Introduction
The digitalization of the supply chain is transforming the analog processes of the supply chain into digital processes. The main contributor in terms of technology in the digitalization of the supply chain is machine learning technology, AI. Digitizing the supply chain process greatly enhances the free cash flows and increases the production rate. This greatly helps the companies to meet the increasing demands of the customers in the market. In traditional supply chain management, the cost of manual processes is significantly higher. Digitalizing the supply chain helps to reduce costs by reducing the manual process by implementing automation. In this report, the digitalization of the supply chain is briefly discussed along with the involvement of big data analytics, machine learning, and Industry 4.0. This report also discussed how digitalization helps in the improvement of the whole supply chain management.
Big Data Analytics in Supply Chain
The greater access to customer data their property, and purchase sources leads to a requirement for a larger database to store all the data. In the organizations the competition is not between them moreover it is between their supply chains (Aamer 2020). The increasing attention on the supply chains forces the managers to implement new technologies and strategies in their supply chains to stay competitive in the market. In modern times both data and technology are greatly available to all organisations. It is up to the managers or leaders of the organizations to decide how to use them. Gaining visibility on expenditure, identifying new trends in the market related to price and performance, inventory tracking, optimizing the manufacturing process, and manipulating the supporting system, the managers of the supply chain greatly rely on data. Big data analytics solves many issues in various domains of business, especially in sales and operations. Big data can affect all aspects of the supply chains (Agrawal, Prakash, and Rakesh 2018). The value is added to the overall operations of the supply chains by improving the efficiency of the operations which is based on the result of the data analysis. Using big data analysis in marketing can transform the overall understanding of the customer into an agile system which helps to transfer a huge amount of information to the upstream in the supply chain. Big data also helps in procurement management. The complexity of data may emerge from the global strategies of purchasing along with huge transactions. Big data also helps to manage warehouses. The modern identification system greatly modifies the warehouse or inventory management after the implementation of radio frequency identification or RFID. In the manufacturing phases of the supply chain, big data greatly helps to optimize the stock ranges, optimise management, and to manage some other facility locations. In the digitalization of the supply chain models, big data can be used in real-time monitoring, supplier sourcing, customer segmentation, knowledge sharing, forecasting demand, and simplifying the distribution process (Babenko et al., 2020). Implementing big data in the digital supply chain model can get many benefits to the business, for example, better visibility in the overall supply chain, better customer experience, improved forecasting demand, increased efficiency in the manufacturing process, providing solutions for the complex distribution network related problems and better inventory management. Apart from the benefits, big data analytics also may pose several challenges including obstacles in data collection, storage, searching process, and visualization. Data inconsistency and insufficient stability are also some challenges related to big data. So before applying big data to supply chain management, organizations should consider its challenges too.
Machine learning in the Supply chain
Machine learning is made up of artificial intelligence. It can learn and adjust without needing any specific programming by using algorithms and software. The models of machine learning tech themselves on their own over time by analysing the current trend and by identifying anomalies (Berawi, Mohammed Ali 2018). By gathering information regularly, machine learning can identify the patterns in the supply chains and then suggest the next possible actions that can add value to the whole supply chain process. Generally, the yearly cost of manually entering the pieces of information in the back-end systems or ERP is over 1 million dollars. Manually entering the invoices derived from the suppliers also costs over 600 million dollars. Machine learning can also help to predict future faults in warehouses which greatly helps to prevent those faults. The manpower in the traditional supply chains can only highlight limited areas for improvement and also take much time to identify but machine learning technology can identify more specific areas of improvement without even taking much less time. This helps to deal with the challenges before it is too late and also prevents future challenges (Brinch et al., 2018). Implementing machine learning in supply chain management can provide efficiency in allocating and using resources. In the traditional supply chain model, manual paper-based processes consume a significant amount of time and also have a high chance of errors. Automating the processes can greatly save time and also reduce the chances of errors in the process. So implementing machine learning can provide more time to the supply chain and inventory management to pay attention to the other strategic activities. Training the machine learning models in the supply chains can greatly help to reduce the wastage of resources and also to identify the coefficient areas. Machine learning can also change the whole warehouse management into a more efficient model by reducing the overheads and highlighting opportunities related to its improvement (Chain 2020). Combining this machine learning technology with human operatives can greatly increase the speed of the processes that generally require a lot of time. This can help to provide more time to pay more attention to the matters that a machine can not solve. that Machine learning can also enhance the safety of warehouses. The high-risk tasks for example product loading and unloading can be done with the help of the machines greatly help to reduce the risks of accidents. So for the well-being of the workers, machine learning plays a major role in the workplace.
