Two Decades of Analyzing Big Data To Drive Key Decisions.
I am passionate about applying analytics and best-in-class planning processes to improve business planning.
- Finance MBA, University of Minnesota
- Chartered Financial Analyst (CFA) Designation
- Investment Banking: 30+ Closed Deals
- Supply Chain Director: Reporting and planning lead for 120 plants
- Division CFO: $150 Million Industrial Business
Assembling a Global Plan.
Plan Director, Global Supply Chain
What I Found: A Fragmented Plan. Each region historically employed a planning process that was different from all the others. Everyone used different inputs, and operated on a unique timeline. This made it nearly impossible to predict supply chain costs at the global level.
What I Did. With input from regional teams, I defined a consistent planning procedure for each region. We all agreed to a standard set of assumptions and adhere to the same timeline:
- Consistent Measure of Foreign Exchange Impact
- Consistent Calculation of Rate, Volume, and Mix Drivers
- Growth Assumptions Aligned to the Business
- Designed Review Process and Cadence
My Impact. With a reliable global plan, we could more easily compare performance across regions. This clarity unlocked efficiency improvements, and enabled centralized investment decisions at the global level.
Planning From Ground Up.
CFO, Division Spinoff
I led the financial separation for a division spinoff.
My finance team built the controls and reporting for the newly-independent business:
- We built forecast and plan procedures from scratch
- Resolved errors in demand planning
- Eliminated redundant SKUs
- Root Cause Analysis: Source of Excess Inventory and Stockouts
- Process Review: Apply Best Practices to Your Planning Cycle
- SKU Rationalization
- Enterprise-Level Reporting
- SKU-Level Analysis
Experience. Each of my corporate roles provided unbelievable experience with best-in-class planning practices. I learned how to facilitate a global plan process across languages and time zones.
Balance. Great experiences often come with a cost of extended working hours away from home. As my young family grows, I want to spend less time in the office and more time with my wife and kids.
What This Means For You. Over the first 20 years of my of my career, I have attained immeasurable experience in financial and supply chain planning. I offer this experience to businesses seeking to improve their own planning processes:
Case Study: Inventory Out of Control
Problem: Excess Inventory + Unfilled Orders
A multinational industrial business continued to accumulate an excessive amount of unsold inventory. As this inventory expired, the business had to regularly write off and dispose of expired product.
Losses from expired inventory exceeded 5% of sales in some countries.
My Approach: Test Entire S&OP Process
I audited the entire sales and operations process, all the way from the sales pipeline to the production plan at the plant.
I compared the reported sales gains with invoiced sales, and measured the accuracy of both the demand plan and supply plan.
What I Found
Finding #1: Sales Team Overstating Wins
The sales team was pressured to report new wins, and frequently overstated the size of the win.
I compared reported gains against actual invoices, and I identified $40 of actual gains for every $100 in reported gains. Thus, reported account wins were overstated by 250% percent.
The reported gains were being fed into the demand plan. As a result, the demand forecast was only 40% accurate.
Finding #2: System Errors Impacted Supply Plan
I reviewed the plant’s supply plan, and quickly realized the supply plan was not aligned with the demand plan. The plant was mysteriously producing too much volume for a number of SKUs, while underproducing volume for other SKUs.
After some research, we found the legacy supply planning tool had not been updated to current release, and some routines were not configured correctly.
This outdated software was causing errors in the production plan, resulting in the plant producing the wrong quantities for many SKUs.
Total Impact: 75% Error Rate
When the inaccuracies of the demand plan (which was 40% accurate) were combined with the above system errors at the plant, the plant simply was not making the correct amounts of product for the plant. In fact, I calculated the accuracy at about 25%.
To illustrate the impact of this: If the business needed 100 units of a specific product in a given month, the plant was on average producing as little as 25 units or as much 175 units.
Some products were constantly under-produced, which yielded low customer fill rate. Other products were constantly over-produced, resulting in excess inventory that eventually expired
I built a Standard Procedure for communicating the sales pipeline:
We developed rules for claiming new account wins, and set up periodic check-ins with sales leads going forward.
We set up Recurring Meetings to ensure sales, marketing, and supply planning were Aligned with the new demand forecast each month.
Sales Leaders had to Justify large volume swings, and plant leadership was held accountable for managing production against the forecast.
This project revealed Technical Issues with legacy planning tool that resulted in Over-Production of various SKUs.
We engaged a programming team to fix the routines in the supply planning software. We Closely Monitored the supply plan for accuracy going forward.
Media and Contributions
Business News Daily: Your Guide to Inventory Management Software
Data Science Dojo: Machine Learning Will Revolutionalize Demand Planning
6 River Fulfillment Systems: Ingredients of an Effective Logistics Strategy
Business.com: How Pass-Through Entities Work
Enterprise League: 22 Smart Ways to Increase Efficiency in Business
Digital Guardian: Tips for IP Protection for Manufacturers
SAP Community: Blog Post Contributions
Upjourney: What Does an Investment Banker Do?
Flow SEO: 18 Tips on Becoming a Consultant
Upjourney: 50 Good Questions to Ask in an Interview
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