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B Appendix: Option Forecast Details

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B

Appendix: Option Forecast Details

Options Forecast is a module within Oracle Retail Artificial Intelligence Foundation (AIF) services, developed to provide advanced analytical support for retail assortment planning. This module leverages Oracle Retail’s robust AI-driven analytics capabilities to transform vast amounts of historical sales data, item lifecycle information, and product attributes into actionable insights for retailers. By examining which attributes and characteristics are associated with high-performing products, Options Forecast helps identify patterns and preferences that drive successful sales outcomes.

The module delivers a comprehensive set of outputs, including Attribute Weights, Optimized History, Total Option Count, Option Count by Attribute Value, and Sales Potential. These outputs help retailers understand which product features most influence customer demand and how historical trends can inform future assortment selections. In addition, Options Forecast performs granular analysis at the store level, evaluating the number of units sold, pinpointing lost opportunities and sales, and highlighting instances of missed or delayed deliveries.

Through these advanced analytics, Options Forecast equips retailers with precise, data-driven recommendations for assortment planning. By identifying growth opportunities and reducing the risk of missed sales, the module enables organizations to strategically optimize their product offerings for upcoming seasons, ultimately supporting better business outcomes and competitive advantage in the retail marketplace.

The Options Forecast methodology within AIF automates key aspects of the Assortment Planning solution by providing strategic recommendations for assortment strategy. This methodology is structured around a systematic run setup, comprising the following steps:

  • Life Cycle: Analyzing the complete lifecycle of products to understand performance trends.

  • Optimize History: Leveraging historical sales data to identify patterns and inform future decisions.

  • Attribute Weights: Determining the relative importance of product attributes that drive sales.

  • Incrementality: Assessing the incremental value each product brings to the overall assortment.

  • Sales Potential: Estimating the future sales potential of various assortment options.

Through this structured approach, Options Forecast enhances the effectiveness and precision of assortment planning decisions. This appendix provides details for the parameters involved in the calculations of each of these steps.

Life Cycle

The life cycle of an item is calculated by identifying when the item first started selling (the start date) and when it stopped selling (the end date) using historical sales data for each store or location. By calculating the number of weeks the item was available for sale, this process helps determine how long each item was actively selling, which is useful for planning future assortments.

August 3, 2026 Appendix B-1 of B-8

Appendix B Life Cycle

Table B-1 Life Cycle Parameters

Parameter NameParameter DescriptionInput Specifications
(Default, Range, and so on)		Parameter Explanation
(AIF)
OPT_LC_EOL_CHECK_WK
S		Number of Weeks to Check
for End of Life Promos/
Stockouts		Default: 4 (weeks)Specifies the number of
weeks to consider when
evaluating end of life
promotions or end of life stock
outs. For example, if it is set
to 8, the system will look at
the sales data for the last 8
weeks of an item’s life cycle
to check if the item had
significantly more promotions
or stock outs than previous
weeks.
OPT_LC_EOL_MIN_SO_PC
T		Percentage Threshold to
Consider End of Life Stock
Outs		Default: 0.3
Range: 0.0 - 1.0		Specifies the minimum
percentage to consider that
week in the end of life window
as a stocked-out. An item is
considered heavily stocked
out when the average
percentage of store count
with inventory (percentage
calculated wrt max store
count with inventory over
entire life) is higher than this
threshold (for example, for
default, 30%).
OPT_LC_ITEMS_STILL_SEL
LING_FLG		Still Selling FilterY/N
Default: Y		Drop items that are still
selling. An item is considered
still selling if it has at least
one positive sale in the last
OPT_LC_EOL_PAST_N_WK
weeks from the end of the
training period. Y means
consider this filter in rule
extraction and N means do
not apply this filter.
OPT_LC_ITEMS_WITH_FE
W_SLS_FLG		Few Sales FilterY/N
Default: Y		Y: Drop items with less than
OPT_LC_MIN_NUM_POS_S
LS_WKS sales data points
(weeks). N: Do Not drop it. Y
means consider this filter in
rule extraction and N means
do not apply this filter.
OPT_LC_LOW_SELLERS_F
LG		Low Sellers FilterY/N
Default: N
Y: Drop items that are low
sellers.
N: Do not apply filter.		This flag controls whether to
exclude items that are low
sellers. Specifically, these are
items that sell less than one
unit per week (fixed value) on
average over all weeks in
their time series. If Y, items
that meet this condition are
excluded from the exit date
estimation process.

