25
less spoilage across 16 DCs
KeHE Distributors modernized its demand planning with Manhattan’s store- and SKU-level forecasting and cut spoilage by 25% across all 16 of its distribution centers.
ActivePlanning
Demand Planning Software That Lends Clarity to the Everyday Chaos
Transportation management software
ActiveTransportation from Manhattan is the company's transportation management system (TMS). It helps shippers move freight on time and at the lowest real cost, whether that's a single load or a global network. It plans, dispatches, tracks, and settles shipments across every carrier and mode.
9
less inventory, no service loss
One retailer’s non-intermittent-demand SKUs, the steady, everyday sellers, carried 9% less inventory on Manhattan’s hybrid forecast, while service to the shelf ticked up.
4
lower inventory investment
A large wholesale distributor already running a tightly tuned forecasting operation still found 4% more inventory to release network-wide after adding Manhattan’s solutions, with service intact.
17
increase in service for intermittent demand SKUs
Manhattan’s solutions enabled one particular retailer to avoid out-of-stocks while mitigating the risk of having more inventory than required.
Demand Changes. Your Forecast Should Keep Pace.
Shifting buying patterns and uneven promotion performance can quickly change inventory needs. ActivePlanning helps planners identify emerging demand changes and adjust forecasts, keeping inventory decisions aligned with current conditions.
See Demand Shifts as They Happen
Demand sensing uses current signals to identify changing patterns. Context such as location, channel, timing, and promotions helps planners understand those shifts and supports forecast adjustments.
Forecasts Adapt as Patterns Change
Advanced AI forecasting combines statistical methods with machine learning to adapt to evolving demand patterns. Automated forecast adjustments help reduce routine maintenance, giving planners more time to focus on exceptions that require business judgment.
Capacity sourcing and procurement features like scenario management and incremental bids ensure spend visibility, standardizing the bid collection process.
Everyone Plans from the Same Forecast
Forecasting, replenishment, allocation, and inventory optimization all work from the same plan, giving planners, merchandisers, and finance teams a consistent source of truth for every decision.
Capacity sourcing and procurement features like scenario management and incremental bids ensure spend visibility, standardizing the bid collection process.
Resources and Downloads
Features
Advanced AI Forecasting
Advanced artificial intelligence (AI) combines proven statistical methods with machine learning to adapt forecasts as demand changes and improve forecast accuracy.
A slow-moving specialty item and a fast-turning bestseller behave differently. ActivePlanning combines statistical forecasting with machine learning and adapts the forecast to each item’s demand pattern as conditions change.
New products do not need years of sales history to get started. When historical demand is unavailable, machine learning identifies patterns from similar items to help establish an initial forecast.
Sightline helps planners review demand signals, compare forecast outputs, and investigate what changed and why, so they can focus on the exceptions that require human attention.
Outside-In Data Integration
Bring internal and external demand signals together to keep demand planning current as conditions change.
Demand sensing combines external signals, such as market trends, competitive activity, economic indicators, social sentiment, weather, and local events, with internal data such as historical sales and seasonal patterns. Together, these signals help planners keep forecasts aligned with changing market conditions.
Your ERP, POS, and CRM systems already contain the transactions, sales history, and customer behavior that inform demand planning. ActivePlanning brings that data into a connected environment alongside your own data sources, so planners can work from current information across systems.
Planners get the context they need for demand forecasting, inventory planning, and broader supply chain planning without manually piecing the story together across separate systems. One view contains historical sales, inventory levels, production schedules, past forecast performance, and external demand signals.
Multi-Echelon Inventory Optimization (MEIO)
Position inventory across the network while balancing availability, service levels, and inventory investment.
Multi-echelon inventory optimization helps planners set and balance inventory targets across locations such as distribution centers, warehouses, and stores, positioning stock where it best supports demand and service goals.
Simulate service-level and safety-stock strategies to understand the trade-offs before applying a plan.
Planners can see how service-level targets affect safety-stock investment in dollars and units, use velocity classifications such as A, B, and C, and compare the results across simulated strategies.
Service Investment Strategies use what-if analysis to let planners simulate and compare service-level and inventory-investment scenarios in real time. Teams can review how each option affects service performance and safety-stock investment before applying a strategy.
The Only One Recognized as a Leader in All Four
“Provides great visibility and is great for cost reducing feature such as route optimization and load consolidation.”
Gartner Operations Manager - Media
Progress and Results
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
On-time performance
“Provides great visibility and is great for cost reducing feature such as route optimization and load consolidation.”
Ann-Marie Daugherty
Vice President Of Logistics | Giant Eagle
Built for Your Role
Fulfill Your Potential With Manhattan
See how companies like yours use the TMS
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Demand Forecasting
Frequently Asked Questions
Demand planning software forecasts how much of each product customers are likely to buy by location, channel, and time period. Manhattan uses statistical forecasting methods and machine learning to build that forecast, then connects it to replenishment, allocation, and inventory planning in ActivePlanning.
Demand forecasting estimates future demand. Demand planning uses that forecast to guide decisions about inventory, replenishment, allocation, and other supply chain planning activities. ActivePlanning connects those steps in one planning process.
Demand planning provides the forecast that informs other supply chain decisions. In ActivePlanning, that forecast supports allocation, replenishment, and multi-echelon inventory optimization, so teams can work from the same view of expected demand.
No. Sales forecasting typically focuses on expected revenue, while demand forecasting estimates product demand by SKU, location, channel, or time period. Demand planners use that operational forecast to support inventory management and other supply chain decisions.
Yes. More accurate demand forecasts help planners align inventory levels with expected demand. Multi-echelon inventory optimization then helps position inventory across the network based on those forecasts and service goals.
No. S&OP is a cross-functional planning process that brings sales, finance, and operations together around a shared plan. Demand planning provides one of the core inputs to that process by establishing the demand forecast.
Your Logistics Partner.
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