Industry 4.0 for supply chain
Digitalization creates a huge impact on the supply chain. Supply chain management is a complex thing that is now a beneficial position for digitalization. Benefits of Industry 4.0- accurate and transparent- the supply chain is a very huge and complex process for any organization (Chalmeta, Ricardo, and Nestor Santos-deLeón 2020). It became more complex in the global context. So after digitalization, it helps to maintain end-to-end transparency, which decreases the risk management factors in the supply chain. Also, it helps to get accurate data at every step of the supply chain.
Decision-making- advanced technology helps in the predict demand and algorithms and proper analysis of data help in the cost-saving decision-making process.
Connections- digitalization helps in the flow of information. Which helps everyone to connect. Suppliers, manufacturers, and customers are starting to collaborate on the next level. Which helps in building trust, and joint planning for the solutions.
Improvement of warehouses- any company’s warehouse is very messy. Mainly the supply chain part is very difficult because of inventory and transportation logistics. But digitalization improves it in a very significant way. Real-time accurate tracking, RFID technology, and other technologies help to reduce the mess.
Agility- implementation of digitalization or new technology in the supply chain management, makes the whole system agile. Technology empowers planning, sourcing, automation, effective analysis, collaborative work, and many more features that improve the agility of the supply chain. This also helps in decision-making, because everyone is deciding the same point of view. Big data analysis helps to manage huge amounts of data that come from different sources (Hossain, Aslam, and Nadeem 2019). Companies can not afford huge amounts of money to ignore the data and analysis of it. Big data analysis helps in the optimization of data, analysis of data, and making them useful. It helps the organization in decision-making, to increase productivity, and in the long term way.
Ways digitalization helps in improving the performance of the supply chains
Digitalization reaches every possible thing, also in business. It is also implemented in the supply chain. RFID, GPS, and Sensors are helping in the transformation of the company from the traditional structure (Ivanov et al., 2019). Different collaborative, agile, and flexible models help in the transformation and the improvement of performance. Digitization of the supply chain helps in planning, flexibility, automation, and logistic team automation. Driven growth, managed risks, and optimized costs.
Flexibility- With digitalization operational flexibility increases. For that, the management team is empowered by the great freedom which will help them to choose an appropriate degree for centralization. Which is needed to support productivity levels across all kinds of locations. It is also the centralization of an organization that helps in increasing the quality of products and the productivity rate.
Decision-making- digitalization in the supply chain helps in making faster decision-making. For each specific function, faster and more informed decisions can be made through the use of digitalization (Benzidia et al., 2020). At the macro level, effective and efficient performance and productivity rates can be measured. It helps to avoid distortion and make appropriate decisions and it is also cost-effective. It also helps to measure the stock of the product, minimize disruption, and replenish the supply.
Automation- end-to-end digitalization in an organization increases accuracy, creates efficiency, and improves data quality which increases supply chain efficiency. Automation of the whole supply chain facilitates the decision-making process at multiple stages of the life cycle. Also, automation helps in determining appropriate shipping modes and scheduling by considering time, carriage, priority, speed, and many other elements. It also automatically altered the danger of delay and complications. It helps to handle the consumer in a better way.
Innovation– digitalization and transformation of the whole process lead the organization to a single goal, which motivates them to innovate (de Vass et al., 2021). Constant improvement and upgradation is a conventional way of supply chain management. It helps in strengthening the company’s business model and building relationships. This relationship between the supplier and consumer with the company became very strong.
