Predicting parts needed for an onsite repair using expected waste derived from repair history
First Claim
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1. A method executed by a computer, comprising:
- determining, by the computer, costs of mis-predicting parts that may be replaced during an onsite repair of a product in response to a repair history, wherein the costs are computed based on probabilities of over-predicting and under-predicting the parts;
selecting, by the computer, a subset of the parts to be sent to the onsite repair in response to the costs; and
selecting, by the computer, another subset of the parts for training of call qualifiers in response to the costs.
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Abstract
A method for predicting parts for onsite repair which takes into account a repair history and the costs associated with mis-predictions. Parts for onsite repair of a product are predicted by determining an expected waste for one or more parts of the product. The parts having a lowest expected waste are selected and sent to the onsite repair. The expected waste indicates parts that are responsible for high support costs and highlights the mistakes being made and scores the mistakes by actual cost.
31 Citations
26 Claims
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1. A method executed by a computer, comprising:
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determining, by the computer, costs of mis-predicting parts that may be replaced during an onsite repair of a product in response to a repair history, wherein the costs are computed based on probabilities of over-predicting and under-predicting the parts; selecting, by the computer, a subset of the parts to be sent to the onsite repair in response to the costs; and selecting, by the computer, another subset of the parts for training of call qualifiers in response to the costs.
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2. A method executed by a computer, comprising:
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determining, by the computer, costs of mis-predicting parts that may be replaced during an onsite repair of a product in response to a repair history, wherein the costs are computed based on probabilities of over-predicting and under-predicting the parts; selecting, by the computer, a subset of the parts to be sent to the onsite repair in response to the costs; and determining, by the computer, which personnel to target for additional training in response to the costs. - View Dependent Claims (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. An apparatus having a computing device that determines costs of mis-predicting parts that may be replaced during an onsite repair of a product in response to a repair history and that selects a subset of the parts to be sent to the onsite repair in response to the costs,
wherein the costs are computed based on probabilities of over-predicting and under-predicting the parts, wherein the computing device computes the costs based on the probabilities by determining numbers of times that the corresponding parts were under-predicted and numbers of times that the parts were over-predicted and numbers of times that the corresponding parts were correctly predicted, the repair history containing the numbers of times that the corresponding parts were under-predicted, the numbers of times that the parts were over-predicted, and the numbers of times that the corresponding parts were correctly predicted.
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26. A method executed by a computer, comprising:
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determining, by the computer, costs of mis-predicting parts that may be replaced during an onsite repair of a product in response to a repair history, wherein the costs are computed based on probabilities of over-predicting and under-predicting the parts; and selecting, by the computer, a subset of the parts to be sent to the onsite repair in response to the costs, wherein determining the costs of mis-predicting comprises determining expected wastes for the corresponding parts, wherein each expected waste is computed based on a number of times the corresponding part was under-predicted, a number of times the corresponding part was over-predicted, a number of times the corresponding part was correctly predicted, a cost of over-predicting the corresponding part, and a cost of under-predicting the corresponding part, wherein the repair history contains the number of times the corresponding part was under-predicted, the number of times the corresponding part was over-predicted, and the number of times the corresponding part was correctly predicted.
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Specification