Delivered projects.
Documented outcomes.
Twelve engagements across Oracle EPM, NetSuite Planning and Budgeting, and Vena Solutions. Each one delivered with a focus on performance, adoption, and long-term value for the finance team.
Full planning suite in NetSuite Planning and Budgeting
An e-commerce company moves from spreadsheet planning to a connected platform covering workforce, financials, balance sheet, cash flow, and capital planning.
Context
A growing e-commerce company on NetSuite ERP was ready to bring planning into a dedicated platform. The finance team had been running budgeting and forecasting in Excel, which had served well in earlier stages but was becoming harder to maintain as the business scaled. Workforce planning, CAPEX, and KPI reporting sat in separate workbooks. Balance Sheet and Cash Flow planning had not yet been formalized. The team wanted a connected environment that covered the full picture without leaving the NetSuite ecosystem.
What I delivered
A full NSPB implementation covering the company's planning needs end-to-end.
- Workforce Planning with driver-based headcount and compensation modeling
- Income Statement planning tied to operational drivers
- Balance Sheet planning with working capital schedules tied to revenue and payable cycles
- Cash Flow planning built via the indirect method with direct method reconciliation
- CAPEX planning with depreciation flowing through both Income Statement and Balance Sheet
- KPI reporting layer integrated across all five models
- Tight integration with NetSuite ERP for actuals
- Training and enablement so the finance team could own the platform post go-live
Balance Sheet and Cash Flow planning were new for the organization, so I worked closely with the finance team to design the driver logic from first principles and validate it against their historical actuals.
Outcome
The platform went live on schedule and adoption across the finance team was strong from day one. Balance Sheet and Cash Flow planning are now regular parts of the monthly cycle, which was a new capability for the organization. The team actively uses all five planning models in their monthly and quarterly processes.
Oracle EPM reengineering for an energy operator
Full reimplementation of Oracle Planning and FCC into a unified application with Groovy-driven business rules and significantly reduced forecasting time.
Context
An energy operator with both traditional oil and gas activities and a growing alternative energies portfolio was running Oracle EPM Planning and FCC as two separate applications. Long-range planning sat on one side. Consolidation sat on the other. The team wanted to bring them together into one unified environment where forecast data could flow through consolidation the same way actuals did. They also wanted to modernize the business rules. The rules that supported forecasting had been built over time and were taking long enough to run that the team was scheduling them outside of business hours.
What I delivered
A full reimplementation of the Oracle EPM environment into a single-application architecture.
- Merged Planning and FCC into one unified application so consolidation and elimination could run for forecast data alongside actuals
- Rewrote all business rules in Groovy with optimized data pipelines
- Designed and implemented long-range planning, which had not previously existed in the environment
- Implemented a workflow layer for approval and review across planning and consolidation
- Aligned consolidation and elimination logic so forecast numbers reconciled cleanly with actuals
Outcome
Business rule execution improved by roughly 98 to 99 percent compared to the previous environment, moving from multi-hour runs to seconds for comparable calculations. Long-range planning is now in place and runs through consolidation in the same flow as actuals. Forecast and actual numbers reconcile cleanly, which had not been possible in the previous setup.
Vena planning with multi-layer minority interest
A real estate investment firm models actuals and forecast consistently across a large portfolio with layered ownership structures and minority interest applied at each level.
Context
A real estate investment firm held a large portfolio of investee entities across a multi-layer ownership structure. The parent held stakes in holding companies, which in turn held stakes in operating entities, each with different ownership percentages. Treating minority interest correctly across this structure was essential for both historical reporting and forward planning. The team wanted one platform where actuals and forecast were handled consistently, with minority interest applied at each level of the ownership tree.
What I delivered
A Vena planning model designed around the firm's actual ownership structure.
- Multi-layer ownership tree modeled natively in Vena with configurable percentages at each level
- Minority interest logic applied at each layer for both actuals and forecast
- Consistent treatment of historical and forecast periods, so ownership percentage changes over time compute correctly in both directions
- Clear audit trail from source-level data up through each layer of consolidation
- Training for the finance team on how to update ownership structures and run the model independently
Outcome
The finance team now has a single consolidated view of the investment portfolio with minority interest correctly applied at every ownership level, across both actuals and forecast. Adoption has been strong and the platform is in active monthly use.
Planning rules rescued and rebuilt for an engineering firm
The most used business rules in a large custom Oracle Planning application were simplified, re-engineered in Groovy, and put back under control, with a documented root cause for the calculation that kept failing.
