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Reducing Latency in Supply Chain Planning – Seven Ways to Get Started

By Jeff Bodenstab28 Apr 2015

Last week we focused on reducing latency in supply chain planning by moving closer to real-time execution via continuous re-planning. One form of this new strategy is called “Predictive Commerce”. It connects upstream demand sensing with downstream supply chain planning and execution in a single model, leveraging granular demand visibility down to the SKU-Location level.  This week we show seven ways to start driving latency from the supply chain.

Solution: Demand sensing linked to dynamic replenishment 

Description: The use of real-time downstream data to drive better inventory, replenishment, transportation and warehousing decisions.

Example Benefits: As shown in the diagram above, Costa Express used machine telemetry feeds from 3800 kiosks for demand-driven dynamic replenishment. They cut field stock by 20 per cent, grew business through increased customer loyalty, and scaled operations by 200% without adding headcount.

Solution: Transportation forecasting and optimization

Description: Connects demand sensing with truckload optimization to proactively manage capacity requirements. Allocation methodologies convert customer, location, and product demand into lane- and mode-specific shipping projections.

Example Benefits: A large consumer goods company reduced reliance on spot markets, locked in carrier commitments, and provided time to adjust to logistical issues. Improved carrier collaboration, increased intermodal shipment opportunities, and smoothed warehouse resources. Reduced freight cost ~5-10% annually.

Solution: Augmenting SAP APO with increased demand visibility

Description: Leverages downstream data from ERP or a demand signal repository (DSR) for a better account-level forecast and more reliable inventory deployment. Takes advantage of demand streams like order-lines, POS data, and customer warehouse data to provide visibility into future baseline demand and true promotional lift.

Example Benefits: A pharma company reduced latency in planning; improved short to medium-term forecast accuracy, reduced inventory by 15% and dramatically improved planner forecasting productivity by more than 80%.

Solution: Market-driven Vendor Managed Inventory (VMI) Replenishment

Description: Understanding customer demand and inventory levels to maintain agreed-upon service levels. Requires prioritizing items and building advanced truckloads to ensure right product is delivered on time with least amount of assets.

Example Benefits: A Fortune 100 consumer goods company reduced freight cost ~5-10%.

Solution: Directed put away and retrieval for the cold chain

Description: Connects inventory service level with sequencing of put aways and retrieval. Selection criteria of outgoing product, such as quantities of products, expiration dates and blocked status are directed by sequencing logic to ensure the right product is retrieved. Example Benefits: Food manufacturer reduced expired products for perishable items. Improvement in warehouse stock rotation reduced “unsalables” ~5%.

Solution: Market-driven Direct Store Delivery (DSD)—For static and dynamic routing

Description: Focuses on shelf inventory visibility to drive accurate replenishments, reduce last-mile delivery, and increase inventory tracking. Driven by POS data to forecast store demand. Dynamic route optimization synchronizes when and where the truck will deliver. Example Benefits: Executing to predicted demand increases in-stock forecast accuracy by up to 20%.

Solution: Market-driven retail last-mile home delivery with dynamic routing

Description: Redirects routes based on real-time inputs. Demand-driven inventory and route planning identifies where inventory needs to be to fill the order and how to route it at least cost.

Example Benefits: Improved order fulfillment and customer experience and increases in-stock availability. Miles-driven savings up to 15%.

The foundation of any Predictive Commerce application is an integrated, predictive forecasting and dynamic replenishment model. It takes advantage of new ways to capture the demand signal and communicate it to functions such as transportation, inventory or replenishment – improving responsiveness and optimization – and reducing latency.

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