Pitch IC

(Intro ... I will start with a story)

In 1908, Henry Ford launched the Ford Model T. It was a big success because there was only one customization. As Henry Ford said, "you can buy any color, as long as it is black".

This car was assembled from 3000 parts. To achieve this historical mass production, Supply chain methodology was developed.

We are in 2019, modern cars are personalized and contain more than 30,000 parts. But the same supply chain methodology is still used.

And it has huge consequences: $2 Trillion dollars is lost every year due to overstocks and out of stocks. For example, we talked to Peugeot: several times per year, they use helicopters to ship missing components. Why? Because they are not reactive enough when something happens at their suppliers.

Traditional solutions are considering suppliers as black boxes. With Flowlity, we give recommendations to planners about how much and when to order by looking in real-time at a supplier stocks. And that is what we are currently doing with 3 of the biggest companies in Europe where we target a 30% inventory reduction.

To succeed in building this product, we need two critical skills:

1. Handling vast amount of data and scale machine learning models-and Karim has been doing just this at Criteo and Axa.

2. And we need deep supply chain planning knowledge to build and sell the solution. I have 7 years of experience on this topic.

Our vision is to create a network to adjust stocks automatically across several companies: it will reduce working capital, eliminate wastes and shortages.

Remarques:

• 2 questions super importantes d'Elspeth qu'on retrouve pas encore dans le pitch: What can you do and others can’t. Why no one else is thinking to use the supplier data.

• Why everyone else is busy improving the forecast? faudrait expliquer pourquoi les gens focaisent sur le forecast, et en vrai car il y a plus d'avantages court terme à réaliser avec le forecast quavec les donnes fournisseurs et que c'est bcp plus facile. Faut insiter aussi sur le fait que la supply chain reste un domaine très en retard, et que ça fait que 2 ans qu'ils connaissent le ML et que le forecast c'était la porte simple d'entrée.

• Sur le why now, je dirais plutot un truc: "Many companies tried and failed this last decades to use suppliers data and improve the collaboration. Today, it is possible, with the rise of IOT that provides real-time stock and demand information.d'aileurs tu penses à quoi quand tu dis demand information, comment l'iot à aider ? pour le stock je comprend mais pas pour la demande) That generatees huge amount of data that can be used to do what was impossible before: elimiante wastes, shortages and reduce working capital."

• Why are we the ideal team to do so? on ne comprend pas trop pourquoi on est le mieux placé, à mon avis faut inclure notre présentation juste après la vision. En disant comme dans l'application IC que tu connais parfaitement la théorie de la supply chain et tu as bcp d'exprience dans le domaine qui te permette de comprendre le marché et de proposer le bon produit et que pour créer ce genre de solution on a besoin de quelqu'un qui sait traiter d'enormes quantités de donnée et qui peut scaler des modèles de machine lerning. ⇒ commme ça, ça fera sens dans le tete qu'on est l'équipe idéal car on dit les compétences rares dont y'a besoin et on dit qu'on les a :)

Comments 9/1/2019

Ne pas commencer pas captors, portals, and saas. Pyramid!

Business point of view, why is it the right moment? très floue

push-pull: good (show expertise)

who you are ? what u do ? how u came up with your idea? (pitch)

not ford T mais ford model T

So who are u as a team? faut l'inclure dans le pitch. Faut juste dire me ceo and him cto. (écouter la question)

Journey of the program?

Value you are adding? (floue à revoir) (30% is lot of money)

Concrete numbers?

Roadmap?

is it specific product for akzo?

Why did you wait till now ?

Methodology Total Adresssable market? NE pas dire 80k mais plutot ce qu'à dit CHRIS ! Trop de détail pas besoin.

Biggest risk?

Competitors? vaut mieux dire la différence avec nous. Pas assez clair surtout pour les SAP/JDA.

Defensibility? vaut mieux donner l'example de nos clients plutot que des general statements.

What do you mean by the network effect?

Would you be able to sign exclusivity?

Do you have a strategy to lock the customers? floue encore

go to market: bien mais trop lent

Business model? Pas besoin de parler de data ça rend floue.

For the three years we lock the contract

Marketing? Commencer par le network. "hello we are peugeot" pas sérieux.

Final world: Big potential, we are the right team, we need to go fast, and it's right timing.

Feedback? later by mail

Je ne suis pas fan du pitch au départ. Ca refroidit dès le départ. Trop lent. Parler de ford etc... Vous pouvez entrer plus rapidement dans le sujet.

Tu pars trop dans les généralités. C'est mieux de parler de vrai case studies.

Rapidement définir le network effect.

Bonne compétition + et business model.

Reformuler pourquoi vous n'avez pas coder depuis le début

Plus de confiance sur les chiffres TAM.

Peut etre la partie risk: un peu trop sommaire. Peut être en donner deux.

Sur pourquoi on veut travailler ensemble? Anne marie.

La vision ? absolument à passer. Matt va surement insister sur la vision

Commencer par "We will use some term that we assume everyone knows, and don't hesitate to interrupt us"

Le risque: c'est de ne pas être standard, je le reformulerai pas comme ça (car s'ils ont parlé Matt W, on leur donnera). Plutot parler de la competition, genre un autre acteur qui va plus vite que nous. PArler aussi de nos recrus potentiel

In 1908, Henry Ford launched the Ford Model T. It was a big success because there was only one customization. As Henry Ford said, "you can buy any color, as long as it is black".

This car was assembled from 3000 parts. To achieve this historical mass production, Supply chain methodology was developed.

We are in 2019, modern cars are personalized and contain more than 30,000 parts. But the same supply chain methodology is still used.

And it has huge consequences: $2 Trillion dollars is lost every year due to overstocks and out of stocks. For example, we talked to Peugeot: several times per year, they use helicopters to ship missing components. Why? Because they are not reactive enough when something happens at their suppliers.

Traditional solutions are considering suppliers as black boxes. With Flowlity, we give recommendations to planners about how much and when to order by looking in real-time at a supplier stocks. And that is what we are currently doing with 3 of the biggest companies in Europe where we target a 30% inventory reduction.

To succeed in building this product, we need two critical skills:

1. Handling vast amount of data and scale machine learning models-and Karim has been doing just this at Criteo and Axa.

2. And we need deep supply chain planning knowledge to build and sell the solution. I have 7 years of experience on this topic.

Our vision is to create a network to adjust stocks automatically across several companies: it will reduce working capital, eliminate wastes and shortages.