PCM features: Profit Curves

Do you know what can be achieved with the tools provided when subscribing to one or more Oracle Cloud Services?

Lack of familiarity with certain features included in the Cloud subscription represents an unmeasured cost as well as a missed opportunity to gain much needed insight without having to employ further monetary resources.

Profitability and Cost Management applications – whether built for Fully allocated P&L Solutions, Transfer Pricing, Shared Services Allocations or Customer/Product Profitability – have out of the box reporting capabilities available via the Intelligence menu that offer insight into allocation models with reduced effort. The purpose of my blog series is to explore how we can setup, configure and use such features and fully leverage the functionality that is included in our Oracle Cloud subscription cost.

The 6 Intelligence menu options are:

 1. Analysis Views

 2.Scatter Analysis

 3.Profit Curves

 4.Traceability

 5.Queries

 6.Key Performance Indicators

The contents of this blog are based on the standard Bikes (BkML30) demo application, so that it can be easily followed in a step by step manner without having to go through an app setup from scratch.

Profit Curves – Overview

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 PCM. 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 and 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 a high level of resources and not justifying such cost for the level of profit incurred from their engagement.

– potential costing issues of over or under costing products.  For example, over burdening a product or product line inappropriately.  A cost study should be performed to determine the appropriate allocation.

– pricing

Information represented through a Profit Curve can be enlightening, helping to focus on the 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 in strategic decision making, especially when dealing with competing projects and limited resources.

A Profit Curve was an eye opener during one of my recent PCM implementations. A company’s staple product, advocated as being their best and most profitable, turned out to be, in fact, the least profitable. This discovery was made 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

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 and Analysis View here.

Analysis Views can contain multiple Measures/ Accounts references; however, the Profit Curve using the Views will analyze and display 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.

Here is an example of a Profit Curve.

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

From a Quarter to Date perspective, which is the Period selected at the top of the View, this Profit Curve indicates that all customers are profitable.  That may raise questions whether the overhead is allocated appropriately or an even spread is used, therefore skewing the results.

Note: Data in the BksML30 model at the time this Profit Curve was generated, had been 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 they are 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 taking money away from the company.  While these customers/products/channels can’t always be eliminated, they can be watched and reviewed for pricing changes. 

Using a PCM Profit Curve

There are options to filter data by the POV dimension, Period, or by metric tied to the Customers. For example, we could exclude from the analysis any Customers with Operating Expenses which 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 metric 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 employed in the PCM model), therefore, focusing on Customers who are generating losses.

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

100% of the customers analyzed based on our 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

-1,148,670 USD.

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, next to the “Add Filter” menu. These selections are predefined in the Analysis View that was employed during the creation of the Profit Curve and you will not be able to modify them unless you modify the underlying View.

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 such report from the Analysis Links menu under the Category section.

A report in the following format will be loaded on the screen, displaying the Customer detail records along with all the other settings defined in the Analysis View.

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.

Note: When launching this report, the 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, the users will have to make this update on the Filter /POV area and followed by the a data Refresh..

We can make changes to the Analysis View to add further details; for example: Cost of Goods.

For the 886 customers that are not profitable, we can dive deeper into their Cost of Goods data, Operating Expenses, or analyze whether the products sold are so heavily discounted that they are no longer generating 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 PCM application, in the underlying Analysis View displayed in the adjacent Pie Chart.

The available categories to display the pie chart data for the Profit Curve chosen are the following:

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

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:

The pie chart reveals that the South East 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.

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.

The 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 the filter criteria.

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

Conclusion on OOTB features: Profit Curves

If you have been following the OOTB features with PCM series, you should be aware of the dashboarding opportunities you have at your disposal with the PCM subscription. The listing of PCMC 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 tip of the end users’ fingers, no longer involving complex requirements gathering processes and iterating between different display options. The PCM users have the capability to build their own dashboards and design them as they see fit. As a result, IT staff is no longer burdened with reporting requests or artifact migration between environments.

This blog post is one in a series of many. 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 and reach out with suggestions @AlecsEPM if there is a PCM related topic that you would like to see covered in more depth.

By Alecs Mlynarzek

PCMCS Product Manager at Alithya 

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