An overseas parent company had acquired five Australian lifting and rigging businesses and merged them into a single operation. Microsoft Business Central was implemented, but master data from all five businesses was loaded without cleansing. The result: thousands of duplicates, inconsistent naming, no standardised pricing, and unreliable procurement data.
The practical impact fell hardest on the sales team. With no pricing in the system, staff quoted from memory or from spreadsheets two to three years out of date. During post-COVID inflation, input costs rose while quoted prices lagged. The business had limited control over its margins.
Establishing standards: Before cleansing, Waypost defined what good data should look like — item master standards, category structures, and field definitions. The customer base was segmented into four categories, enabling pricing to vary by both product attributes and customer type.
Working through the data: Waypost managed cleansing end-to-end, one category at a time: removing duplicates, correcting descriptions, enhancing procurement data. A key early decision was closing miscellaneous item codes that had become catch-all buckets, resulting in approximately 2,000 new, properly specified items.
Building the pricing framework: For each category, Waypost convened pricing workgroups with the executive team. Pricing models calculated sell prices based on landed costs and category-specific attributes. The result was 35,000 to 45,000 price points across 7,000 SKUs and four customer tiers.
Managing the change: Each wave required careful change management. Waypost briefed sales personnel before pushing master data changes and new pricing. Service codes were consolidated from 250+ down to approximately 20 standardised labour codes.
The client removed more than 5,000 duplicate SKUs and enhanced 9,000 items. Of these, 7,000 now use structured pricing that auto-populates when staff build quotes. The business now has margin visibility it never had before.
The work created a platform for further improvement: a rapid six-week project optimised pricing on low-value items and introduced volume discounts — work impossible without the foundational data quality now in place.
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