Negotiation Automation Platform
In actual business situations , numerous complex types of coordination ( negotiations ) may be required . These scenarios are represented as patterns in the standard , with semantic protocols and data definitions specified for each pattern ( Figure 3-2 ). In these complex negotiations , multiple negotiations can be handled as one combined negotiation thread . Examples include nested negotiation ( where one negotiation includes other negotiations ), competitive negotiation ( which involves choosing one of several negotiations ), and sync / async negotiation ( waiting for other negotiation results or not waiting for other negotiation results ).
Figure
3-2 : Negotiation patterns .
Additionally , three scenarios are referenced as use cases : ocean shipping , demand forecasting , and air cargo are explained in the implementation guidelines as part of the standardization activities .
3.3 ENTERPRISE SYSTEM
As previously mentioned , to generate appropriate offers and determine acceptance , negotiation AI requires access to internal company data , such as inventory levels , available resources , production capacity , production plans , target profits , and safety margins .
Typically , this information is stored in internal systems such as ERP ( Enterprise Resource Planning Systems ), PDM ( Product Data Management Systems ), and WMS ( Warehouse Management Systems ) and should be transferred to the AI platform accordingly . Furthermore , the results of the negotiations will be sent back to these systems via API as updates to planning information . Integration between the negotiation AI and internal systems , or collaboration through data lakes , is necessary to facilitate this transfer and write-back process .
Journal of Innovation 63