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Enterprise Mgt with Blockchain
(Mgt Science + Data Science + eERP + Automatic Execution)

8Manage extends ERP type of automations to business straight-thru driven by data science intelligence. Specifically, 8Manage extends:

  • Internal team to multiparty (e.g., clients, suppliers) straight-thru processing
  • Assembly line to any form of white collar project-based controlled execution
  • Mgt by documents to mgt by digital rules, corporate memory and audit trails
  • Human invocation of isolated ERP functions to smart contract driven automatic execution of integrated CRM, SPM, eCommerce/O2O, PPM, HCM, OA, Finance and BI functions
  • Manual reports to automatic real-time forecast, decision support or business execution

In the past few decades, business managers’ intelligence was much weakened by the delayed, fragmented and massaged information that made available to them because of the limitations on computing power and old software design (data connectivity is limited by module boundary). 8Manage is aimed at using the modern technology and design to provide automatic and real-time business straight-thru based on data science intelligence and digital rule control.



Enterprise Automation, Digitalization & Integrated Mgt

Most enterprises are at the lowest 2 levels of automation, digitization and integrated mgt today and not prepare themselves well for the digital era. 8Manage would allow them to move up to the 3rd, 4th or 5th level of automation, digitalization and integrated mgt.

When an enterprise reaches the 3rd level of automation, digitalization and integrated mgt, it would achieve the highest level of decision support and managerial efficiency for its business operations. When an enterprise reaches the 4th level, it would be able to make optimized decisions and achieve the highest level of business operations efficiency across its clients and business partners . When an enterprise reaches the 5th level, it would be able to collect micro behavior statistics of its employees, clients and business partners and use the data for deep learning for recognizing and predicting patterns and automatically selecting or devising methods for optimized results.

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