To derive absolute numerical grounds (facts) in distribution center system design and engineering proposal writing, data analysis proceeds logically and thoroughly according to the following 9 steps. The purpose and specific practical operational logic of each step are explained below.
Raw source data (raw data) provided by customers—such as historical shipping data (CSV/Excel format), product masters, and destination masters—almost always contains mixed notation inconsistencies, duplicates, format mismatches, and non-standard characters. First, after organizing these inconsistencies (data cleansing), import them into a database (Access, SQL Server, etc.) adept at high-speed processing of large-volume data.
Regarding Tera Calculation specifications, against a completely initialized (empty state) database environment, data is stored into the "T000_ShippingData" table based on a unified import format. At this stage, control totals (total count/quantity) of data row counts (record counts) are recorded to establish benchmark values for double-checking whether data omission or calculation inconsistencies occur in subsequent aggregation steps.
Primary joining (relationships) is established between imported shipping data in the database and product masters (case pack quantities, pallet case stacking quantities, outer case volumes, case gross weights) or destination masters using key items (product code, destination code). By linking physical attributes such as dimensions and weight to mere order line item information, visualization of physical quantities necessary for logistics system engineering becomes possible for the first time.
Through automated calculation processing in Tera Calculation, "Converted Case Values (piece quantity ÷ case pack quantity)," "Pallet (PL) Converted Values (converted case value ÷ PL stacking quantity)," "Converted Volume Values (converted case value × case volume)," and "Converted Weight Values (converted case value × case gross weight)" are calculated in batch per shipping line item record. Through this, order quantity (piece count) information is completely converted into spatial and weight volume quantities required to design warehouse space, storage racks, and material handling equipment.
Utilizing database queries, multi-faceted volume aggregations (shipping line counts, case counts, PL counts, volume amounts, etc.) are executed across yearly, monthly, weekly, daily, and hourly time slot axes. This visualizes the swing width and peak characteristics of "shipping fluctuations (volatility)" driven by seasonal factors, end-of-month surges, weekend concentrations, and sale periods.
The greatest point in design is not conducting facility design aligned with the annual maximum peak day. Determining equipment or square footage aligned with maximum peak days creates vast wasted space and excessive machine investment during off-peak periods, resulting in an extremely uneconomical distribution center. Therefore, Tera Calculation selects a leveled "weekly average day" or "standard high-load day (target day on a specific day of the week)" rather than weekly maximum peak days as the system design benchmark day. Regarding excess volume on peak days, company-wide agreements are formed to cover it via software aspects (operations), such as extending working hours, temporary staffing increases, spot utilization of external warehouses, and advanced shipping operations.
Organize the series of physical operational lines in the target distribution center (inbound, inspection, put-away, storage, replenishment, picking work, sorting, packing, outbound staging, loading) and create a visual "Logistics Operational Flowchart."
Visualize inbound packaging conditions (whether palletized or mixed-case inbound), automated replenishment routes from storage areas, and volume line branching passing through operational racks (flow racks or medium/light-duty shelving). Structuring this material flow in advance makes the system concept extremely clear regarding aggregation categories for the next EIQ analysis and which equipment should be placed in which process.
Designing a new center based solely on raw historical volume data (current status data) prevents accommodating business growth or product handling shifts after startup. Therefore, target day data determined in Step 3 is multiplied by fluctuation parameters based on future business plans to generate "Future Vision Cargo Volume Data."
Specifically, elements such as "annual shipping growth rate (%)" based on management plans, "expected increases in active item (SKU) counts," "fluctuations in destination (store/client) counts," and "seasonal product lineup shifts" are mathematically incorporated. Using this future adjustment data as the baseline for aggregation enables the design of a distribution center possessing scalability capable of withstanding business expansion 3 to 5 years later.
Against target day data incorporating future visioning, execute "EIQ Matrix Analysis," the core feature of Tera Calculation 1. Unlike conventional ABC analysis (3 rank classifications on a single axis), volume is aggregated and classified into a "25-block matrix table" intersecting item ranks (5 classifications) and destination ranks (5 classifications).
From historical shipping data, back-calculate and estimate "theoretical inventory storage volume" and "daily appropriate inbound volume" required for the center based on mathematical logic.
Directly export various analysis results completed on Tera Calculation (simple shipping data aggregation, EIQ scatter plots, 25-block EIQ matrix tables, equipment allocation volume tables, inventory/inbound estimation tables, etc.) from the system into Excel format via DataGridViews.
Result data exported from Tera Calculation is organized not only as actual numbers (case counts, piece counts, volume m³, weight kg), but also as constituent percentage (%) data taking total volume as 100%. Based on this exported data, practitioners freely create presentation graphics and custom summary tables like the following:
Consolidate and publish the charts, tables, and numerical data beautifully visualized and structured in Excel in Step 8 into Chapter 1 "Precondition Settings" and Chapter 2 "System Design Basis (Equipment Selection & Required Area Calculation)" of the logistics system proposal document.
To questions like "Why is this automated warehouse necessary?" or "Why does it require this number of shelf tiers / square meters?", presenting quantitative justification that is completely data-driven (fact-based) rather than intuitive or rule-of-thumb completes the highest quality proposal document offering overwhelming persuasiveness and trust to customer executive teams and logistics managers.