Table of Contents

• Background
  • Forecasting
  • Testing

Background

Forecasting future Solid State Drive (SSD) NPI build costs ($) using historical quotes from Offshore Design/Device Manufacturers (ODM) to enable data-driven financial budget planning. Summary statistics such as mean or weighted mean are used to compute per unit build costs at a product code or program family level.

Forecasting

Cost forecasts are computed for a build unit using variables such as "BOM+MVA Cost" or "Subtotal=NRE+Qty*(BOM+MVA)" in the ODM quote files. Currently, the code supports mean and weighted mean as two options to forecast the variable at a program family, product code or build ID level. The default parameter values are pertaining to the Pegatron ODM in this example but the logic neatly follows for other ODMs such as PTI Taiwan. Refer to the gen_odm_forecast() function in the run.py and config.py to follow along.

Data Type	Parameter	Short Description	Default Value
str	read_file_path	Path of ODM quote file	null
list	ignore_sheets	Sheets in input without quote data to be skipped during processing	['Input', 'MainSheet']
boolean	excel_output	Generate output in Excel or not	True
str	write_file_path	Path where forecast outputs are written	null
str	log_file_path	Path where logged info is written	"odm_quote_forecast/anchored_results/forecasting_log.log"
str	site_name	ODM name to assign to input	'PEGATRON'
list	ww_range_allowed	Range of WWs to filter builds	[202241, 202253]
str	ww_col	Column name in ODM quote file with WWs	'Req WW (WW enterd)'
str	build_status_allowed	Statuses to filter builds	['ACTIVE', 'WIP', 'DONE']
str	level	Drill-down category to generate forecast in addition to program family and ODM site	'Product Code'
dict	ft_method	Column names to forecast and corresponding methods	{'BOM+MVA Cost': ['mean', 'weighted mean'], 'Subtotal = NRE+Qty*(BOM+MVA)': ['mean']}
str	weight_col	Column name in ODM quote file with build quantities needed for weighted mean	'Build Qty'
str	po_col	Column name in ODM quote file with the build's PO number	'PO#'
str	quote_tracking_col	Column name in ODM quote file with the build's quote tracking number	'Quote Tracking #'

Testing

The testing capability helps ensure that the forecasting code is working as it should by comparing the results with those computed by hand. The default parameter values are pertaining to the Pegatron ODM in this example but the logic neatly follows for other ODMs such as PTI Taiwan. Refer to the test.py and config.py to follow along. The following parameters are in addition to the forecasting parameters outlined above.

Data Type	Parameter	Short Description	Default Value
df	ft_true	Dataframe with manually computed forecasts	null
df	ft_test	Dataframe computed using the code	null
list	cols	Category and column to use for comparison	['Product Code', 'Forecast of: BOM+MVA Cost (mean)']