Out-of-the-Box Features: Profitability and Cost Management Cloud Service (PCMCS) – Intelligence and Dashboarding: Profit Curves

Welcome back to this series of blog posts to cover out-of-the-box (OOTB) features of Profitability and Cost management Cloud Service (PCMCS). There is a need within the Oracle Cloud client community to discover what can be achieved with the tools provided when subscribing to one or more Oracle Cloud Services. A lack of awareness of the features included with your subscription is an unmeasured cost and a missed opportunity to gain much needed insight without further spend.

PCMCS applications – whether built for Fully Allocated P&L Solutions, Transfer Pricing, Shared Services Allocations or Customer/Product Profitability – have OOTB reporting capabilities available via the Intelligence menu that offer insight into allocation models with reduced effort. Here, we’ll explore how to set up, configure, and use such features and fully leverage the functionality that is included in the Oracle Cloud subscription cost.

The order in which I am covering the OOTB features is directly related to the Intelligence menu options available in PCMCS.  The 6 menu options are:

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 1  1.  Analysis Views (learn how to create, customize, and use them here)

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 2  2.  Scatter Analysis (discover how to set up and configure them here)

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 3  3.  Profit Curves (this blog post focuses on Profit Curves)

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 4  4.  Traceability

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 5  5.  Queries

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 6  6.  Key Performance Indicators

The content of this blog is based on the standard Bikes (BkML30) demo application, so you can follow the step-by-step information without having to go through an app setup from scratch. You can load and deploy this application directly from your PCMCS instance through a couple of clicks via the Application menu using the + / Create button.

 

Profit Curves – What Are They?

If you are looking for a graphical representation for the concentration of your profit by either Customer, Products, Channels, or Funds, look no further than the Profit Curves section in PCMCS. Profit Curves, also referred to as Whale Curves, are used to identify which cluster of Customers, Channels, or Products generate the most profit. Profit Curves display a graphical representation of the relationship between economic profit and the quantity of output sold.

The details of the profit or net income split by unit/service/customer displayed in a Profit Curve identify issues with:

  • expansion of a production line
  • breadth of services that may have a negative impact on profit
  • onerous clients consuming numerous resources without justifying the cost for the profit gained from their engagement
  • potential costing issues of “over” or “under” costing products (for example, overburdening a product or product line inappropriately);  a cost study should be performed to determine the appropriate allocation
  • pricing

Information illustrated with a Profit Curve can be enlightening and help to put the focus on specific customers, products, or channels where the greatest profit attention is needed, indicating situations where a few products, services, or clients create enough profit to maintain the rest of the company’s offering. Profit Curves are key to strategic decision making, especially when dealing with competing projects and limited resources.

During one of my recent PCMCS implementations, a Profit Curve proved valuable when the client’s staple product, advocated as being its best and most profitable, was discovered to be the least profitable after the implementation of an accurate cost allocation methodology in PCM!

The easy-to-follow Profit Curve provides the foundational insight needed to rapidly shift gears across product lines, ensuring alignment of management decisions backed up by real information.

 

Building a Profit Curve

There are several Profit Curves available in the Demo application BksML30. In order to build a Profit Curve, there must be a corresponding Analysis View that can be leveraged as the basis for data selection. See a step-by-step guide on how to build an Analysis View here.

Analysis Views can contain multiple references to Measures and/or Accounts; however, the Profit Curve using the Views analyzes and displays only one measure at a time.  Users can choose to define names for the X and Y axis to add clarity to the Profit Curve information consumers.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 1.png

Here is an example of a Profit Curve:

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 2

The curve displays a listing of Net Income generated by Customer.

From a Quarter-to-Date perspective (the Period selected at the top of the View), this Profit Curve indicates that all customers are profitable.  That may raise questions about whether or not the overhead is allocated appropriately or an even spread is used, thus skewing the results.

Note: Data in the BksML30 model at the time this Profit Curve was generated was calculated only for January, confirming the Profit Curve display, as the profit by customer distribution was evened out at Quarter-to-Date level.

The details of each customer/product/channel/segment and how much net income each is generating can be reviewed in the Category Analysis section. From a cost management and process improvement point of view, the right side is the most important.  This side generally represents customers/products/channels with a negative profit or that cost the company money.  While these customers/products/channels can’t always be eliminated, they can be watched and reviewed for pricing changes.

Using a PCMCS Profit Curve

There are options to filter data by the POV dimension, Period, or by metrics tied to Customers. For example, we can exclude from the analysis any Customers with Operating Expenses that are considered marginal. After defining the required filters, we can refresh the Profit Curve and review the newly generated pie charts.  Filters can be added to all available metrics and can be stacked up to generate any custom report.