Customers- It helps to increase the engagement of the customer. End-to-end digitalization in the supply chain increases customer engagement. Automation of the process such as, from placing the order to receiving it, in this whole process consumers can be involved in it, Consumers or the receiver of the product can track their order after placing the order. This facility feels more secure to the customer and they also can control the situation.
Quality– in the supply chain process they have huge pressure, but with the help of the digitisation and automation process organisations maintain the safety standard of product quality. Maintaining the quality of the product became very risky in this dynamic market (Wenzel et al., 2019). Trade disputes, environmental changes, and economic pressures put extra pressure on the supply chain. It can be decreased by the use of digitalization.
Conclusion
In this project, it is discussed how the supply chains are affecting or improving their situation after digitalization. Implementation of new technology or Digitalisation of the supply chain which is implementing big data, industry 4.0, machine learning, printing, and many other technologies helps in many ways. In every possible way, digitalization helps the supply chain. it can smooth the process or reduce the risks. Planning, logistics, data analysis everything can be done by using these technologies. While doing this project some other aspects are coming out such as cash flow, information flow increased, and knowledge gap, misunderstanding, and low rate of productivity are decreasing day by day. Also, the implementation of technology enhances the chance of innovation, degradation, and development in the supply chain. It helps in decreasing the rate of risk and makes the supply chain process collaborative work. Every step of the supply chain helps to maintain the transparency level, and it helps in the decision-making process. Automation of the whole process increases the trust of the consumer in the supply chain. That helps to increase the involvement of the consumers in the process. Technological implementation will be more helpful in global supply chain management.
Bibliography
Agrawal, Prakash, and Rakesh Narain. “Digital supply chain management: An Overview.” In IOP Conference Series: Materials Science and Engineering, vol. 455, no. 1, p. 012074. IOP Publishing, 2018.
Babenko, I. V., A. Yu Anisimov, V. Yu Melnikov, I. A. Kubrak, I. I. Golubov, and V. L. Boyko. “Sustainable supply chain management in city logistics solutions.” International Journal of Supply Chain Management 9, no. 2 (2020): 1081-1085.
Benzidia, Smail, Naouel Makaoui, and Omar Bentahar. “The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance.” Technological Forecasting and Social Change 165 (2021): 120557.
Berawi, Mohammed Ali. “Utilizing big data in industry 4.0: Managing competitive advantages and business ethics.” International Journal of Technology 9, no. 3 (2018): 430-433.
Brinch, Morten, Jan Stentoft, Jesper Kronborg Jensen, and Christopher Rajkumar. “Practitioners understanding of big data and its applications in supply chain management.” The International Journal of Logistics Management (2018).
Chain, Improving Supply. “Data, not digitalization, transforms the post-pandemic supply chain.” (2020).
Chalmeta, Ricardo, and Nestor J. Santos-deLeón. “Sustainable supply chain in the era of industry 4.0 and big data: A systematic analysis of literature and research.” Sustainability 12, no. 10 (2020): 4108.
de Vass, Tharaka, Himanshu Shee, and Shah Jahan Miah. “IoT in supply chain management: Opportunities and challenges for businesses in early industry 4.0 context.” Operations and Supply Chain Management: An International Journal 14, no. 2 (2021): 148-161.
Hossain, Md Aslam, and Abid Nadeem. “Towards digitizing the construction industry: State of the art of construction 4.0.” In Proceedings of the ISEC, vol. 10. 2019.
Ivanov, Dmitry, Alexandre Dolgui, Ajay Das, and Boris Sokolov. “Digital supply chain twins: Managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility.” In Handbook of ripple effects in the supply chain, pp. 309-332. Springer, Cham, 2019.
Wenzel, Hannah, Daniel Smit, and Saskia Sardesai. “A literature review on machine learning in supply chain management.” In Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 27, pp. 413-441. Berlin: epubli GmbH, 2019.