Context
An engineering firm ran a large custom Oracle Planning application whose most used business rules had grown complicated over the years. Some runs failed with errors that changed from one run to the next. Others stopped without any error at all. Every change to a rule was slow and risky, and the administrators were spending their time firefighting instead of supporting planners.
What I delivered
A rescue and rebuild of the rule layer, plus the planning logic the firm had been missing.
- Simplified the logic of the most used rules, then re-engineered them in Groovy for speed and for easier changes when the business asks for one
- Found the root cause of the failing calculation, a parallel execution pattern that ran out of memory only at full volume, and documented it for Oracle Support with reproducible evidence
- Fixed technical issues and logic errors in the existing rules
- FTE and rate logic, budget seeding and guidance, and forecast calculations that fire from the forms planners already use
- An audit trail on save that records who changed what and when, one clean record per save
- A better reconciliation process for the administrators, plus ad hoc support and recommendations along the way
- Knowledge transfer and handover so the internal team owns the application
Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
The rules that planners depend on run faster and are easier to change. The calculation that used to fail has a documented root cause. Administrators have a reconciliation process, an audit trail, and a clear handover, and the internal team owns the application.
Related reading: the first 30 days when an Oracle EPM go-live goes wrong.
Workforce rules that run in minutes and project budgets that flow to Oracle Fusion PFM
A government organization gets Workforce Planning calculations down from hours to minutes, rules that run on save, and a two way integration between Oracle Planning and Oracle Fusion Project Financial Management.
Context
A government organization plans project budgets in Oracle Planning and manages projects in Oracle Fusion Project Financial Management (PFM). Moving budgets and forecasts between the two systems took manual reshaping. Workforce Planning calculations took hours, and planners ran rule after rule by hand with a right click after every change.
What I delivered
Faster workforce planning, a better Projects module, and an integration that works in both directions.
- Workforce Planning rules re-engineered: calculations that took hours now finish in a few minutes
- Rules run on save, so planners no longer right click through a list of rules after every edit
- The Projects module improved, with integration back and forth between Oracle Planning and Oracle Fusion PFM
- Metadata updates to keep the two systems aligned
- VBA conversion scripts updated to automate the steps in between and to carry new fields: Planning exports become PFM load files with plan versions, line references, resources, and scenario rules handled automatically
- Data validation on every load in both directions
- Train the trainer sessions and UAT support
A conversion run handles as many projects as the source holds; the only practical limit is Excel itself, and every converted file loads straight into EPM. Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
Workforce forecasts that took hours now take a few minutes. Planners save and the numbers follow, with no rules to run by hand. Project budgets and forecasts move between Planning and PFM without re-keying, validated at each step. The organization's own trainers carry it forward.
Driver based expense allocations and steady application care for a subscription fitness business
A digital fitness and wellness company allocates expenses on revenue share, FTE, and manual drivers, and keeps its planning application healthy between cycles.
Context
A consumer subscription business in digital fitness runs its planning on NetSuite Planning and Budgeting. Finance wanted expenses allocated on drivers, the same way every time, instead of spread by hand, and an application that stays healthy without a full time administrator.
What I delivered
Driver based expense allocations and ongoing care of the application.
- Expense allocations built on drivers: revenue share for most costs, FTE where that fits better, and manual input where finance wants direct control
- Application health maintenance between planning cycles
- Administration help that simplified routine processes for the finance team
Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
Forecasting is easier. Finance can review the data at any time instead of waiting for a cycle to close, and keeps versions and scenarios side by side to compare.
Group consolidation moved to Vena for a mining company
Entities, currencies, ownership structures, and intercompany activity rebuilt in Vena, replacing an outdated legacy consolidation system.
Context
A mining group consolidated its results in an outdated legacy consolidation system. Multiple entities, multiple currencies, layered ownership, and intercompany activity all had to keep working through the move to Vena, and the consolidated numbers had to match before anyone would trust the new system.
What I delivered
The full consolidation build in Vena.
- Entity structure and consolidation logic
- Multi-currency translation
- Ownership structures
- Intercompany eliminations
- Consolidated reporting
Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
The group consolidates in Vena, with the legacy consolidation system replaced. Entities, currencies, ownership, and intercompany all run in the new platform, and the results were tied out against the old system before cutover.
Revenue model in Vena for a pharmaceutical company
Products, channels, cost centres, and rebates in one revenue model, with a flexible calculation for the open months of the year.
