代写RSM2008H-LEC0101D

日期：2024-03-10 06:54

RSM2008H-LEC0101DL IntrApril 9th, 2021

Ollibrands.com

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Introduction

Olli Brands Inc. (OBi) is a premium cannabis edibles manufacturer who focuses on

creating quality and great tasting edible products. Our company’s core point of

differentiation is its focus on creating products with a food first mentality and

cannabis simply an ingredient. Management’s goal for 2021 is to maximize sales and

increase our product suite to 20 SKUs. In order to do this, we need to be a thought

leader with innovative products to build and maintain a strong market position.

Our product development and R&D team spend countless hours researching

product trends, consumer insights, flavours and innovative ingredients to keep up

with the ever-evolving pace of the cannabis industry while maintaining the

company’s core mission to create delicious tasting products that do not taste like

cannabis. This is a key piece to our differentiation strategy as our target market is not

a cannabis connoisseur but rather a new or renewed cannabis consumer.

This development process is both costly, causing management to have to choose a

limited selection of products to focus on, and risky, coming with a lot of uncertainty

given the regulations restrict us from easily testing the infused product in-house. It is

also incredibly time consuming often resulting in a lost opportunity to be first to

market and stay ahead of the trends. Given the time and there being no way for us to

test our products for taste aside from doing costly clinical trials or waiting until after

we have already launched a product, OBi can leverage AI to advance the product

development process and eliminate some of the uncertainties.

Summary of the AI Canvas

We can leverage AI to predict what type of ingredients and flavours to use that will

mask the cannabis taste and which types of products will be successful in the current

market. The double-action machine will only accept ingredients and flavours that

mask cannabis taste and from here, it will narrow down a list of potential products

even further by analyzing the probability of success of such product in the market.

The measure of success will be a product free of cannabis taste and that achieves a

ROI of at least 35% in year one of product sales (see Appendix II for ROI

calculation). The machine will be rewarded each time it successfully achieves the

desired outcome to help further its potential for future successes. We can input data

from various sources including consumer insight reports, cannabis palatability

studies, employee experiences, terpene profiles of food and cannabis, and market

trends to help train the machine.

We will not have to make any immediate changes to our workforce, and I foresee a

reduced need to hire as rapidly as planned for our product development team. This

will also allow our current team to become more experienced in building lab

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prototypes and focusing on innovations in cannabis or processes by reducing the

need for countless hours of research of food ingredients and trends. A smaller team

will be able to accomplish producing more new products in a given year. Refer to

Appendix I for the AI Canvas and Appendix III for a diagram of how the machine

will work the to further describe the process.

Benefits

The cost savings for this type of investment will be massive. I would plan for a six-

month set-up, similar to our ERP implementation at which point we can begin to

use AI in the development process. Each time we develop a new product, we can

input more data on the outcome of each product to help it get better all the time.

There would be cost savings of ~$26,000.00 in the product development process by

reducing the labour hours required for market research and product testing,

increasing ROI by 35% (Appendix II). In addition to this, a reduction in labour

hours required in the development process by 52% (Appendix II) frees up time for

the product development team to focus on additional products and develop more

products in a given year. It will also allow the company to stay ahead of trends,

reduce uncertainties about cannabis taste in our finished product and avoid having to

test products in market only to find them not succeed. I have estimated this process

will reduce uncertainty and risk by 15% - 20% and we will save over $360,000.00

over a five-year period in failed product costs.

Risks

There are few risks that come along with this investment in AI. Firstly, we may not

be able to access enough data to train the machine given the newness of the cannabis

industry and publicly available data. This could restrict us in how quickly we can train

the machine and get to the level of accuracy I have predicted (95%). I advise we

allocate a $15,000 budget to acquire data from Headset, Business of Cannabis and

various retailers offering industry statistics for a fee. Secondly, the machine may

predict flavours that mask the cannabis taste but do not work well together or result

in a great tasting product. To mitigate this risk, we will have to test the flavours and

ingredients in a non-infused prototype before approving it for the machines second

action. Lastly, and most importantly, we may miss truly innovative opportunities

with new flavours or ingredients where data is not available to feed and train the

machine. Human judgement from both the management and product development

teams will continue to be crucial in making the final decision on product launches to

pursue.