Why Is OTIF Important?
Not complying with your retailers’ OTIF requirements can be very costly, and result in hefty fines. For example, Walmart fines its vendors 3% of the cost of goods sold if the order fails OTIF requirements. In 2016, Target stores increased their OTIF penalties to 5% of the order cost, five times what it previously was. That kind of money adds up!
Having your product in stores according to your sales forecasts and correctly stocking your product helps maintain or even increase your sales and overall profit margins. According to recent Walmart data, poor In-Full performance accounted for $1.7 billion in lost sales during the 2019 fiscal year.
The formula for your OTIF score:
OTIF ( % ) = number of cases on time in full ÷ total number of cases ordered * 100
Note: Orders can be split and still comply. If an order has two separate delivery dates, consider each delivery separately. Check with your purchaser before breaking up orders to make sure they accept split shipments.
The Walmart OTIF Program
Walmart breaks down OTIF into separate metrics, On Time and In Full. Each metric has its own percentage goal that must be met in order to avoid fines. The metrics that apply to you differ based on whether you manage your own transportation (i.e, “Prepaid”) or Walmart picks up the product from your facilities (i.e., “Collect”).
For Prepaid shipping, 98% of your ordered cases must arrive on time. For Collect orders, 98% of your ordered cases must be confirmed on time and ready for pickup by Walmart.
Finally, the In Full percentage goals are across all merchandising alignments so that you must ship in full for 98% of all ordered cases regardless of what department you are in.
Related Resources
Written by The SupplyPike Team
About The SupplyPike Team
SupplyPike builds software to help retail suppliers fight deductions, meet compliance standards, and dig down to root cause issues in their supply chain.
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SupplyPike helps you fight deductions, increase in-stocks, and meet OTIF goals in the built-for-you platform, powered by machine learning.
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