Context
A pharmaceutical company needed a revenue model that finance could run and change on its own: many products, several channels, cost centres, rebates, and a calculation for the open months of the year that gives finance several ways to project them.
What I delivered
A revenue model built in Vena.
- Revenue planning by product, channel, and cost centre
- Rebate logic built into the model
- A calculation for the open months of the year with the options finance needs to choose how each month is projected
Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
Revenue planning runs in Vena by product, channel, and cost centre, with rebates and open months handled inside the model rather than around it.
Driver based forecasting and expense allocation for a non-profit
Revenue and cost of goods driven by activity, expenses trended from history, and actual expenses allocated across departments by one re-runnable engine.
Context
A non-profit needed a forecasting process that follows its operations: revenue and cost of goods that move with activity, expenses that trend from history, and actual expenses allocated across departments on drivers that finance can explain.
What I delivered
A forecasting process and an allocation engine built in NetSuite Planning and Budgeting.
- Driver based revenue forecasting
- Driver based cost of goods forecasting
- Expense forecasting by trending
- Allocation of actual expenses: eight driver based allocations in one Groovy engine (FTE, meals, and production shifts as drivers), configured through one table, re-runnable without double counting, and every allocated amount traceable to its source
- Test scripts, security, an administration guide, and coaching sessions so the team runs it themselves
Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
Planners enter volumes and drivers, and revenue, cost of goods, and expenses follow. Allocations that finance can explain run in minutes and net to zero across departments.
Cost allocation in Oracle EPCM for an asset management firm
Three allocation rules, each with its own logic, run over a dynamic time range, and a Groovy connection that pulls the driver data from the client's external system through its API.
Context
An asset management firm needed its costs allocated across funds with more than one method, on drivers that live in a system outside Oracle and change often. Loading those drivers by hand was never going to hold.
What I delivered
The allocation build in Oracle EPCM, end to end.
- Three allocation rules in the EPCM rule engine, each with a different allocation logic, including a direct allocation
- A Groovy rule that connects EPCM to the client's external, non Oracle system through its API and pulls the driver data in, scheduled to run every morning
- A dynamic time range: a Groovy driver calculation with run time prompts, so finance chooses the year, the start and end of the driver window, and the allocation month each time it runs, and the allocations follow
- An architecture decision to keep everything inside one EPCM application instead of a separate Planning application pushing data across
- UAT test scripts to prove every rule before go-live
Change the driver window next month and nothing needs rework. Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
The application is live. Allocations run on the method and the dynamic time range finance chooses at run time, the driver data arrives on its own every morning, and every allocated amount traces back to the driver that produced it.
Planning application support for universities in Canada and the United States
Ongoing support across Vena and Oracle EPBCS for a Canadian university and US universities: re-engineering existing solutions, building new logic, and troubleshooting issues.
Context
Universities run planning applications that have to keep working through every budget cycle while the requirements keep moving. A Canadian university and US universities needed an architect who could re-engineer what no longer fit, build what was missing, and fix what broke, on two different platforms.
What I delivered
Support across both platforms.
- Re-engineering of existing solutions that no longer fit the way the universities plan
- New calculation logic built where the applications had gaps
- Troubleshooting and resolution of issues as they came up
Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
Planning applications on Vena and Oracle EPBCS that keep working through the cycle, with solutions re-engineered where needed and new logic in place.
Driver based forecasting for an online trading platform
Revenue, cost of goods, and operating expenses forecast from drivers with different trending methods, with the dashboards and reports finance runs the business on.
Context
An online trading platform company wanted a forecasting process that follows its business drivers instead of last year plus a percentage: revenue, cost of goods, and operating expenses each projected the way that fits them, and a set of dashboards and reports that finance and management read every month.
What I delivered
The driver based forecasting build in NetSuite Planning and Budgeting.
- Driver based revenue forecasting
- Driver based cost of goods forecasting
- Driver based operating expense forecasting, with different trending methods so each line is projected the way that fits it
- Dashboards and reports on top of the forecast
Every change shipped with proof: results compared side by side against the previous numbers before finance accepted it.
Outcome
Finance enters drivers and the forecast follows, line by line, with the trending method that fits each one. Dashboards and reports show the result without a spreadsheet in between.
A note on references
Some of these engagements were delivered directly and others through consulting partners and system integrators, where direct client references are governed by partner agreements. In every case the work described is work I performed, written without client names or identifying details. Every engagement was delivered with number validation, side by side against the previous method, because that is what finance trusts. I am glad to walk through any of them in detail on a call.
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