"Prediction is very difficult, especially without a past."

About us

Bravo Lucy is a Norwegian supply chain analytics company and the sister company of Molde Analytics (, our development center based in Hyderabad, India. Bravo Lucy brings to the market state of the art processes and software solutions for efficient analysis supporting decisions ranging from operational to strategic levels. Our unique identifier is our focus on new products where no historic data is available; this makes us stand out from most of the competition where the focus is to make predictions based on the past. Bravo Lucy is based in Norway, but has strong connections to both the academic environment around our founder, Professor Nils Rudi, as well as the sister company in India.

Supply chain


New products are great. This is how firms innovate, renew themselves, gain market share in existing product categories and expand into new product categories. And by being ahead of this innovation, market shares can be increased and one can command a premium in the form of larger margins.

Software for Supply Chain & Retail Analytics are traditionally for stable products. This means that the products have been in the market for a while and sufficient data is obtained to facilitate analysis and make decisions on future quantities.

Traditionally, many companies have no system support for their quantity decisions on new products, and the typical purchase meeting starts without any other input than an analysis of what happened last year or last season, and the expertise of the participants.

We strongly believe (and have proven) that it is possible to make more qualified decisions by improving this process and introducing tools to support this new process. Our offering is not one software system; it is 25% a central system, 25% apps for mobile phones and 50% about the process.

How it works?

The science of BIG data has gained much popularity. With massive collection of data and powerful computing resources, one can find relationships that are virtually impossible for humans to identify. Forecasting new products is on the opposite side. This is the science of NO data. Since the product is not yet released, we do not have sales data – we don’t know how the market will respond to it.

Planning for new products is not mainly about improving efficiency and shaving off cost here and there – as the primary focus for stable products is. The key driver for planning new products is managing demand and risk. In general, quantities of new products will be wrong – either too much, resulting in markdowns to clear inventory, or too little, resulting in stock-outs and lost sales. To do this well, you need to be a gambler – similar to professional sports gamblers who use risk to their favor and gain a competitive edge over their peers.

The basis for the solution is the “Wisdom of crowds”. But – that is not enough. We have developed a process with supporting software and apps.


op8or Logo


An app that collects input
from users in a fun way.  

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App that supports the picture
taking and joining photos with
barcodes and product information. 

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Web app

The heart of the solution –
this is where all the input is
crunched into a solid foundation
for quantity decision.

Our process

Create involves the setting up of a project, and define the product-job-project hierarchy. This can either be done manually or by importing products from a file. This is an admin specific task, and it is absolutely crucial since it lays out the foundation for the rest of the planning cycle.
This step is essential in making product identification easier, and more secure. Assigned barcodes can be printed, and photos for the products/project can be imported for each product individually, or for the entire project if the photo files have the same naming as the product id. Photos can also be taken and linked to products through the iPina app, and finally they can be uploaded through the product upload using url links.
In the Assign step the admin user assigns tasks to users, i.e. this is where the admin decides who will participate giving their opinions, and in what way. There are several ways a user can be invited to give their opinions, e.g. scoring, ranking, numbers or thumbs up/down. After assigning the users, they can be notified through an email alert sent from the application. The email template can be customized in the Settings page.
A product’s demand profile -- which captures both the expected demand and the degree of demand uncertainty -- is the foundation for making quantity decisions. The votes are combined with forecasts for total demand for a product category and how dispersed the demands within the product category tends to be. This is done by using the built-in analytics tools. This is the final step in our Entry-version, and the results from the process can be exported in this step, providing valuable information and data for a purchase meeting.
The Store step is where products are allocated to stores. Based on the collected opinions and the following analysis, the more attractive products can be prioritized for a broader distribution and vice versa. In addition to the distribution of the product, launch date and end date will affect the potential sales, hence this is another adjustment that the admin can do in the Store page.
In the Order step demand is converted into a final purchase figure. By combining a product’s demand profile with its financial parameters, the system will propose a quantity that balances the implications of buying too little with the implications of buying too much. The admin user can run different what-if scenarios and look at the financials before a final decision is made.
The In-season screen is a powerful analytical module that helps identifying problems early in the season, still in time to take actions like markdowns or re-buys, either on product or more aggregated levels. Forecasts and actual order quantities are uploaded to the system before the season starts, and the module is fed with fresh sales transactions once the season starts.
The Post-season step is the final step of our process; it’s time to learn from the past season and find areas for improvement for the next planning cycle. From our experience, companies are getting better and better the more experience they gain, and spending some time analyzing what really happened compared to the pre-season efforts is an important part of the learning and improving experience.