Inventory Optimization

Taking inventory management to the next level

Machine-learning and automation deliver significant top- and bottom-line returns across the three-tiers of alcohol beverage.

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Inventory optimization takes inventory management to the next level, making you more competitive

Inventory optimization uses sophisticated machine-learning intelligence to power accurate demand forecasting and planning. At BeverageX, we mine big datasets and adjust forecasts as trends happen in real-time. When combined with Predictive Orders, retailers have daily access to precise re-order quantities by SKU and location—optimized individually for cash flow, margin, and service level returns.

What makes us better than the competition?

Our team has over 40 years of combined supply chain management experience spanning retail, healthcare, manufacturing, and wholesale industries. In our experience, there are two critical factors that should be hard-wired into an inventory optimization solutions:

1. Supply chain data transparency
2. Robust machine learning/AI algorithms

Supply chains that share data experience rapid improvements in profit.

We offer a stand-alone solution that powers transparent data sharing across the supply chain called Unified Supply Chain Management. This solution allows retailers to connect with distributors (and suppliers) and share real-time inventory data.

Research on successful companies such as Toyota, FedEx, and Walmart backs up our recommendation: having a unified supply chain results in significant cash flow & margin improvements .

Our machine learning algorithms outperform traditional methods.

We start by analyzing each stock unit at each location to determine when to buy and at what quantities. This involves looking at all units together, and then assessing which single extra unit will create the highest return.

Next, we prioritize all SKUs based on their incremental contribution margin. In doing so, we weigh financial drivers like gross margin, inventory carrying costs, stock out penalties, etc. and assess how each driver adds profits to each unit.

When complete, our algorithms have optimized reorder quantities based on an uncertain future and across a multitude of profit-driven parameters—automated for you and updated daily.

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