A Data-Driven Approach to Expedited Shipping

We return to a few simple formulas this week in HOW INSIGHTFUL . In the fast-paced world of retail, supply chain efficiency is paramount. One key metric that can significantly impact this efficiency is the Projected Daily Sales (PDS) number. This number can help us make informed decisions about whether to expedite shipping for late purchase orders.

Calculating Projected Daily Sales

The PDS can be calculated using historical sales data, current inventory levels, and demand forecasts. Here’s a simplified example:

Let’s say we have a product that has been selling 100 units per day on average. If we have 500 units in stock and we expect the demand to remain constant, our PDS would be:

PDS = frac{Current Inventory}{Expected Days of Demand} = frac{500}{5} = 100PDS=Expected?Days?of?DemandCurrent?Inventory=5500=100

Expedited Shipping Decision

If a purchase order is late, we need to determine if the cost of expedited shipping is justified by the potential increase in sales. If the PDS is significantly higher than our current inventory level, it might be worth it to expedite shipping to avoid stockouts and lost sales.

Estimating Lift from New Arrivals

When new stock arrives, we can expect a lift in sales. This lift can be estimated by comparing the sales when stores have the product in stock versus when they are waiting for the stock.

For example, if stores in the East have the product and are selling 120 units per day, while stores in the West are waiting for the stock and selling only 80 units per day, the estimated lift from the new arrival would be:

Lift = Sales_{Have} - Sales_{Waiting} = 120 - 80 = 40Lift=SalesHave?SalesWaiting=120?80=40

This lift can then be summed up to a department level, with contributions by style, to get a more comprehensive view of the impact of new arrivals on sales.

An Actual Example

Let’s consider a real-world example. Suppose we have a popular style of jeans that sells 200 units per day when in stock. We have 1000 units in stock, and we expect the demand to remain constant for the next 5 days.

However, our next shipment of 5000 units is delayed and won’t arrive for another 10 days. If we expedite the shipping to get the stock in 5 days, it will cost us an additional $5000.

Our PDS is:

PDS = frac{1000}{5} = 200PDS=51000=200

If we don’t expedite the shipping, we will be out of stock for 5 days, resulting in lost sales of:

Lost Sales = PDS times Days Out of Stock = 200 times 5 = 1000Lost?Sales=PDS×Days?Out?of?Stock=200×5=1000

If the profit margin on the jeans is $10, we stand to lose $10,000 in profit. In this case, it would be worth it to expedite the shipping, as the cost of expedited shipping is less than the potential lost profit.

By using data-driven approaches like these, we can make more informed decisions and improve the efficiency of our supply chain.

Remember, every decision we make in the supply chain has a ripple effect throughout the entire organization. So, let’s make those decisions count!