Manufacturing depth. Applied across the operating landscape.
We tailor analytics strategy, dashboards, forecasting and AI-assisted decision support to the way each industry actually plans, sources, makes, delivers, returns and enables its operations.
Manufacturing
Production planning and scheduling, capacity analytics, IIoT plant monitoring, and multi-tier demand consensus across MTS and MTO flows. Our deepest domain expertise.
Retail
Demand forecasting, assortment and inventory optimisation, and store-to-DC replenishment analytics tuned to fast-moving, promotion-driven demand.
CPG
S&OP consensus planning, trade and promotional forecasting, and distribution analytics across high-SKU, short-shelf-life portfolios.
Pharma
Batch and capacity planning, regulatory-aware inventory logic, and track-and-trace analytics across complex, compliance-driven supply networks.
Industrial
Engineer-to-order and configure-to-order planning, spares and warranty analytics, and control-tower visibility across long-lead-time components.
Distribution
Network design analytics, order-promising, and logistics performance tracking across multi-echelon distribution networks.
Logistics
Route and carrier performance analytics, reverse logistics, and real-time track-and-trace visibility across transportation networks.
Supply chain planning transformation — commercial vehicle manufacturer
Four business problems. Six core capabilities. One planning platform. Every capability exists to move a decision that was costing the business something.
Demand clarity across a multi-tier channel
Demand forms across dealer, regional and vertical tiers, with make-to-stock and make-to-order flowing through the same network — a genuinely hard consensus to converge on.
Integrated Demand Planning · S&OP Command Centre
Targeted impact: one consensus demand plan with multi-tier roll-up, dual-cycle governance and full plan-change traceability.
Order promises aligned to supply reality
Promising dates across a wide product and variant mix calls for a view of capacity and material availability that spans several systems at once.
Supply Planning & Order Promising
Targeted impact: delivery dates computed from the live supply plan, so commitments reflect real capacity and material availability.
Planner effort moved to the decision
Experienced planners were spending a large share of their day assembling the picture across sources before they could act on it.
Supply Chain Control Tower · Planning Intelligence & Decision Support
Targeted impact: exceptions surfaced early with the analysis already done — effort moves from compiling to deciding.
Trusted data, and plans that can be tested
Planning quality rests on master data consistency across a large finished-goods and variant hierarchy — and on being able to test a plan before committing to it.
Master Data Management & Governance · Scenario Simulation & What-If Planning
Targeted impact: governed planning master data, and what-if testing before the business commits.
Underneath: multi-tier planning hierarchy design (dealer → regional → vertical), dual-cycle S&OP with plan-change tracking, MTS and MTO demand in a single planning view, and ageing- and liquidation-aware replenishment logic.
Eight stages of decision maturity
Most organizations are at stage two, three or four. AI is an evolution along this path — never a prerequisite for starting.
Understand
Process undocumented; decisions live in people's heads → business and process discovery, decision mapping.
Improve
Process is known but inconsistent → process redesign, planning operating model, S&OP cadence.
Trust the data
Reporting exists but nobody believes it → data readiness assessment, master data governance, EDA.
See clearly
Reporting works; insight is still manual → analytics, planning intelligence, visibility and control tower.
Predict & optimise
Insight exists; decisions remain judgement calls → forecasting science, simulation, optimisation.
Act intelligently
Models exist but sit outside the workflow → intelligent planning applications, decision workflows.
Scale the decision
Decisions are good but do not scale to volume → AI agents on exceptions and routine decisions.
Continuous
The organisation improves its own decisions → sustained partnership, capability transfer.