the use of machine learning, costs are reduced and quality is improved. As a result of upstream optimization, products reach the marketplace “just in time” for sales. With simple, proven administrative practices, managing suppliers becomes easier. There are several advantages to using machine learning software in this area. For example, it enhances package traceability by facilitating ETA calculations, As well as finding the quickest route in real-time, or preventing delays and interruptions, it can handle a greater number of third-party data streams.


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References

Aidilhaswin Hassan, DLSM. (2021). “Smart Technologies for Efficient Warehousing”. Retrieved from SIPMM: https://publication.sipmm.edu.sg/smart-technologies-efficient-warehousing/, accessed 16/09/2021.

A Volini, A. A Shah, R Koch, S Moradia. (2021). “Using blockchain to drive supply chain transparency Future trends in supply chain”. Retrieved fromhttps://www2.deloitte.com/us/en/pages/operations/articles/blockchain-supply-chain-innovation.html, accessed 14/09/2021.

Blume Global. (2021). “How Machine Learning Optimizes the Supply Chain”. Retrieved from https://www.blumeglobal.com/learning/machine-learning/, accessed 16/09/2021.

Gravity Supply Chain Solution. (2021). “How AI, Automation, and Big data are disrupting Freight”. Retrieved from https://blog.gravitysupplychain.com/how-ai-automation-and-big-data-are-disrupting-freight, accessed 16/09/2021.

M Ahmed. (2019). “24 Supply Chain Technologies which Are Shaping Present and Future of Supply Chain”. Retrieved from:http://www.scmdojo.com/supply-chain-technologies/, accessed 13/09/2021.

Michael Eng Tien Wah, ADLSM. (2018). “Data Analytics And Artificial Intelligence For Effective Logistics’’. Retrieved from SIPMM: https://publication.sipmm.edu.sg/data-analytics-artificial-intelligence-effective-logistics/,accessed 15/09/2021.

Michelle Lee Ean Wei, ADPSM. (2019). “New Technologies that will Impact Future Supply Chains”. Retrieved from SIPMM: https://publication.sipmm.edu.sg/new-technologies-impact-future-supply-chains/,accessed 16/09/2021.

Ng Zhen Hui, SDDL. (2021). “Key Technologies for Digital Logistics”. Retrieved from SIPMM: https://publication.sipmm.edu.sg/key-technologies-digital-logistics/, accessed 14/09/2021.

Susannah Lawrence, DLSM
Susannah Lawrence, DLSM
Susannah Lawrence has substantive years of experience in the food and beverage industry, and specifically in the field of operations, shipping and logistics coordination. She currently handles ERP consulting and quality assurance issues, and she is a member of the Singapore Institute of Purchasing and Materials Management (SIPMM). Susannah completed the Diploma in Logistics and Supply Management (DLSM) on September 2021 at SIPMM Institute.
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