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Use Case Situation Description
Improving spot margin performance through analytics KPIs in the process industry offers several advantages over margin/volume forecast methods. It enhances real-time decision-making precision, identifies profit opportunities, and minimizes revenue leakage. By leveraging data-driven insights, it optimizes production, reduces operational costs, and mitigates risks. This approach ensures proactive adjustments, maximizes profitability, and fosters competitive edge in a dynamic market landscape.
Prescriptive Design Requirements
Business Objective
Spot pricing is typically updated on a frequent basis (weekly, monthly, quarterly), or ad-hoc as market, competitive, and internal annual business plans require. The goal is to improve short-term spot pricing (non-contract customers) based on specific monthly margin, revenue, and volume goals (per the annual operating plan / forecast). They will do this using real-time pricing tools that allow for frequent pricing changes due to regular cost and market index changes, margin expansion goals, and other competitive forces.
Complication:
High frequency of underlying market cost changes
Limited time to update data, complete models, review, and react
Limited visibility into impact on margin and volume due to market cost changes
Limited visibility into underperformance and recommendation for price improvements
Capability Needed:
Detailed view of spot revenue, margin, volume performance over time by region/segment
Trending detail on spot revenue, margin, volume performance against financial plan
Force ranking by category of high/low performing customers and products
Benefit:
Reduce possibility of margin compression by failing to pass on market cost changes
Increase margin to stay in alignment with financial planning
Increased alignment between pricing and sales teams
User Stories
This is a list of all the usr stories associated with this use case.
Data Requirements
NOTE: There have been no data requirements identified at this time
Out-of-Scope
NOTE: There have been no out-of-scope functions or features identified at this time
Solution Design
The solution aims to enhance spot margin performance in the process industry by leveraging analytics KPIs. Using data-driven insights, real-time decision-making precision can be improved, profit opportunities can be identified, and revenue leakage can be minimized while optimizing production, reducing operational costs, and mitigating risks. Pricing and sales teams in the chemical industry can achieve increased alignment and reduce the possibility of margin compression with the help of detailed visibility into spot revenue, margin, and volume performance over time, as well as trending details against the financial plan. Here is how this can be achieved in Pricefx.
o Filters will be built based on the Transaction history datamart
o Total Revenue: Total Revenue from Start Date to End Date
o Total Volume: Total Volume from Start Date to End Date
o Total Margin: Total Margin from Start Date to End Date
o Revenue Trend YTD: (YTD Current year – YTD Previous year) / YTD Previous year
o Margin Trend YTD: (YTD Current year – YTD Previous year) / YTD Previous year
o Volume Trend YTD: (YTD Current year – YTD Previous year) / YTD Previous year
Detailed version, grouped by on industry, region, product group
o Industry: Customer Industry
o Region: Customer region
o Product Group: Product group
o Total Revenue: Total revenue from Start Date to End Date
o Total Volume: Total Volume from Start Date to End Date
o Total Margin: Total Margin from Start Date to End Date
o Revenue Trend YTD: (YTD Current year – YTD Previous year) / YTD Previous year
o Margin Trend YTD: (YTD Current year – YTD Previous year) / YTD Previous year
o Volume Trend YTD: (YTD Current year – YTD Previous year) / YTD Previous year
Histogram comparing previous 12 months (from sales history) vs next 12 months (from the forecast DM)
o Use Highcharts to build the histogram
o Data are taken from Start Date to End Date from Sales History datamart and Forecast datamart
o Margin is calculated by month
o cost = revenue - Margin
o Margin % = (revenue – cost) / revenue
o Use Highcharts to build the histogram
o Data are taken from Sales History datamart
o The calculations are the same for Revenue, Margin and Volume. Below is an example for revenue
Group data by customer Id
Get max Revenue across customers
Get min Revenue across customers
high = max - ((max - min) / 3)
low = min + ((max - min) / 3)
Categorize the customers into the buckets
Calculate total revenue
Calculate revenue contribution
o Customer Month Guidelines is an embedded portlet will be triggered by Month Guidelines By Region
o When user select a Segment on Month Guidelines By Region, Customer Month Guidelines will show data for the selected segment
o Data are taken from Sales History and Forecast datamart
o The Guidelines are calculated for the most recent full month
o Calculation details
Volume: Volume of the calculated month
Target CVM: (margin – rebates) / volume
Actual Margin: margin of the calculated month
Forecast margin: forecast margin of the calculated month
