Manufacturing Analytics Market to see huge growth by 2030
Valuing at US$2.5bn in 2019, the US manufacturing market is expected to reach a revenue value of US$14.1bn by 2030, a compound annual growth rate (CAGR) of 17.6% during the period of 2020-2030). Due to the likes of prominent industry players such as Salesforce, IBM , SAP, Oracle, SAS Institute and General Electric Company, the US manufacturing analytics market has become highly competitive market.
Image source: Prescient and Strategic Intelligence
Prescient and Strategic Intelligence attributes this growth to the wide-scale adoption of analytics software within the manufacturing industry, which is being harnessed for quality management, inventory management, asset management and predictive maintenance. As a result, the industry is seeing a reduction in equipment downtime and improved operational efficiency.
To gain competitive advantage within the industry and improve business operations, manufacturers in the US have increased their adoption of virtualisation software which separates applications from physical software, allowing multiple virtual workloads to be handled simultaneously.
In addition to this, the technology allows for ease when it comes to the initial set up, and reduces the expenditure of physical hardware, as well as addressing lifecycle management issues.
This adoption comes as part of the industries increased awareness of the role supply chain management plays in reducing operational costs, improving customer satisfaction, increasing visibility and increasing control over inventories.
Supply chain management also plays an important role in addressing the requirements of sales and operations planning, product lifecycle management, manufacturing and logistics optimization, business intelligence, radio-frequency identification (RFID), business intelligence, raw material procurement, and network and inventory optimisation.
Ultimately the connectivity provided by analytics enables manufacturing organisations to improve and enhance communications across the supply chain.
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