Leveraging Historical Data for Smarter Stock Allocation
The Forecasting Challenge in Modern Procurement
For procurement teams, accurately predicting inventory needs is a constant balancing act. Overstocking ties up capital and increases storage costs, while understocking leads to stockouts, delayed projects, and lost revenue. ACBUY’s methodology demonstrates how structured spreadsheet analytics can transform this challenge into a data-driven, precise planning process.
The Core Data Foundation: Previous Orders & QC Analysis
Accurate forecasting begins with mining your existing data. Two primary datasets are crucial:
- Historical Order Data:
- Quality Control (QC) Data:
Step-by-Step: Building a Forecasting Model in Your Spreadsheet
1. Data Consolidation & Cleaning
Create separate sheets or tables for OrdersQC Results. Clean the data by standardizing SKU names, dates, and removing outliers. Use In a new "Analysis" sheet, calculate for each SKU:VLOOKUPXLOOKUP
2. Calculating Key Demand Metrics
3. Determining Reorder Points & Safety Stock
Incorporate lead time and desired service level to build a robust model:
- Lead Time Demand:= [AMD] * [AverageLeadTimeInDays] / 30
- Safety Stock:= [Z-score for service level] * [Demand Std Dev] * SQRT([LeadTime]/30)
- Reorder Point (ROP):= [Lead Time Demand] + [Safety Stock]. This tells you when
4. Projecting Future Allocation
For the next planning period (e.g., next quarter), calculate:
Forecasted Order Quantity = ( [Forecasted AMD] * [Period] * [Defect Adjusted Rate] ) + [Safety Stock] - [Current Inventory]
Use spreadsheet tools like trendlinesForecasted AMD.
Visualizing for Insight and Action
Create dynamic charts to communicate the plan:
- Time-Series Line Chart:
- Inventory Runway Chart:
- Supplier Comparison Dashboard:how muchfrom whom
- Inventory Runway Chart:
ACBUY Best Practices for Sustainable Accuracy
- Regular Data Refresh:
- Scenario Analysis:
- Collaborative Review:
- Continuous Refinement:
- Scenario Analysis: