3 Basics for Inventory Optimization

The term “inventory optimization” might conjure any number of cloud-based apps that make inventory management tasks easier, or offer simplistic models for calculating stock levels and reorder points. Too often, these optimization tools fail to deliver because they’re not actually statistically optimizing stock levels, nor inventory policies. In essence, they’re not applying advanced statistical analysis to mathematically derived demand forecasts.

These pared-down approaches to true inventory optimization can be an organizational hazard, especially for companies facing short planning and product cycles, or increasingly sophisticated markets. They fall short when trying to manage stocking locations from multiple distribution centers, or within Multi-Echelon stocking and distribution schemes.

What is true inventory optimization?

True inventory optimization is the optimal alignment of demand, sourcing, production scheduling, storage, and distribution. The aim is two-fold:

  1. Get the right items in the right amounts to the right places at the right times.
  2. Meet and exceed customer service, revenue, and profitability goals.

Note, the price of inventory optimization, especially when pulling in demand and supply volatility, is unrelenting vigilance. This is best handled by artificially intelligent systems, not people.

To truly optimize inventory, take advantage of the following three best practices as a starting point: remember that sound, statistical inventory optimization is a key component of any wider supply chain optimization effort:

  1. Start with forecast & optimization engines. These back-end software tools are the basis for statistical analyses of your entire inventory cycle. A modern statistical forecast engine should auto-select the best possible forecast method per data instance, factor trend seasonality and outliers, and make room for knowledge workers to easily layer in information that is not yet in the historical data. Next, a formidable optimization engine can compare product profitability, channel profitability, carrying cost, and other factors to calculate optimal service levels, stock levels, replenishment schemes, and other operating policies.
  2. Set Key Performance Indicators (KPIs). Set KPIs holistically: assess all metrics, not in isolation, but in how each performs as part of a collective. The goal is not to maximize the performance of each metric, but to maximize the net performance of the organization as a whole. A global thought process and design is the way to go.
  3. Look at the big picture with a balanced scorecard. As we recently posted, it’s important to consider your organization’s learning and growth, internal business processes, customer service, and financial performance.

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Vanguard Software

About Vanguard Software

Vanguard Software introduced its first product for decision support analysis in 1995. Today, companies across every major industry and more than 60 countries rely on the Vanguard Predictive Planning platform. Vanguard Software is based in Cary, North Carolina.

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