August 3, 2026 Appendix B-2 of B-8

Appendix B Life Cycle

Table B-1 (Cont.) Life Cycle Parameters

Parameter NameParameter DescriptionInput Specifications
(Default, Range, and so on)		Parameter Explanation
(AIF)
OPT_LC_MIN_NUM_POS_S
LS_WKS		Minimum Number of Weeks
with Non-zero Sales		Default: 5 (weeks)Minimum number of non-zero
sales weeks an item must
have to calculate the lifespan.
Items with less than this value
are not considered for exit
dates rules generation.
OPT_LC_RECENCY_IN_WK
S		Item Recency Minimum
Weeks		Applied with
OPT_LC_RECENT_ITEMS_
FLG.
Default: 8 (weeks)
For example, if an item has
had its first sale in the past
eight weeks, exclude from
exit date estimation.		Specifies the number of
weeks to consider when
determining whether an item
is recent. Items that have
their first sale within this
number of weeks from the
end of the training period are
considered recent and may
be excluded from the analysis
if
OPT_LC_RECENT_ITEMS_
FLG is Y.
OPT_LC_RECENT_ITEMS_
FLG		Recent Items FilterY/N
Default: Y
Y: Drop items that started
selling recently, that is, that
started selling less than
OPT_LC_RECENCY_IN_WK
S ago (for example, 7 weeks
ago).
N: Do not apply filter.		Controls whether to exclude
items that have recently been
introduced. If Y, items that
have their first sale within
OPT_LC_RECENCY_IN_WK
S weeks from the end of the
training period are excluded
from the exit date estimation
process.
OPT_LC_RETURN_SLS_FL
G		Only Returns FilterY/N
Default: N
Y: Drop sales entries of items
if they are exclusively returns.
N: Do not apply filter.		Controls whether to exclude
sales entries that are
exclusively returns, as returns
can artificially extend the
lifespan of an item.
OPT_LC_SLS_AFT_98_PRC
_FLG		98% Cumulative Percentage
Filter		Y/N
Default: Y
Y: Exit date is estimated as
the week when the
cumulative sales reach 98%
of the total sales.
N: Exit date is estimated as
the last week with sales.		Determines the method used
to estimate the exit date for
an item. If Y, the exit date is
estimated as the week when
the cumulative sales reach
98% of the total sales. If N,
the exit date is estimated as
the last week with sales.
OPT_LC_SLS_BFR_FST_R
CPT_FLG		Sales Before First Receipt
Filter		Y/N
Default: Y
Y: Sales that occur before the
first receipt week ID are
excluded from exit date
estimation.
N: Do not apply filter.		Controls whether to exclude
sales that occurred before the
first receipt week ID. For
example, if an item was first
received in week 10, and
OPT_LC_SLS_BFR_FST_R
CPT_FLG is Y, any sales data
for that item prior to week 10
will be excluded from the
analysis.

August 3, 2026 Appendix B-3 of B-8

Appendix B Optimize History

Optimize History

Optimized History is a careful process that helps retailers get a true picture of product demand by correcting sales data for issues related to inventory, such as stockouts (times when products were out of stock) and limited variety on the shelves. This allows retailers to answer important questions such as, “What would our sales have looked like if we always had enough of each product available?”

Optimized History is computed both at the item (stylecolor) and attribute level in two separate algorithms. This allows AIF to produce predictions for both how a specific stylecolor would optimally sell, and how any item possessing a certain attribute (that is, being of color grey, or brand Nike) would sell.

Using this data, a statistical model is trained to understand overall customer demand and how much sales are affected when inventory is limited, for each attribute or item. This model helps adjust both the reported sales and the number of product options (option count), reflecting what would have happened if inventory was never an issue. The result is a much clearer and more accurate foundation for planning future assortments, allowing retailers to better decide how much variety in color, style, or material they should carry, even when planning for products that have not been sold before. This ensures future predictions and assortment choices are based on real customer preferences, not just what happened to be in stock in the past.

