Predicting Part Lead Times: Big Data Techniques

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Silas Walsh
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1 Predicting Part Lead Times: Big Data Techniques 27 April 2016 LMI

2 Table of Contents Background The Problem Big Data and Machine Learning Implementation Requirements Potential Pitfalls 2

3 Background Big data is the next big buzzword, but concrete application examples are sparse LMI conducted a research and development project for DLA WSSP, improving estimates of production lead time(plt) using big data techniques PLT is used by DLA to schedule orders, determine order quantities, and assign safety stock levels Current estimation methods use weighted averages of past PLTs to estimate what PLT would be for an order made today Inaccurate estimates result in delivery delays and increased inventory costs 3

4 Performance (order frequency) Improvement for items purchased least frequently Error of Estimation Methods (FY14-15) # NIINs 1/3 Rule 2/3 Rule R&D Model 80 Average Absolute Error Maintain performance for most frequently purchased items 10 0 Less than Annually Annually Bi-Annually Quarterly Monthly Bought Less Frequently Bought More Frequently 4

5 The Problem Spare parts catalogs are characterized by a small number of frequently ordered parts, and a large number of infrequently ordered parts The simplest estimation approach assesses each part individually Parts ordered infrequently have limited history on which to base estimation Other standard approaches would require consistent order history Ideally want a modeling approach that accommodates highly variable data 5

6 Big Data and Machine Learning Big data: a collection of tools, platforms, and techniques to squeeze out useful information from extreme quantities of data Machine learning: technique that uses algorithms (computer processes) to build a model rather than analysts pre-defining relationships Doesn t presuppose any explicit relationships, but instead dynamically identifies relationships from the data Allows the construction of more complex and nuanced models with less effort 6

7 Big Data Applied to PLT Estimation Our approach was to use gradient boosted regression to: Pool the entire catalog to identify similar items and take advantage of correlations in behavior Account for systemic trends across DLA s system Supplement the amount of data available to predict any one part s PLT More valuable for items with limited history Includes items never purchased before 7

8 Implementation Requirements Enterprise Business Intelligence System capable of delivering quantities of valid data on the problem Statistical and/or big data packages SAS, SPSS, HADOOP, etc. Analysts who understand the business process to be modeled and the data connections within your Enterprise Business Intelligence System Essential that the analysts understand your business enough to identify false or misleading correlations 8

9 Potential Pitfalls Big data does not identify cause-effect relationships Care must be taken in using results to make process improvements Training set and test set Complex analysis methods can quickly fit to a set of data, without having predictive power Consider the example, if we predict out in time on the green line we will predict a dramatic reduction in value Define a test set that can be used to assess the model s performance Linear Regression Over Training Example Complex Prediction Raw Data Next Value

10 Other Potential Uses of Big Data Buyer Assistance Systems Identify similar products procured recently Predict Fair and Reasonable Pricing Identify potential bad vendor behaviors (e.x. Cage Hopping) Identify potential no quote solicitations early Identify vendors who may have the capability to make a part but have not bid Demand Forecasting Link Maintenance data and Maintenance plans to drive procurement decisions Predict buy/build arounds Predict effects of OPTEMPO changes and deployment cycles 10

Cost and Pricing Overview

DEFENSE LOGISTICS AGENCY AMERICA S COMBAT LOGISTICS SUPPORT AGENCY Cost and Pricing Overview WARFIGHTER FIRST PEOPLE & CULTURE STRATEGIC ENGAGEMENT FINANCIAL STEWARDSHIP PROCESS EXCELLENCE 1 Agenda Better