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Information Technology and Supply Chain Management
The exponential growth of Information Technology (IT) with communication technology within the Supply Chain Management (SCM) influences the management of companies in optimising decisions along with the the supply chain network and inventory planning (Hugos, 2018). In this regards, Marinagi, Trivellas and Sakas (2014) stated that the consideration of appropriate supply chain management decisions with consideration of contemporary technologies supports companies in achieving organisational competitiveness through which companies could improve the higher service level, lowering inventory level, the reduction in the supply chain costs and reducing electronic risks of different business transactions. In the context of contemporary supply chain management practices, companies integrate different technologies and information sharing tools to support the effective information sharing across for maximising the efficiency of supply chain management. In this context, Nakasumi (2017) asserted that the organisations are moving towards the virtual supply chain with consideration of a variety of technologies and and IT applications that include the Electronic Data Exchange (EDI), Bar Code, Radio Frequency Identification (RFID), Electronic Commerce, Enterprises Resource Planning (ERP) package, Decision Support system, and others. In maximising the efficiency of supply chain management practices, contemporary technologies help firms in integration of hardware, software, management of different resources, corporate supplies network, cloud-based information share system and others (Lotfi, Sahran and Zadeh, 2013).
Key Elements that Influence the Importance Information Management in Supply Chain Management
As per the research of Vanpoucke, Vereecke and Muylle (2017), it found that the increase in the popularity of ecommerce has enhanced the popularity of big data analytics and other internet-based technologies in managing the supply of different goods and services in an efficient manner. In the context, internet-based trade operations involve the application of different tools and techniques to manage different business operations in the form of paperless environment that comprises electronic data interchange, usage of e-mail, electronic fund transfers, electronic publishing of different products and services, image processing and information sharing, formulation of electronic bulletin boards, development of shared databases and optical data capturing. In addition to that, the approach of ecommerce help companies to automate different operations associated with transferring of records, documentation of data, sharing of information between customers and suppliers and application of data analytics (Waters and Rinsler, 2014).
The investigation of Addo-Tenkorang and Helo (2016) determined that the popularity of big data analytics is significantly aligned with electronic data interchange. The concept of the electronic Data Interchange (EDI) influences the business entity for swapping of business documents in a standard format from computer-to-computer. It enhances the capabilities of contemporary firms to manage the seamless exchanging information between two companies electronically in comparison to traditional form of communication. There have been several major advantages of EDI that include instant processing of information, high quality and improvised customer service, reduction in the paper work, high level of productivity, advanced tracing and expediting, facilitation of the cost efficiency, competitive benefits to handle strategic decisions, and advanced billing. Nguyen and et.al. (2018) stated that the application of EDI supply chain partners can avoid the emergence of the deformity along with the falsehood in supply and demand information by remodeling a range of contemporary technologies to support real time sharing of actual demand and supply information. It assists companies in creation of big data to attain different corporate objectives.
The investigation of Ben-Daya, Hassini and Bahroun (2019) determined that the increase in the popularity of barcode based system plays a critical role in the development of an efficient information sharing system within the supply chain management system. In the context of barcode-based supply chain management system, this code states the name of product along with other details such as name of manufacturer, product description and many more. Some other practical applications of barcode scanners are tracking the moving items like elements in PC assembly operations along with the assembling of automobiles in assembly plants. Therefore, the emergence of barcode enhances the reliability of big data analytics with collection and sharing of a variety of information (Meredith, and Shafer, 2019).
As per study of Govindan and Shukla (2018), the data warehousing is being termed as the important aspect of supply chain management. Data warehouse can be defined as a store that is comprising all the databases. It seems as the centralized database that is prolonged independently from the production system and supply chain management system of companies. For attainment of different business objectives, several companies maintain multiple databases. Instead of some particular business operations, it is established in relation to different informational subjects. The data presented in the data warehouses is influenced by the different time schedule and it could provide the easily accessibility of information. Historical data may also be accumulated in data warehouse (Schoenherr and Speier‐Pero, 2015).
