What does this have to do with anything? As you can see, every 1% improvement in service level becomes exponentially more expensive, and the difference between 95% and 97% is significantly more than 85% and 87%. But retailers want high service levels to maintain customer loyalty. And so often, when service levels are set to meet these goals, high inventory and additional costs are the outcomes.
But why wouldn't people then pull back the service level to 95% or 90%?
Well, in some cases, they do. But when and what is that trigger? What is the "right" inventory level to maximize profitability across the enterprise, region, or store? And shouldn't that service level change over time to optimize the inventory and profitability?
These are the questions we ponder. How do you connect financials to inventory? Or conversely, how can you connect your replenishment decisions to financials? Rather than optimizing service levels and keeping your inventory levels in check, you focus on maximizing profit with the same amount of inventory investment. Strategically, you would want to designate a higher preference for some items. But imagine the flexibility you gain as your service levels, order points, and order-up-to points adjust to meet your financial goals.
There are other benefits to this approach too. When retailers tighten their belts, they can lower inventory investment while maintaining profitability. If they want to invest in a location, they could increase their inventory levels to see if they can increase profitability.
Much of this approach requires forecasting demand and predicting outcomes. After all, it requires data crunching, analysis, and AI and machine learning. But herein may be a concern for retailers. Wouldn't this approach be restricted if a good percentage of a retailer's assortment uses min/max settings for replenishment?
Simply, yes. But let's back up some.
Even before this approach, min/max replenishment settings were never ideal. Generally, they are used for slow-moving items where forecast predictability is questionable. But today, forecasting enhancements have increased the forecast accuracy of slow-moving items. "Dynamic aggregation" is one method we use at antuit.ai to help retailers and consumer packaged goods (CPG) companies scour the data and determine the right level to forecast.
Additionally, our demand calculations include non-traditional leading indicators such as weather, pollen, or local events that can generate more responsive and accurate forecasts. It opens the door to items and categories that people once considered unforecastable – this isn't your dad's forecasting tool.
Driving retail decisions through financial metrics is not a foreign concept, as markdown optimization solutions have applied this convention for years. Rather than using predefined discount levels at specific times (25% at 12 weeks, 50% at 16 weeks), markdown optimization solutions use sell-through and margin goals to determine when and how deep to discount. The same financial decision logic also has found its way into allocation decisions, where markdown costs and lost sales are evaluated as part of the inventory allocation decision. Even size profile optimization solutions consider financial benefits and pitfalls as shoppers push for more size inclusion.
Most traditional technology systems automated an existing process. But as technology advanced and external market conditions have changed, new systems have brought a fresh approach to solving an old problem. Due to AI advancements and new data sources, rapidly shifting market replenishment systems are ready to take that next leap.
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