Operational Decision Services, Built on One Engine

Five modular assessments. Your team scopes which modules to evaluate, and the findings drive which services we operationalize together.

• A1 · Inventory: stocking strategy and service philosophy, policy design, item and location data quality, positioning by node, working capital, excess and obsolete discipline, service versus policy performance.
• A2 · Network & Distribution: DC count and roles, hub-and-spoke logic, sourcing assignments, transfer and rebalancing rules, node-level inventory health.
• A3 · Planning: forecast accuracy and demand patterns, demand management, constrained supply and capacity planning, inventory planning, process repeatability.
• A4 · Strategy & Operating Model: strategy alignment, SIOP/S&OE management system, decision rights, change discipline, continuous improvement, KPI linkage.
• A5 · AI Adoption: where AI is and could be embedded across planning, network, and execution decisions, including explainability of AI-driven recommendations.

Simulation-based, item × location inventory policies built for real-world variability rather than formula assumptions. This is the epicenter of RSI’s better science, and the engine every other service builds on.

• Item × location fit: the engine evaluates demand and lead-time history and assigns the correct statistical pattern to every item and location pair, then uses it to simulate future behavior.
• Real-world variability: day-by-day simulations run thousands of times with random demand and random lead times, converging on positions that hold service without expediting while respecting MOQs and reorder multiples.
• Patented lead-time model: two history-based inputs, average and 98th-percentile lead time, capture both central tendency and tail behavior rather than assuming a normal curve.
• Explainable decisions: we explain why every item is on, over, or under target, surfacing data integrity, process, network, demand, and supplier insights along the way.

The network is rarely short on inventory. It is short on inventory in the right place. Network Balancing extends the optimization engine across nodes, scoring every SKU × location weekly against its own simulated safety stock, reorder point, and max.

• Node-level RYGB health: position (on-hand plus on-order) is compared to simulated policy, and the color is assigned by the math, not by judgment. Red is below safety stock; blue is surplus.
• Red-first moves: we do not rebalance everything. Surplus nodes feed at-risk nodes on the same SKU, prioritized by service risk and dollar impact, and new POs are raised only when the whole network is short.
• Two modes, one engine: open-network optimization where any location can cover any other, and designed-lane DC rebalancing where historical demand mix and transport capacity set what each lane should carry.
• Guardrails built in: full pallets only, a move can never push the source below its own safe level, and hand-planned SKUs are never auto-suggested.

PRIO turns the engine’s optimized policies into a prioritized, executable playbook: what to build, where, and when, at the SKU, cell, and location level.

• Capacity-aware scheduling: one framework distinguishes Demand-Driven production from Inventory-Target replenishment and Capacity-Fill builds, replacing manual decisions with repeatable logic.
• Priority action list: buyers, planners, and schedulers start each day with a ranked queue of decisions instead of a maze of exceptions.
• Buffer engine and dashboards: inventory health classified for every SKU at every location, with historical RYGB and scheduling trends for leadership.
• Proactive alerts: demand spikes, safety-stock gaps, and overstock conditions are flagged so planners act before adverse events rather than after.

The SIOP/S&OE management system is the spine: the cadence that ties sales, planning, operations, and finance, with SIOP at the executive horizon and S&OE at the weekly horizon.

• Supply chain strategy: articulate or refine the strategy, map it to business strategy, identify alignment gaps, and define investment priorities.
• Operating model design: organization structure, decision rights, RACI, and where capabilities sit, so it is clear how decisions are made and acted on.
• Decision governance: forum design, escalation paths, and accountability mapping that resolve who decides what, and when.
• Change management and continuous improvement: stakeholder mapping, communication, adoption tracking, and a CI engine with a named owner, cadence, backlog, and results discipline.

RSI built these capabilities inside its own business first, and clients asked for them next. The newest of RSI's services, and the one most directly pulled by client demand.

• Data and decision automation: the preparation, validation, and exception handling that precede any analysis, then carrying decisions into the systems that execute them.
• Document-heavy functions: assistants over contracts, policies, specifications, and compliance material, with extraction, analysis, and deadline tracking.
• Customer and consumer support: assistants and voice agents for routine enquiries, grounded in product, warranty, and policy documentation, with a clean handoff to a person.
• Operations and manufacturing: exception handling and workflow automation, quality and process data pipelines, and internal tooling that removes manual reporting effort.
• Sales and channel: CRM automation, lead capture and scoring, follow-up sequencing, and reporting on where activity actually stands.
• Marketing, IT, and platform: content generation and campaign measurement, integration with existing enterprise systems, cloud infrastructure, and data platform work.