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Slip Robotics is a fast-growing robotics automation company transforming logistics through autonomous platforms. As Supply & Demand Planner, you will be the critical link between Revenue and Operations, owning the master production schedule, sales pipeline translation, and inventory strategy across active and legacy equipment fleets.
Key responsibilities include building and maintaining the master production schedule published to Sales as committable dates; operating the SIOP (Sales & Operations Planning) process to translate pipeline probability into build authorizations and capacity checks; planning material coverage for all committed and probable deployments to ensure equipment readiness; and owning the unified inventory plan across production, headquarters, and field stocking locations with accountability for min/max levels, safety stock policy, reorder triggers, and stocking versus on-demand sourcing decisions.
You will also own lifetime-buy and service-parts strategy for the legacy fleet, sized against reliability data and remaining contract life, managing total inventory value within company exposure caps. You'll partner with purchasing and sourcing on execution and with Finance on inventory classification and reserves.
The ideal candidate brings 4–7 years of supply/demand planning or S&OP experience within manufacturing, with hands-on MPS/MRP ownership in an ERP environment. You should have inventory optimization experience with direct accountability for both availability and working-capital outcomes, ideally across multiple stocking locations, and demonstrated ownership of a cross-functional operating rhythm such as SIOP.
Preferred qualifications include experience planning in a build-to-availability model where delivery dates are a sales lever, service or spare-parts planning for fielded equipment tied to contractual uptime, end-of-life and lifetime-buy planning, NetSuite or SAP MRP expertise, APICS/ASCM CPIM certification, and background in capital equipment, robotics, or RaaS (Robotics-as-a-Service) business models.
