Inventory Management

Date : 29-03-19

Value Proposition


Implemented an inventory replenishment system that helped client meet objectives like service goals inventory turn-over ratios.

Impact of the Engagement


There were less frequent stock outs for fast moving high margin products which led to 8% inventory improvement on Annual days. Improved inventory turn-over ratios for slow moving products. Inventory turn-over for these products rose by 9% to 23%.

Inventory Management

Client

Client has a chain of departmental stores across US, offering product categories in fashion, electronics, appliances, home etc.

Trigger – Inventory pile up/Stock out

The client witnessed multiple inventory related issues across different product categories – inventory pileups for slow moving products and stock-outs for high margin fast moving products. Moreover, uncertainty in lead times caused more unreliability in the inventory levels and service levels.

Challenge and Approach

The client faced severe losses due to high inventory holding costs and opportunity cost for the stock out items. The footfall numbers dropped because of pile up of slow moving goods in store and the uncertainty in lead times caused more unreliability in the inventory levels and service levels. Affine’s solution comprised addressing four key aspects critical to designing a foolproof Inventory Replenishment system. • Demand Forecast • Lead Time • Inventory Carrying cost • Ordering cost
Keeping these aspects in mind, Affine implemented a high-performance demand forecasting engine to forecast the demand of each SKU/Store using the best statistical approach. Models were tested for external factors like Macroeconomic factors, Special events, Marketing Spends and the champion model producing the best result was selected. We then implemented a Simulation Engine to efficiently simulate multiple safety stock scenarios using historical data to capture the impact of demand variability and lead time variability on stock-outs. Another engine was then implemented to simulate the impact of various re-order quantities on Gross Margins and inventory carrying costs. A big data based infrastructure was setup to allow for large scale modeling and simulation required to produce the results regularly and quickly.

Outcome – Improved accuracy

  • A holistic inventory replenishment system was built that improved their inventory turn-over ratios, mitigated inventory holding costs.
  • There were less frequent stock outs for fast moving high margin products which led to 8% inventory improvement on Annual days.
  • Inventory turn-over ratios improved for slow moving products and it rose by 9% to 23%.
  • Inventory plans for next season were optimized based on store demand patterns, and learnings from similar but top performing stores.
  • Inventory replenishment system was implemented across all business units in a scalable fashion and in a quick time which produced results regularly and quickly.

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