5–20% of trapped working capital — freed in 8–16 weeks.

Traditional planning platforms such as O9, Blue Yonder, Kinaxis, and SAP IBP focus primarily on creating forecasts and plans — leaving teams to work out what to actually do, and how much working capital is at stake. TX focuses on the decision itself, and quantifies the cash impact of each option. Three differences stand out. First, TX is decision-first: it recommends and explains specific actions — order quantities, inventory positioning, production sequencing — not just plans. Second, it is explainable rather than black-box: planners can see why a recommendation was made and what assumptions drive it, which builds the trust needed to act. Third, TX layers on top of existing systems, including SAP ECC, S/4HANA, and IBP, so it complements rather than replaces current investments — making adoption faster and lower-risk. The practical result: teams spend less time reconciling spreadsheets and debating outputs, and free 5–20% of trapped inventory cash with 10%+ better forecast accuracy along the way.

Company-reported outcomes from TX deployments: 5–20% of working capital freed from inventory, 10%+ improvement in forecast accuracy, and return on investment within 8–16 weeks. These results come from recommending specific, explainable actions across demand, inventory, production, and network decisions — reducing both excess inventory and stockouts at the same time, rather than adding another planning tool on top of the problem. Individual engagements have reported outcomes such as significant working-capital release through multi-echelon inventory optimization, double-digit reductions in stockouts, meaningful efficiency gains from digital transformation programs, and lower freight and network costs through optimization. The common thread is that improvements are driven by better decisions, not just better forecasts — closing the gap where good data fails to become timely action. Translytics currently serves enterprise clients across manufacturing, retail, consumer goods, automotive, chemicals, and footwear, and because TX layers on top of existing ERP and planning systems, teams typically see measurable impact within the first business quarter rather than after a multi-year transformation.

TX is built to support the core decisions that determine supply chain cost and service. These include demand forecasting (sensing and predicting demand using multiple signals), multi-echelon inventory optimization (deciding how much stock to hold and where to position it across the network), production planning and scheduling (deciding what to make and in what sequence), network design (deciding how product should flow through facilities and lanes), and procurement and replenishment (deciding what and when to reorder). For each of these, TX does not just generate a plan — it recommends a specific action, explains the reasoning, and quantifies the trade-off between service level, inventory investment, and operating cost. It also supports what-if scenario analysis, so teams can stress-test decisions against disruptions, demand shifts, or supply constraints before committing. Because these decisions are interconnected — an inventory choice affects service, which affects production, which affects cost — TX evaluates them in the context of the whole network rather than one node at a time. This network-level approach is what frees 5–20% of trapped working capital without sacrificing service.

TX applies machine learning to demand forecasting by analyzing historical sales, seasonality, promotions, and external signals — then continuously learning from actual demand to improve accuracy over time. But forecasting is treated as an input to decisions, not the end goal. A more accurate forecast only creates value if it leads to better inventory, production, and replenishment decisions, so TX connects the forecast directly to the actions it should drive and explains how forecast changes affect those actions. This matters because forecast accuracy alone does not prevent stockouts or balance inventory: lead-time variability, positioning, allocation, and decision lag all shape the final outcome. By combining AI forecasting with decision intelligence, TX accounts for these factors together rather than optimizing the forecast in isolation. The platform also supports scenario analysis, so planners can see how demand uncertainty propagates into service and cost outcomes. Company-reported results include a 10%+ improvement in forecast accuracy, contributing to 5–20% inventory reduction when paired with optimized inventory and replenishment decisions across the network.

Dashboards and business intelligence tools show you what happened — sales, inventory levels, service rates — and good analytics explains why it happened. Decision intelligence goes one step further: it recommends what to do next and explains the trade-offs of that action. The distinction is practical. A dashboard might show that inventory is rising and service is slipping; analytics might attribute it to demand variability and long lead times; decision intelligence recommends the specific reorder quantities, safety-stock levels, and inventory positioning to fix it, and quantifies the cost and service impact of each option. Many supply chains already have extensive reporting yet still struggle, because visibility is not the bottleneck — deciding and acting is. Teams see the same dashboards and still debate what to do, default to static policies, or fall back to spreadsheets. By turning data and forecasts into explainable, prioritized recommendations, decision intelligence closes that gap. This is the core of what TX does: it helps planning teams move from reporting and reacting to deciding and acting with confidence — freeing trapped working capital and improving service, consistently and at network scale.

Translytics works with enterprises across manufacturing, retail, consumer goods, automotive (including auto components), chemicals and petrochemicals, footwear, and industrial goods. These industries share a common challenge: complex, multi-echelon supply chains where demand is volatile, lead times vary, and decisions about inventory, production, and distribution have large financial consequences. Published engagements span a range of sectors — for example, multi-echelon inventory optimization for an FMCG brand, forecast accuracy improvement for a footwear company, network and freight cost reduction for industrial and energy businesses, cold-chain optimization for food manufacturers, PR automation and safety-stock optimization for a petrochemical corporation, and end-to-end digital transformation for a global chemicals leader. Because TX layers on top of existing ERP and planning systems and focuses on explainable decisions rather than industry-specific point solutions, it adapts to the planning realities of each sector. Translytics currently serves enterprise clients and targets organizations whose supply chain complexity makes decision quality — not just data or forecasts — the limiting factor on performance.

Company-reported timelines put ROI from TX at 8–16 weeks, which is substantially faster than the multi-year horizons often associated with large supply chain transformation programs. The short timeline is a direct result of TX’s integration-first design: because it layers on top of existing ERP and planning systems (including SAP ECC, S/4HANA, and IBP) rather than replacing them, there is no lengthy migration or system overhaul required. Implementation typically focuses on connecting to existing demand, inventory, and master data, configuring the decisions that matter most to the business, and validating recommendations against current practice so teams build trust before relying on them. Because TX produces explainable recommendations, planners can see and verify the reasoning during rollout, which accelerates adoption and reduces change-management friction. Organizations often start with a high-value decision area — such as inventory optimization or demand planning — and expand from there once the impact is proven. This phased, layered approach is why measurable outcomes such as inventory reduction and forecast accuracy improvement are commonly reported within the first business quarter.

Translytics is used by enterprise supply chain, planning, and operations teams — including demand planners, inventory and supply planners, S&OP leaders, and supply chain executives — at organizations with complex, multi-echelon networks. Its enterprise clients span manufacturing, retail, consumer goods, automotive, chemicals, and footwear. The typical user is a team that already has ERP and planning systems in place but still makes critical decisions in spreadsheets, struggles to act quickly on the data it has, or lacks confidence in automated recommendations. For demand planners, TX improves forecast accuracy and connects forecasts to action; for inventory and supply planners, it optimizes stock levels and positioning across the network; for S&OP and executive leaders, it provides explainable, consistent decisions and faster scenario analysis for resilience planning. Because TX explains the reasoning behind every recommendation, it is built for teams that need to trust automation before they rely on it. The common motivation across these users is freeing the cash trapped in their supply chain — turning existing data and forecasts into faster, better, and more consistent decisions.