Below is an example of the same “All Customers” Profit curve, limited to January and with a selection of all Customers who had a Net Income smaller than 1 positive unit (USD or the currency defined in the PCM model) thereby highlighting Customers creating losses.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 3

In the Details section of the Profit Curve, there is a count of 886 customers with a Net Income smaller than 1.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 4100% of the customers analyzed based on the specified criteria are unprofitable. The “Actual Profit” in this Details section can be translated into “Actual Loss” as the total accumulated value across the 886 customers is US$ -1,148,670.

If there are doubts regarding the data intersection for the remaining dimensions in the PCM model such as Product or Entity, we can analyze related information through the configuration icon located next to the “Add Filter” menu. These selections are predefined in the Analysis View that was used during the creation of the Profit Curve, and you will not be able to modify them unless you modify the underlying View.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 5

If questions are raised during the analysis on the Profit Curve screen and a list of details by Customer is requested, we have the option to launch a report from the “Analysis Links” menu under the Category section.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 6

A report in the following format will be generated to display the Customer detail records along with all the other settings defined in the Analysis View.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 7

This report can be exported in .xls format (“Export to Excel” option), and it represents a base level data dump report, in column format, containing multiple generations and references to attribute dimensions.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 8

Note: When launching this report, users must check that the parameters have transitioned correctly from the previous screen. The Period parameter, which is saved to be Quarter-to-Date on the original Analysis View used in the Profit Curve diagram, will override any other selection made during run time analysis. If there is a need to revert to a specific month before launching the Export to Excel, users will have to make this update on the Filter /POV area and perform a data Refresh.

We can make changes to the Analysis View to add further details (for example, Cost of Goods).

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 9

For the 886 customers that are not profitable, we can dive deeper into their Cost of Goods data, Operating Expenses, or analyze whether or not the products sold are so heavily discounted that they no longer generate a margin.

 

Pie Charts Related to PCM Profit Curves

 

We can further analyze the resulting Profit Curve data by using the available predefined categories tied to the Attribute dimensions available in the PCMCS application, in the underlying Analysis View displayed in the adjacent Pie Chart.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 10

The available categories to display the Pie Chart data for the Profit Curve chosen are the following:

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 11

When selecting the Region category/attribute, we learn that the Southeast area contains 26,07% of all the unprofitable customers.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 12

If we change the Focus of the Category to be on Top 10% most unprofitable customers by Amount vs. All Customers/Number of Customers, the following information is displayed:

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 13
Alex Mlynarzek - Profit Curves - 4-18-19 - Image 14

The Pie Chart reveals that the Southeast region has the highest number of unprofitable customers both by Number of Customers as well as by Total Amount/Loss.

When adding a filter based on Customer Generation 3 which distinguishes between Department Stores and Specialty Retailers, it looks like 87.64% of the Top 10% most unprofitable customers are from Department Stores.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 15

A look at the 4th generation in the Customer dimension where we can analyze the split of the losses at Customer level indicates that one store is responsible with 65.17% of all losses within the top 10% most unprofitable Customers.

Alex Mlynarzek - Profit Curves - 4-18-19 - Image 16The Pie Chart is the only artifact that is refreshed based on the selections of the Category Analysis menu while the Profit Curve remains constant based on the selections in the POV and filter criteria.

While all users of PCMCS can generate/launch Profit Curve reports and export their associated Analysis Views, in order to create and set up a Profit Curve report, the PCMCS administrator must update the requesting user’s permissions. As with all Intelligence screens within PCMCS, the Viewer role allows the use of these artifacts, not its creation or setup.

Concluding Thoughts About OOTB Features: Profit Curves

If you have been following the posts in this blog series, you’ve become aware of the dashboarding opportunities at your disposal with a PCM subscription. The listing of PCMCS OOTB features is a good starting point for comparing any other profitability and cost management tools on the market, regardless of vendor and technology employed.

Creating insightful dashboards is now at the tips of end users’ fingers, no longer involving complex requirements gathering processes and iterating between different display options. PCMCS users have the ability to build and customize their own dashboards. As a result, IT staff is no longer burdened with reporting requests or artifact migration between environments.

Subscribe to our mailing list for updates on the next blog post covering Traceability, Queries, and KPIs. Don’t think the PCMCS OOTB features blog series will stop at the Intelligence menu options! There is more to come on Model Validation, System Reports used for maintenance and troubleshooting, Integration with Cloud Data Management, and the Application Backup and Restore functionality. All this and more will be covered in future blog posts, so watch this space for updates.  If there is a PCMCS-related topic that you would like to see covered in more depth, email us at infosolutions@alithya.com.