The investigation Zhong and et.al., (2016). stated that the Enterprise Resource Planning (ERP) Tools provide a great support to several infrastructures in managing the information needs with consideration of different business needs. Some of the ERP tools include Baan, SAP, PeopleSoft, and others. ERP system has been emerged as the important processing tool for many companies. It supports companies in the collection of a variety of data and to minimize the manual activities and tasks aligned with the processing of financial data, inventory management and customer order information processing. Chen, Preston, and Swink (2015) argued that ERP system holds a high level of integration that is achieved with consideration of a systematic application of a single data model. It determines different standards for the development and sharing of information with reference to different business requirements.
The investigation of Ross (2015) determined that there have been several technologies involving in different parts of the world. However, the change in the business environment and expectations of consumers influences companies for becoming more prone to uncertain environment. In this running market, technology and big data analytics are playing a critical role in the information management and resource planning. In addition to that, the strategic along with different types of technological interventions in supply chain have been found very useful in predicting the buy and sell features of a company. The application of data analytics support businesses to use the potential of the internet with the clear vision, strong data planning and technical insight management (Tiwari, Wee and Daryanto, 2018). This is essential for better supply chain management and enhances the corporate competitiveness within highly competitive market conditions.
Evaluation of the Big Data Analytics and its Application in Contemporary Supply Chain Management Practices of Different Industries
The study of Hugos (2018) determined that the big data analytics is playing a key role in improving supply chain management in an efficient manner. It resolves several critical points at strategic, operational, and tactical levels. Big data is making an impact on all supply chain activities. It ranges from improving delivery times to identifying ways to reduce the communication gap between manufacturers and suppliers. Analytics reports enable decision-makers to achieve operational efficiency and monitor performance to improve productivity. Supply chain analytics augment data-driven decisions to reduce costs and improve service levels. In this regard, several technologies are playing a critical role in the strategic planning and information sharing. In this regard, the IoT-based systems are playing a critical role in influencing the requirements of supply chain management aligned with contemporary technologies (Marinagi, Trivellas and Sakas, 2014). Nakasumi (2017) determined that the blockchain has gained the first ranking to bring the improved visibility and transparency to supply chain processes. This is because the blockchain creates an immutable record of transactions, the technology is well situated to track the provenance of goods and establish trust in shared supplier information, especially when the parties have competing agendas and don’t particularly engender trust. Moreover, the blockchain supply chain has found very useful to manage the food related supply chain. The Walmart is running a pilot project with IBM’s Food Trust Solution to track for developing an efficient supply chain system to avoid deviation in business processes and managerial operations. In similar way, SAP and Bumble Bee Foods are collaborating to use the SAP cloud platform to manage the sustainable sourcing of food (Lotfi, Sahran and Zadeh, 2013). In similar way, the combination of different technologies such as AI, machine learning, predictive analytics, improve delivery times, proactively management of inventory. optimise strategic sourcing relationship and creating new customer experiences that influences the corporate satisfactions and boost sales.
As per the research of Vanpoucke, Vereecke and Muylle (2017), it found that big data analytics provides a great support in managing the future proofing of logistic with real-time analytics. The big UK supermarkets arguably lead the charge in this area, and it plays a critical role in simulating distribution scenarios with historical data to optimise different stock levels. Analytics tools are becoming instrumental in deciding different variables of product planning and information management. For example, Tesco considers a variety of approaches in the product planning and information management. Moreover, the efficient inventory management is being emerged as the crucial approach for reducing wastage of resource so as the forward-thinking businesses are increasingly using analytics to anticipate the consumer demand. This thing requires a detailed assessment of a diverse array of sources such as the existing operational systems, advertising responses, vehicle diagnostics, and even social media mentions (Waters and Rinsler, 2014). Moreover, the investigation of Addo-Tenkorang and Helo (2016) stated that predictive analytics allows retailers to manage different product offering. Behind the scenes, it would allow the retailers for the identification of the areas of high demand through which the business entity quickly capitalise the sales trends and provides assurance to facilitate different stocks towards different warehouses. For example, Nippon Paint used SAP Hana in-memory analysis software for analysing the contemporary trends in consumer behaviour with reference to their distinct requirement such as colours, and designers. On the other hand, Nguyen and et.al. (2018) stated that the SAP’s analytics tools connected different key data sources within the information management such sales, suppliers, and purchases. It influences the development, manufacturing, and delivery of different components to attain distinct business requirements.
