To establish absolute mathematical facts for distribution center engineering and system proposals, data analysis is performed step-by-step through the following 9 logical procedures. Below is a detailed explanation of each step's operational objective and logic.
Raw shipment logs (CSV/Excel files), item masters, and customer masters supplied by clients almost always contain format inconsistencies, duplicates, and missing codes. We first perform rigorous data cleansing and then import the cleaned dataset into a high-capacity relational database (such as Access or SQL Server).
Within Tera Calculation, data is stored as the "T000_ShipmentData" table inside a freshly initialized database environment using a standardized schema. At this stage, control totals (total row counts, total piece quantities) are recorded to establish baseline benchmarks for double-checking data integrity in subsequent analytical stages.
Primary relationships (joins) are established between the imported shipment log and item master files (pack sizes, cases per pallet, case volume, gross weight) as well as destination masters using key fields (item code, customer code). Linking physical dimensions to order lines transforms transactional data into physical volume metrics required for engineering.
Tera Calculation automatically calculates physical equivalents for every record line: "Case Equivalent (Pieces ÷ Pack Size)", "Pallet (PL) Equivalent (Cases ÷ Cases per Pallet)", "Volume Equivalent (Cases × Case Volume)", and "Weight Equivalent (Cases × Gross Weight)". This process converts simple order piece counts into volumetric and gravimetric space requirements needed to design storage racks and material handling equipment.
Using database queries, multi-dimensional aggregations (line counts, cases, pallets, volume) are executed across annual, monthly, weekly, daily, and hourly timeframes. This visualizes volume volatility and peak characteristics caused by seasonality, month-end spikes, and promotional campaigns.
A crucial engineering principle is to avoid designing facility capacities around maximum peak days. Sizing buildings and machinery to absolute peak spikes causes vast idle space and excessive capital expenditure during normal periods. In Tera Calculation, a standardized high-demand day (such as a representative weekday target day) is selected as the baseline. Peak-day surcharges are addressed through operational strategies, such as shift extensions, temporary staffing, third-party warehousing, or pre-picking workflows.
Physical operational flows (receiving, inspection, putaway, bulk storage, replenishment, picking, sorting, packing, staging, and loading) within the target distribution center are mapped into a visual operational flow diagram.
Inbound unit loads (single-SKU pallets vs. mixed-case pallets) and automated replenishment routes to pick faces (flow racks, shelving) are mapped. Structuring this material flow beforehand clarifies EIQ aggregation boundaries and system concepts regarding equipment placement.
Designing a facility purely on historical performance data risks obsolescence as business grows or product lines change. Therefore, future growth parameters are applied to the target-day baseline data established in Step 3 to generate "Future Projection Datasets."
Parameters include annual volume growth rate (%), SKU expansion forecasts, destination network changes, and seasonal assortment shifts derived from corporate business plans. Using future-adjusted datasets ensures facility designs can seamlessly accommodate business expansion 3 to 5 years post-launch.
Future-projected target-day datasets undergo core "EIQ Matrix Analysis" in Tera Calculation 1. Unlike traditional 3-tier ABC analysis, volume is cross-aggregated into a 25-block matrix (5 Item Tiers × 5 Destination Tiers).
Theoretical inventory holding requirements and daily inbound volumes are back-calculated from outbound demand data using mathematical models.
Completed analytical outputs in Tera Calculation (summary totals, EIQ scatter plots, 25-block EIQ matrix tables, equipment throughput tables, inventory/inbound estimations) are directly exported into Microsoft Excel via data grid views.
Exported datasets contain both absolute physical units (cases, pieces, volume m³, gross weight kg) and percentage composition data (100% total base). Engineers utilize these structured outputs to produce presentation graphics and custom summary tables:
Visualized tables and charts generated in Step 8 are embedded into "Chapter 1: Design Prerequisites" and "Chapter 2: Technical Rationale (Equipment Sizing & Area Calculations)" of the formal logistics engineering proposal.
Providing quantitative, fact-based answers to questions like "Why is this AS/RS required?" or "How was this square footage calculated?" delivers convincing proof to client executive management and logistics leaders, producing high-trust technical proposals.