NPI Ramp-Up Curve Generation Using Another Product

Created by Shyam Sayana, Modified on Mon, 11 May at 5:59 AM by Shyam Sayana

TABLE OF CONTENTS

Overview

Overview

The NPI Ramp-Up Curve feature allows planners to generate a realistic adoption curve for a new product introduction (NPI) by referencing the historical performance of an existing product. Instead of assuming a smooth, monotonic growth pattern, this approach captures the real-world fluctuations (increases and decreases) in the reference product's data to produce a more accurate NPI forecast.

How It Works

The ramp-up curve is generated in four steps:

Select a reference product and its input measure — Pull actual data from an existing product's history.
Normalize the data — Convert the reference data into a percentage-based ramp-up curve.
Apply the curve to the Base NPI Forecast — Multiply the base forecast by the ramp-up percentages to get the Final NPI Forecast.
Adjust for Estimated Sales (optional) — If an estimated total sales figure is provided, scale the forecast to match the target.

Step-by-Step Guide

Step 1: Select Inputs

Navigate to the NPI Plan and choose Ramp-up curve → Other Product. Configure the following:

Field	Description
Reference Product	Select a product from the product master. Displayed as `Product ID - Product Description`. Only one product can be selected.
Input Measure	Choose the input measure to use as the basis for the ramp-up curve.
Start From	The beginning of the reference period. Can be a historical or future bucket. This depends on the NPI Planning horizon selected.
End At	The end of the reference period. Must not be before "Start From". The ramp-up period length must not exceed the NPI forecast horizon.

Step 2: Reference Product Data Is Fetched

The system retrieves the selected input measure values for the reference product across the chosen date range.

Example — Reference Product Z, Sales Units:

Month	Product Z Sales Units
Jan 2025	120
Feb 2025	300
Mar 2025	250
Apr 2025	500
May 2025	400
Jun 2025	600

Note: The reference data does not need to be steadily increasing. The system handles fluctuations (e.g., Feb → Mar drops from 300 to 250) to reflect real adoption patterns.

Step 3: Normalize to Create the Ramp-Up Curve

The system identifies the maximum value in the selected range and divides each period's value by that maximum to produce a ramp-up multiplier (as a percentage).

Normalization formula:

Ramp-Up % = Period Value ÷ Maximum Value in Range

In this example, the maximum value is 600 (Jun 2025).

Month	Sales Units	Ramp-Up % (Units ÷ 600)
Jan 2025	120	20%
Feb 2025	300	50%
Mar 2025	250	42%
Apr 2025	500	83%
May 2025	400	67%
Jun 2025	600	100%

Important: Because the reference product data can fluctuate, the ramp-up multiplier can go up and down across periods. This is expected behavior.

Step 4: Apply the Ramp-Up Curve to the Base NPI Forecast

The Final NPI Forecast is calculated by multiplying the Base NPI Forecast by the Ramp-Up % for each period.

Formula:

Final NPI Forecast = Base NPI Forecast × Ramp-Up %

Suppose the Base NPI Forecast for the new product (Product X) is 900 units/month:

Month	Base NPI	Ramp-Up %	Final NPI Forecast
Jan 2026	900	20%	180
Feb 2026	900	50%	450
Mar 2026	900	42%	378
Apr 2026	900	83%	747
May 2026	900	67%	603
Jun 2026	900	100%	900
Total			3,258

Step 5 (Optional): Adjust for Estimated Sales

If the planner provides an Estimated Sales target for the ramp-up horizon, the system scales the Final NPI Forecast so that the total across all periods equals the Estimated Sales value.

How scaling works:

Calculate the initial Final NPI Forecast using the ramp-up curve (as shown in Step 4).
Compute the Adjustment Multiplier:
Multiplier = Estimated Sales ÷ Sum(Initial Final NPI Forecast)
Recalculate each period's forecast:
Adjusted Final NPI Forecast = Initial Final NPI Forecast × Multiplier

Example — Estimated Sales = 2,500 units:

Multiplier = 2,500 ÷ 3,258 = 0.7673

Month	Final NPI (Before Adjustment)	Multiplier	Adjusted Final NPI Forecast
Jan 2026	180	0.7673	138
Feb 2026	450	0.7673	345
Mar 2026	378	0.7673	290
Apr 2026	747	0.7673	573
May 2026	603	0.7673	463
Jun 2026	900	0.7673	691
Total	3,258		2,500

The adjusted totals now sum to exactly 2,500, matching the Estimated Sales target.

Validation & Error Handling

Scenario	System Behavior
If the selected 'Other Product' does not have data for the selected product/measure/date range	Warning: "No data exists in the selected range."
For the selected 'Other Product', if all the values in the range are zero	Warning: "Cannot generate ramp-up curve: reference series has no non-zero values."

Key Concepts at a Glance

Term	Definition
Reference Product	An existing product whose historical data is used to model the NPI ramp-up pattern.
Input Measure	The specific metric (e.g., Sales Units) from the reference product used for curve generation.
Ramp-Up Curve	A time series of multipliers derived from normalizing the reference product's data.
Base NPI Forecast	The flat (unadjusted) forecast for the new product per period.
Final NPI Forecast	The result of applying the ramp-up curve to the Base NPI Forecast.
Estimated Sales	An optional total target; when provided, the Final NPI Forecast is scaled to match this total.
Adjustment Multiplier	The scaling factor is applied when Estimated Sales is specified (Estimated Sales ÷ Sum of Final NPI).

FAQ

Q: Can the ramp-up curve decrease from one period to the next?
Yes. The curve reflects the actual pattern of the reference product, including dips. For example, if the reference product's sales dropped from 300 to 250 between Feb and Mar, the ramp-up % will also decrease (from 50% to 42%).

Q: What happens if I change the Estimated Sales value later?
The system will recalculate the Adjustment Multiplier and scale the Final NPI Forecast accordingly. The shape of the curve remains the same; only the magnitude changes.

Q: Can I edit the ramp-up configuration after saving?
Yes. The system stores the "Other Product" ramp-up configuration (product, input measure, start/end period) and retrieves it when you reopen the plan in edit mode.