As per the investigation of Ben-Daya, Hassini and Bahroun (2019), different global retail and manufacturing giants are also considering different robotic technologies for stimulating the automation processes within supply chain management. In this context, the Amazon has applied a variety of orange Kiva robots in its warehouses (Future-Proofing Logistics with Real-Time Analytics, 2021). These technologies are playing a critical role in the strategic planning and resource management. In the context of supply chain management, companies use different software and data base management systems to manage the seamless of supply of different products and services in an efficient manner. However, Govindan and Shukla (2018) argued that the technology is revolutionising the supply chain for supporting the contemporary business planning and resource management from smart shelves to cloud-powered data analytics that helps companies in capturing information from millions of data points. With consideration of data analytics, companies are able to manage different decision associated with warehouse management, inventory level assessment, selection of product portfolio, and others (Meredith, and Shafer, 2019). In the context of highly competitive market trends, data analytics system can not only support business strategies but also offer fresh insights about the new business opportunities through which an organisation can stimulate its growth and operational efficiency.
In this regard, a startup organisation of UK named Simfoni creates a machine learning analytics platform that could assist companies in managing different procurement and sourcing related task in the form of procurement specialists. This technology allows manufacturers in analysing and identifying different potential problems during the procurement process (6 Top Data Analytics Startups In Logistics & Supply Chain Management. 2021). In this regard, Zhong and et.al., (2016)stated that procurement is being considered as an important element of any business supply chain and it provides assurance to companies that all purchases are aligned with the required standards. In this context, the information acquired through big data analytics supported companies in development of different procurement standards and selection of suppliers. This is because companies are found several difficulties in managing a wide number of suppliers. an appropriate procurement system plays a critical role in managing a various price negotiation, inventory levels, along with the purchase orders to manage the massive strain on the traditional procurement systems. Chen, Preston, and Swink (2015) stated that data analytics plays a key role in the application of the machine learning so as companies can identify different trends and patterns in managing the procurement process. Moreover, a supplier’s performance has not always been guaranteed to remain the same so as the manufacturers have to apply appropriate standards and procurement practices to track the suppliers’ performance for supporting a smooth production process.
Benefits of Big Data Analytics in Supply Chain Management
According to study of Lotfi, Sahran and Zadeh (2013), the big data analytics supports companies in evaluating the consumer behaviour and usage pattern. This thing influences companies in restructure the product and service portfolio with reference to contemporary market trends. By considering the needs and expectations of people, companies would be able to restructure the procurement approach. For example, Leading telecom businesses are actively investing in big data analytics for analysing the usage patterns and different habits of their customers. The information gathered from the analytics report enables businesses for influencing the overall business revenue. In this regard, the Vodafone has been applied different tools of big data analytics for predicting the network growth with consideration of demand of telecon services (Waters and Rinsler, 2014). However, the research of Vanpoucke, Vereecke and Muylle (2017) determined that the big data helps firms in the form of enhanced inventory management. This is because the big box retailers along with the top online stores that are having a sizeable inventory are facing several challenges. Therefore, the big data analytics enables operation managers for assessing the minute-by-minute overview of different business operations and identification of the bottlenecks that could hamper the efficiency of the supply chain processes. Additionally, consumer trends also enable businesses for promoting the bestselling products along with optimization of the inventory level. Addo-Tenkorang and Helo (2016) stated that product traceability is being termed as the most critical element of the successful supply chain operations. In this regard, supply chain managers can easily trace different product and supplies with consideration of barcode scanners and attaching radio frequency identification devices. Moreover, the big data analytics enables businesses for gathering the accurate product information through which companies can influence the efficiency of their distribution cycle. Nguyen and et.al. (2018) argued that the improved traceability ensures the tracking of goods from production to retail in seamless manner so as the management of companies can manage a better coordination with supply chain stakeholders with reference to market demand of different goods (Ben-Daya, Hassini and Bahroun, 2019)
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