Pandemic Effect on the performance of manufacturing
Industry Insights | 10 june 2022 | Ratna Priya
Manufacturing’s critical societal and economic functions, particularly in the production of medical equipment, medications, and other important items, have been highlighted by the COVID-19 pandemic. At the same time, it has wreaked havoc on the industry’s finances, forcing cost-cutting and the construction of transparent, flexible supply chains to become top goals.
Manufacturing businesses should now, more than ever, collaborate to apply data and analytics to their operations to create value and achieve long-term success.
Increase in Productivity
Global manufacturing leaders are already using analytics to improve asset productivity, labor efficiency and net working capital optimization across the entire factory and supply chain.
Companies are looking for ways to boost asset productivity. TOSHIBA, for example, collaborates with a leading water treatment equipment manufacturer to improve its quality and durability. They optimize scheduling and input of essential chemicals based on sensor data provided by the plant operator. This then allowed the operator to minimize expenditures while improving their environmental impact.
Businesses are also taking advantage of supply chain opportunities. Through a platform supplied by Surgere, supply chain actors communicate data using a common set of processes and transactions to enable real-time accurate tracking of inventory and other assets. This visibility not only improves productivity, but it also improves supply chain resilience by recognizing supply chain flaws.
Customer Experience
Manufacturers also employ data-and-analytics collaborations to improve the customer experience, such as by allowing them to verify the provenance of items and delivering more tailored products.
Personalized cancer treatments are one possibility.Â
We can take reference of Johnson & Johnson, that collaborates with a large network of healthcare providers, suppliers, and laboratories to enhance cancer patient outcomes. To personalize the treatment of individual tumours, all ecosystem actors share patient data. Seamless end to end data flow between all players hence enables treatment using patients own cells.Â
Data and analytics collaborations assist society, particularly in terms of sustainability. When businesses and governments work together to solve problems like these, they rely on a shared data ecosystem.
On its own, a single producer is unlikely to be able to fully realize these benefits. Companies require a comprehensive, end-to-end data set, such as that which covers the full supply chain or provides visibility into the entire process. They also require a large enough data set to generate valid insights, such as statistically significant correlations for artificial intelligence training.
As a result, data from external partners frequently plays an important role. As a result, manufacturers must create an ecosystem in which enterprises may interact to share data. For mutual understanding and collaboration, participants require a common data ecosystem that provides a single source of truth.
RPA
CSPs serve many clients who conduct millions of daily transactions, all of which are vulnerable to human mistake. RPA (Robotic Process Automation) is a type of AI-based business process automation technology. RPA can improve telecommunications efficiency by helping telcos to manage their back-office operations and enormous quantities of repetitive and rules-based actions more easily. RPA frees up CSP personnel for more value-add work by automating complicated, labor-intensive, and time-consuming operations like billing, data entry, workforce management, and order fulfilment. According to a Deloitte survey, 40% of Telecom, Media, and Technology executives said cognitive technologies have provided “significant” benefits, with 25% investing $10 million or more. Within the next three years, more than three-quarters of respondents expect cognitive computing to “significantly transform” their businesses.
 Future Analysis About AI In Telecom Industry
In the telecommunications industry, artificial intelligence technologies are progressively assisting CSPs in managing, optimising, and maintaining not only infrastructure but also customer support operations. AI has touched the telecom business in a variety of ways, including network optimization, predictive maintenance, virtual assistants, and robotic process automation (RPA). As Big Data tools and applications become more widely available and powerful, AI is expected to thrive in this highly competitive industry.
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