“The Triniti team helped us take our first step in the Oracle Cloud journey with Oracle Demand Cloud. We also continue to benefit from ‘Triniti’s Cloud integration’ for automation and ‘Demand Management Cloud Audit’ to complement the Oracle demand management application”
- Dan Bolduc, Sr. Director - IT Enterprise Systems, AngioDynamics
Watch the video to know more about Triniti Audit Solution for Oracle Demand Management Cloud
Collaborative demand planning typically uses a statistical forecast as the starting point. The Sales and Marketing teams review this forecast and make adjustments based on customers, channel partners, and field sales inputs. Since the sales team is held accountable for achieving the numbers, its information generally trumps the statistical forecast.
In such a collaborative demand planning process, trust is paramount.
Being able to determine who changed what and when is critical to establishing trust in numbers. The collaborators can change numbers at any level of aggregation (e.g., reduce the forecast for all customers in a region by 10%). It exacerbates the problem.
In Oracle Demand Management Cloud out-of-box audit report, the above information is not accessible in a useful way.
As seen in the screenshot below, one can only search by Measure, User, and Date. Only after going into the detail-record can one know the dimension values such as SKU, Customer, Month, Region.
However, the actual use cases for this are centered around answering questions such as -
Was there any change to the forecast for an Item?
Who changed the December forecast for the EMEA geography?
Did we update the forecast for the automotive customer who is going out of business?
The trust in and the system’s adoption relies heavily on the user’s ability to get answers to such questions quickly and efficiently.
Triniti’s DM Cloud plug-in helps the users achieve that. It utilizes the audit information generated by Oracle Demand Management Cloud and presents it in a manner that is quickly consumed by the users.
Key Features:
Users can slice and dice data using any dimension hierarchy level value
Plug-in automatically incorporates all dimensions, hierarchies, and level values specific to the particular implementation
Generic Text Search helps refine the results further (e.g., TableFilters)
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