Out-of-the-Box Features: Profitability and Cost Management Cloud Service (PCMCS) – Intelligence and Dashboarding: Analysis Views and Scatter Analysis

PCMCS Out-of-the-Box (OOTB) Features:  2. Intelligence and Dashboarding – Analysis Views and Scatter Analysis

Two teams of consultants with similar amounts of experience and prestige guarantee that they can perform an application implementation to the highest quality: one at a higher cost, but shorter timeframe; and the other at a lower cost, but in a longer timeframe?  All other considerations being equal, should I save money, or should I save time?

A few days ago, I released my first blog post on PCMCS, covering Rule Balancing reports usage and customization. This post builds on that first post to cover intelligence capabilities, some of which are only available in the Cloud version of the PCM software.

There are 6 menu options when accessing the Intelligence menu within PCMCS.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 1  1.  Analysis Views

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 2  2.  Scatter Analysis

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 3  3.  Profit Curves

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 4  4.  Traceability

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 5  5.  Queries

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 6  6.  Key Performance Indicators

This post covers the first two menu options to explain how to set up Analysis Views and how to use Scatter Analysis.

Analysis Views

Analysis Views are the first set of reports available to end users within the PCMCS user interface.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 7

These views represent a way to predefine and save intersections of members for future review.  The selections within Analysis Views are open to all dimensions within the PCMCS application at various levels within the hierarchies. This is the first step you need to take towards building or defining a dashboard for your PCMCS application.

If you cannot create or edit an analysis view, then you need to reach out to your PCMCS administrator in order to review and adjust your security settings.

The example Analysis Views for this post are based on the “Demo Bikes” application that can be deployed with a few clicks in your PCMCS instance BksML30.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 8

A data slice is a combination of rows and columns along with the page selection, which, in this case, is the Period dimension.

Any dimension that is not specified in any of the 3 areas (row, column, page) will be read at top level and will be displayed in the settings menu.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 9

The Add Filter section allows you to filter the columns based on specific numerical values. In this case, the columns are represented by the Product dimension selections.

To create an analysis view, click on the plus (+) sign on the main menu. The three tabs displayed will allow you to define a name and description as well as the setup for row and column dimensions. You cannot select more than a dimension for either rows or columns.

Within the Row dimension selection, you can leverage different formulas applicable to the hierarchies within PCM such as Children of member, Member and children, Level 0 descendants, etc.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 10

Columns do not have options for member formulas beyond the usage of User preferences.

The row dimension will allow you to display further information such as generation or level details. For example, for the Product dimension, we can display the generation 3 and 4 information alongside the level 0 members, allowing us to expand our analysis to different product categories, or types.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 11

Selecting new members within the Analysis Views will not impact the original data definition. If you choose to display data for any month other than the one that was setup and saved in the Analysis view, you can do so because the Page parameter is open to end user modifications. If; however, you want to update and store a selection change within the analysis view, you must perform such update via the Edit menu instead of simply selecting a new parameter on the screen in view mode.

You may need to utilize the concept of period ranges when using Analysis Views in order to dynamically reference specific members of your Period dimension.

Defining a current period for the application is mandatory in order to be able to create formulas dependent on time. This action is available via the Application menu by selecting the Edit application option and navigating to the tab called Dimension settings. Here is where you can define the current Period and the Current Year for your PCMCS application.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 13

These settings will be applied when using the “Single…” or “Current” selection options within Analysis Views. Single (-1) Level 0 selection represents, in this case, the month of May, since the current Period selection for the PCMCS application is June. The Single (-1) Level 1 selections return Q1, since June is in Q2.

Scatter Analysis

Scatter Analysis graphs will compare one member’s values against another member’s values. The two members selected must be within the same dimension. Your PCMCS Demo application may not have any sample Scatter Analysis graphs. However, you can create one by leveraging the Analysis Views at your disposal.

You can launch Analysis Views from within Scatter graphs.

Note that saved Scatter Analysis cannot be reused or referenced in dashboards. You should use this section to create graphs for ad-hoc use outside of the dashboarding capability.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 14

If you need to include Scatter Analysis within your dashboards, you will have a corresponding menu item that allows you to create dashboards within the list of available items.

You can select an existing Analysis view, but you must reselect your X-axis and Y-axis dimension references.

Alex Mlynarzek - Analysis Views and Scatter Analysis - 2-28-19 - Image 15

Conclusion:  PCMCS Intelligence – Analysis Views and Scatter Analysis

While there are many alternative reporting solutions to use in conjunction with PCMCS applications, assuming that both time and money are of essence in any project implementation, it is safe to conclude that using the PCMCS OOTB reporting features would be cost effective as well as efficient. The Intelligence screens shared in this post are included in the PCMCS subscription cost, and any end user of a PCMCS application with the right level of access can take charge and build the desired reports, saving end users in a location accessible to their peers while spending no time in iterations of reporting requirements and data validations.