Table B-2 Optimize History Parameters

Parameter NameParameter DescriptionInput Specifications
(Default, Range, and so on)		Parameter Explanation
(AIF)
OPT_OH_ITEMS_MISSING_
SCWI_FLG		Missing Store Count with
Inventory Filter		Y/N filter for data cleaning.
Default = Y
Y: Drop items with more than
OPT_OH_ITEMS_MISSING_
SCWI_PCT% having zero or
null store_count_with_inv
values.
N: Do not apply filter.		This flag controls whether to
exclude items with a high
percentage of missing or zero
Store Count With Inventory
(SCWI) values. If Y, items
with more than
OPT_OH_ITEMS_MISSING_
SCWI_PCT percentage of
weeks having zero or null
SCWI values are excluded,
indicating that they may have
unreliable inventory data and
may not be suitable for
optimize history corrections.
OPT_OH_ITEMS_MISSING_
SCWI_PCT		Missing Store Count with
Inventory Percentage		Continuous filter for data
cleaning, paired with
OPT_OH_ITEMS_MISSING_
SCWI_FLG.
Range: 0.0-1.0, Default: 0.2
A higher threshold value
(such as 0.5 or 50%) will
result in more lenient filtering,
including items with a larger
percentage of missing or zero
store_count_with_inv values.		Maximum percentage of
sales weeks where store
count with inventory
(store_count_with_inv column
in pmo_options_activities)
can be either zero or null.
Default is 20%, which means
that items with more than
20% of sales weeks having
zero or null
store_count_with_inv values
will be excluded.

August 3, 2026 Appendix B-4 of B-8

Appendix B Optimize History

Table B-2 (Cont.) Optimize History Parameters

Parameter Name Parameter Description Input Specifications Parameter Explanation (Default, Range, and so on) (AIF) OPT_OH_ITEMS_STILL_SE Still Selling Filter Y/N filter for data cleaning. This flag controls whether to LLING_FLG exclude items that are still Y: Drop items that are still selling (drop items with sales actively selling. If Y, items in the last with sales in the last OPT_LC_EOL_PAST_N_WK OPT_LC_EOL_PAST_N_WK weeks). weeks are excluded, indicating that they may not N: Do not drop items that are have reached the end of their still selling. life cycle and may not be Default = N suitable for optimize history corrections. OPT_OH_ITEMS_WITH_FE Few Sales Filter Y/N filter for data cleaning. This flag controls whether to W_SLS_FLG exclude items with limited Y: Drop items with less than OPT_LC_MIN_NUM_POS_S sales data. If Y, items with LS_WKS weeks where sales fewer than OPT_LC_MIN_NUM_POS_S are positive, in which the default is LS_WKS weeks of positive OPT_LC_MIN_NUM_POS_S sales are excluded, indicating LS_WKS=5 weeks. that they may not have sufficient data for reliable N: Do not drop items meeting optimize history calculations. this condition. Default = N OPT_OH_MAX_WKS_APPL Maximum Weeks to Apply Default = 4 Maximum allowed difference Y_ED_RULE Exit Date Rule For example, with in weeks between the OPT_OH_MAX_WKS_APPL calculated exit date and Y_ED_RULE=4, if the actual life of an item. If the estimated exit week is 9 difference is higher than OPT_OH_MAX_WKS_APPL weeks beyond the expected life cycle (defined here as Y_ED_RULE, exit date rules are not used to define this start week + weeks to exit), item’s end of life in the the rule will not be applied because 9 > 4. optimize history estimation

Maximum allowed difference in weeks between the calculated exit date and actual life of an item. If the difference is higher than OPT_OH_MAX_WKS_APPL Y_ED_RULE, exit date rules are not used to define this item’s end of life in the optimize history estimation phase.

This parameter controls the tolerance for the difference between the estimated exit week ID and the actual life of the item. If the difference is too large, the extracted exit date rule is not applied, indicating that the rule may not be reliable for optimize history corrections.

August 3, 2026 Appendix B-5 of B-8

Appendix B Attribute Weights

Table B-2 (Cont.) Optimize History Parameters

Parameter NameParameter DescriptionInput Specifications
(Default, Range, and so on)		Parameter Explanation
(AIF)
OPT_OH_RECENT_ITEMS_
FLG		Recent Items FilterY/N filter for data cleaning.
Y: Drop items that started
selling recently, that is, that
started selling less than
OPT_LC_RECENCY_IN_WK
S ago (for example, 7 weeks
ago).
N: Do not drop it.
Default = N		This flag controls whether to
exclude items that have
recently been introduced. If Y,
items that have their first sale
within
OPT_LC_RECENCY_IN_WK
S weeks from the end of the
training period are excluded,
indicating that they may not
have sufficient historical data
for reliable optimize history
calculations.
OPT_OH_ATTR_CORR_THR
ESHOLD		Optimize History Model TypeRange: 0.0 - 1.0
Default: 0.9		Threshold of sales
percentage to compute
Optimize History by Attribute
corrections. 90% means that
for a given Attribute, top
Attribute Values that
cumulatively contribute 90%
of the total sales will be
corrected.

