Inventory Replenishment: Why Push When You Can Pull?

Today‘s supply chains are undergoing significant pressures to become more demand-driven. Retailers, distributors, and manufacturers must choose the approach they hope will make the most profit. Should you produce goods according to demand forecasts, or by reacting to what consumers already bought? Companies are investing in a new generation of cloud technologies that enable the transition from push to pull environments.

The Push System

Many companies use the forecast approach, or what is called a push system. These companies forecast and feel confident goods will not run out unexpectedly. In the push world, decision points occur at every reorder. How much should be purchased? How often?

The sheer volume of SKUs and associated decision points mean push systems use the peanut butter approach, where all products are treated roughly the same despite different demand profiles. Thus, we see the following: Forecasting gets done at the aggregate level. Product is then pushed to the store weekly, without accounting for how individual SKUs sell for a particular store.

The problem is, forecasts that drive push systems are often wrong. Despite billions spent annually on technology, actual demand varies from forecasts. Forecasting does not make the end consumer react predictably.

No matter how sophisticated its algorithm, a forecast is a guess. Wrong guesses mean excess investment and lower profits. They also lead to other problems like high carrying costs, discounting, disposals, missed sales, weak customer loyalty, shortages, high debt loads, inventory disposals, emergency shipments, rescheduled production and attenuated profits.

The Pull System

Modern cloud-based technologies are enabling a pull-based approach to retail replenishment that uses daily consumer-level demand to generate a true forecast.

Pull systems use demand data to drive both replenishment and production. Only immediate customer requirements are drawn from the protective inventories upstream. This approach is driven by actual consumption at the store (store/SKU/daily demand) as well as with forecasts. This allows for a much more granular approach than push systems.

For example, a pull network supports multiple replenishment policies based on the individual demand profile of the product. For a slow selling product, you can manage by a simple reorder point (sell one, replenish one). For a turn item, however, you can use a more sophisticated minimum/maximum policy.

The result is an automated inventory policy driven by actual pull requirements at the granular level. By acting on actual demand, statistical variations are damped rather than magnified, steadying on-hand inventory levels at every stocking location. Since goods only flow downstream to cover immediate need, inventory remains further up the supply chain, closer to the source. In contrast, many push systems put the majority of the inventory at the retail store.

Many organizations are implementing demand-driven value network (DDVN) processes. Companies that have implemented a pull-based network have seen the following benefits:

  • As much as 30 percent inventory reduction
  • 20 percent increase in perfect orders
  • 10 percent increase in revenue
  • Improved service levels
  • Forecast Accuracy Improvements


Retailers who use push systems end up with more inventory than they need to cover immediate consumption. As a result, the biggest accumulation of inventory in a push supply chain resides at the retail node.

The pull system is the real key to supply chain savings. Using readily available point-of-sale data as inputs, shortages can be reduced due to the quick response nature of these flow systems. Customers will more often find what they need, product will be continuously replenished, and consumers are more likely to stay loyal.

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