The PCMCS OOTB reporting features support not only troubleshooting, but also detailed analysis and reporting within one screen.  Such capabilities should not be ignored as they will surely add meaningful insight into finance teams’ day-to-day use of PCMCS.

If you need advice and guidance on how to leverage the PCMCS reporting capabilities for existing or future applications, reach out to our team of PCMCS experts at infosolutions@alithya.com.

The remaining intelligence menus will be covered in subsequent posts over the next few weeks. If you are interested in receiving notifications of such posts, subscribe to notifications.

OBIEE and Essbase – Defining OLAP Integration

In this second part of our OBIEE series, the integration between OBIEE and Essbase is a seamless transition from our OLAP cube to the OBIEE suite managed by using OBIEE’s Administration Tool.

OBIEE Administration Tool view

OBIEE Administration Tool view

This Administration tool has been designed with wizards, utilities, and interface design elements to help administrators work more efficiently.

Essbase test outline

Essbase test outline

Using an existing Essbase outline called ‘test’, this outline can be used to import an OLAP connection to OBIEE.

From the Administration Tool, select

File | Import | from Multi-dimensional

 
Enter the provider type, Essbase Server name, and its login credentials. The physical layer table, connection pooling, etc. will be automated and established once the import completes. You can also manually set each individual component in the physical layer if you want this level of control.

obiee-import

obiee-import-login

 

When the Physical layer has been established, simply drag and drop the folder of your Essbase outline from the Physical layer to the Business Model and Mapping layer to define a mapping between the business model and the physical layer schemas.

Physical Layer in Administration Tool

Physical Layer in Administration Tool

 

Once the business model mapping has been established, move the business model to the Presentation layer to make it available for user views.

Business Model & Mapping Layer in Administration Tool

Business Model & Mapping Layer in Administration Tool

 

This Presentation layer allows the Administration tool to present customized views of the business model to users. The business models can be managed in this presentation layer by removing unwanted or unneeded columns, restrict certain columns from view, or maybe rename a column to a more user-friendly name.

Presentation Layer in Administration Tool

Presentation Layer in Administration Tool

 

Once adjustments to column views have been completed and ready in the presentation layer, it can be made available in the Subject Areas for users to develop reports using the Answers component of OBIEE.

Subject Areas in OBIEE Answers

OBIEE Subject Area in the Answers component of OBIEE

 

So the three layers within the OBIEE Administration tool are defined as follows:

  • Physical layer – Represents the physical structure of the data sources to which the Oracle BI Server submits queries. This layer is displayed in the right pane of the Administration Tool.
  • Business Model and Mapping layer – Represents the logical structure of the information in the repository. The business models contain logical columns arranged in logical tables, logical joins, and dimensional hierarchy definitions. This layer also contains the mappings from the logical columns to the source data in the Physical layer. It is displayed in the middle pane of the Administration Tool.
  • Presentation layer – Represents the presentation structure of the repository. This layer allows you to present a view different from the Business Model and Mapping layer to users. It is displayed in the left pane of the Administration Tool.

 

Some of the features of the Administration tool make management of metadata and data much less complicated. The change management feature makes it easy to change multiple object names, text, case, and adding prefixes and suffixes. This allows for drag and drop capabilities from the physical to the business model layer.

Organization of metadata is straightforward using a feature called metadata administration. This feature grants users the ability to create folders to manage dimension tables and hierarchies.

The multi-user collaboration feature regulates the off-line/on-line modes for read only or to take effect immediately. This enables metadata repositories to be checked out or checked in and authorizes multiple administrators to work on a repository concurrently.

The Export/Import feature supports the export and import of metadata to move systems from staging to production and provide documentation.

Defining how OLAP is presented to OBIEE has been explained in basic format within this blog article but readers should know that this Administration Tool is much more powerful and can allow for more focused control within each of its layer process managing metadata and data. It is integrated and is flexible and its goal is to help move disparate source data to the OBIEE suite.

The end result can be a visual dashboard that makes sense of data utilizing charts, graphs, stop lighting, embedded images, tickers, etc. to organize and present data in a manner your audience will embrace and use.

Dashboard created with Answers from Essbase test outline

Dashboard created with Answers from Essbase test outline

 

This concludes part 2 of the OBIEE & Essbase integration. Keep an eye out for my next article where I’ll review RDBMS integrated with OBIEE and how it can be used in conjunction with Essbase in Answers reporting.