Attribute Weights

Attribute weight refers to a numerical value assigned to each product attribute, representing its relative importance in determining product similarity within a retail assortment. Products are typically described by several attributes, such as brand, color, collar type, neckline, and sleeve length. However, these attributes do not all contribute equally when assessing how similar two products are. For example, brand and color may have a greater influence on perceived similarity compared to collar type or sleeve length. Attribute weights serve to quantify this difference in importance by assigning higher weights to more influential attributes.

Incrementality

An incrementality curve is a curve that models projected sales across a range of option counts within an assortment. This curve is generated by simulating sales outcomes using estimated arrival rates (which reflect seasonality), product attractiveness scores, and store-specific assortment data. The incrementality curve illustrates the incremental sales lift associated with each additional option introduced to the assortment. By depicting the relationship between the number of available options and the corresponding incremental sales, this curve enables retailers to evaluate the marginal benefit of assortment expansion and supports data-driven assortment planning decisions.

August 3, 2026 Appendix B-6 of B-8

Appendix B Sales Potential

Table B-3 Incrementality Parameters

Parameter NameParameter DescriptionInput Specifications
(Default, Range, and so on)		Parameter Explanation
(AIF)
OPT_IC_MARKET_SHAREMarket ShareDefault = 0.5
Range: 0.0-1.0		Specify the retailer market
share, which is the fraction of
all potential customers
expected to make a purchase
when all products are
available. Lower market share
means more customers
choose not to purchase;
higher market share means
more customers shop with
the retailer.

Sales Potential

Sales potential is an estimated measure of the expected sales that a product or assortment can achieve within a specific period. This metric leverages factors such as historical sales data, product attributes, and product category to forecast likely sales outcomes. Primarily, it models information on product sales and attributes, as in color, brand, and so on, to understand how well certain attributes, as well as combinations of attributes, sell. Sales potential helps retailers identify high-opportunity products or attribute combinations and support more accurate, data-driven planning.

Table B-4 Sales Potential Parameters

Parameter NameParameter DescriptionInput Specifications
(Default, Range, and so on)		Parameter Explanation
(AIF)
SP_MAX_ATTR_VAL_DOMI
NATION		Maximum Attribute Value
Domination		Range: 0.0 - 1.0
Default: 0.5		Specifies the maximum
percentage of items an
attribute value can dominate
for a given attribute to be
included in training. For
example, if all items have a
single attribute value, that
attribute is not useful for
model training. A value of 0.5
means that if an attribute
value is present in more than
50% of the items, the attribute
will be dropped.
SP_MIN_ATTR_FREQUENC
Y		Minimum Attribute FrequencyRange: Integer value >= 1
Default: 1		Specifies the minimum
number of items that must
have a non-null value for an
attribute to be included in
training. For instance, if only
three items have sleeve-
length populated, then
sleeve-length will be dropped
from model training if
SP_MIN_ATTR_FREQUENC
Y is greater than 3.

August 3, 2026 Appendix B-7 of B-8

Appendix B Sales Potential

Table B-4 (Cont.) Sales Potential Parameters

Parameter NameParameter DescriptionInput Specifications
(Default, Range, and so on)		Parameter Explanation
(AIF)
SP_MIN_ATTR_VAL_DIVER
SITY		Minimum Attribute Value
Diversity		Range: Integer value >= 2
(recommended)
Default: 2		Specifies the minimum
number of unique attribute
values required for an
attribute to be included in
training. Attributes with low
cardinality (that is, fewer
unique values) may not be
useful for model training.
SP_MIN_LIFECYCLE_WIND
OW		Minimum Lifecycle WindowRange: Integer value >= 1
Default: 1		Specifies the minimum
historical lifecycle length in
weeks for an item to be
included in training. Items
with a short lifecycle may not
provide enough data for
effective model training.
SP_MIN_MAX_STORE_CNT
_WITH_INV		Minimum Value for Max Store
Count with Inventory		Range: Integer value >= 1
Default: 1		Specifies the minimum value
for the maximum store count
with inventory for an item to
be included in training. Items
with low inventory levels
across stores may not be
representative of the overall
sales pattern.
SP_MIN_SALES_UNITSMinimum Sales UnitsRange: Integer value >= 1
Default: 1		Specifies the minimum
amount of sales units
required for an item to be
included in training. Items
with very low sales may not
provide enough data for
effective model training.

August 3, 2026 Appendix B-8 of B-8

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