How a Commercial Cleaning Supplier Cut Inventory 55% and Raised Service 200 bps With One Daily Plan

A commercial cleaning supplier serves restaurants, hotels, healthcare and janitorial businesses, and builds equipment kits for customers that run many sites. Every order it ships carries an on-time, in-full promise.

So the supplier runs seven planning workflows on Solvoyo as one closed loop, from master data and forecasting to order promising, DC purchasing and kit production planning. One optimization run a day turns demand signals and vendor data into purchase and production orders. Inventory is down 55% and service is up 200 bps.

Kit Demand Cascades Through Every Bill of Materials

A kit can only ship when its scarcest component is there. Demand for one kit runs down through sub-kits and component groups, each with its own lead time, order quantities and substitution rules. Vendors send master data, costs and open orders on their own clocks, and customers expect every order on time and in full.

The planning team was dealing with:

• Delivery dates promised without a check on component availability
• Demand from open opportunities that the supply plan could not see
• Equipment lists rebuilt by hand for every customer site
• No way to trace a promised date back to the component that constrained it

Seven Workflows and Kit Production Planning in One Daily Plan

Solvoyo reads inventory, orders, bills of materials and receipts from the supplier’s ERP, vendor feeds, purchase orders and invoices from its procurement platform, and carrier tracking over EDI. One optimization run a day closes the loop.

Master data management keeps each product in one record: status, type, lead times, order quantities and ownership, with multi-level bills of materials and their substitution and replacement rules. Vendor data is reviewed before it reaches the master, and cost changes pass approval gates. Every change is logged, and approved changes sync back to the ERP.

Demand planning runs an AI-based forecast every week across the active SKU base and selects a model per item group. Engine warnings and outliers go to a planner for review. The forecast and captured demand combine into one view of total demand.

Available to promise captures prospects with a probability rating and a location count, and applies equipment templates to generate the order lines. It then simulates availability down to the constraining component. The team gets an earliest available date before it promises one, and approved demand feeds purchasing and production.

DC purchasing and production planning net requirements against inventory and inbound receipts, then shape them to minimum and incremental order quantities. Approved purchase orders go to the procurement platform and are tracked to their need-by date. Kit production is planned against component availability and the freeze and firm calendar, then sequenced and pushed to the ERP.

Inventory optimization segments every SKU on value, variability, order frequency and volume. A target service level per segment drives dynamic safety stock, and planners see the suggested and revised figures and set the final one themselves. A 13-week projection shows the forward position, and non-performing stock is classified with its root cause.

KPI tracking puts service, capital, plan acceptance and data quality in one place: OTIF against SLA rules, user acceptance rate on purchase and production recommendations, inventory investment and excess by category and owner, and alerts for stock-outs, reorder points and late orders. Data-quality rules flag master data and integration issues before they reach the plan.

Solvi Explains the Plan and Places the Order

Planners ask Solvi, Solvoyo’s AI planning agent, in plain language: why a purchase order recommendation is exactly that quantity, which SKUs need attention today, why a kit cannot be produced next week, or why OTIF dropped last month. Solvi returns the figure and the reasoning behind it. For a purchase order, that is the requirement against inventory and receipts, then the order-quantity shaping, with every input shown. For the day ahead, it is a ranked worklist, worst exception first. For a blocked kit, it names the lead time, freeze week or do-not-buy component. For OTIF, it splits misses into on-time and in-full, by customer.

Solvi can also raise purchase and production orders from a recommendation. Nothing high-impact runs until a person approves it. Every answer names its inputs and its as-of time, and Solvi says when the data behind an answer is missing. The language model sees only the database schema; the query runs inside the customer’s own cloud, so business data never leaves it.

55% Less Inventory, 200 bps Better Service

With kit production planning, purchasing and inventory in one daily plan, the supplier holds 55% less inventory and its service level is 200 bps higher.

The other gains:

• 5x planner productivity
• 87% of production recommendations accepted by planners
• 82% of purchase order recommendations accepted by planners
• Delivery dates checked against component availability before they are promised, with the constraint traceable
• Every planner override recorded with a reason code, and every master data change logged

What Does This Mean for You?

This supplier no longer promises dates it has not checked. Every purchase order, production order and delivery date comes from one daily plan that shows its inputs, and planners accept most of it as recommended.

Are your products built from components with different lead times, with planners still piecing the picture together by hand? Look at how one daily plan connects master data, forecasting, order promising, purchasing and production.

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