Risk Management Plan
The Sous Chef Team acknowledges that there are certain risks involved in our project.
After careful analysis, the Sous Chef team has identified six distinct risks that will affect the
project’s success. Our risks were addressed on two main factors, which are the probability and
the impact. They were ranked 1-5 with 5 being the most severe, multiplied together, and then
were placed on the risk matrix (shown below). Our risks, in order of severity are as follows:
1. Database Integration
- Probability:
- Impact:
4
4
Integrating with an unknown database is a complicated task due to the difference in table
layouts, missing required data, and extraneous data. That is why integrating with the store’s
database for customer information, inventory, and sales is a major risk. The probability is 4
because this is very likely to happen, and the impact is also 4 because if we are unable to
integrate the database, we cannot deliver a system with all of Sous Chef’s features.
Mitigating this risk seems like a daunting task at first, but upon closer examination, the
task is not that difficult. The Sous Chef team will implement an integration system that will allow
the team to import data from a database regardless of its source. At the beginning of the
integration process, database schemas will be exchanged with the grocery store. After the field
properties are discussed and agreed upon, the store will send us an updated data in a flat file
format at the scheduled times via an ftp process. We will place a batch job to grab these files and
write stored-procedures to translate, validate and import the data into the appropriate tables of the
Sous Chef database.
2. Database Performance
- Probability:
- Impact:
2
4
Having a database is useful, but if the database does not perform well, its usefulness
declines rapidly. The database needs to be designed with the system’s complexity in mind. The
traffic coming in and out of the database will be high due to the number of users accessing the
system and queries requested daily; therefore, the database must be designed with performance as
a high priority. The probability of this risk is 2 because the database will be designed with high
performance in mind at an early stage. However, the impact is 4 because if the developer fails to
create a robust database, the system will suffer and customers will be unhappy due to its slow
performance.
The Sous Chef team plans on mitigating this risk by focusing on database performance at
the beginning of the product’s developmental stage. We will consult a database expert during the
development process who will assist, analyze and suggest a plan to create a well designed and
robust database structure that can handle the heavy load of data processing.
3. Database Scalability
- Probability:
- Impact:
2
3
In order for Sous Chef to be successful, the database must also be flexible to handle many
concurrent users and an increasing number of users. The ability of a system to handle both small
and large numbers of users is called its scalability. If Sous Chef is not scalable, the store’s use for
Sous Chef is not scalable. Due to the possibility of a large user increase over a short period of
time, the probability is 2. However, the impact is only 3 because the problem can be resolved
quickly without any significant impact on the system or the budget.
We plan on mitigating this risk by designing the database and associated servers to be
interchangeable and load-balancing. That means that servers can be added as needed and the load
will be equally distributed amongst all the servers. That will increase scalability by increasing the
ability of the overall system to bear the load of more users.
4. Test Data Consistency
- Probability:
- Impact:
3
2
Because we will not be purchasing the recipe database until Phase II, we will be creating a
set of pseudo recipes for database testing in Phase I. We plan to write a program to generate a set
of pseudo recipes in order to test the system. The risk in generating pseudo data is that the data
characteristics might not be consistent with the real data. The probability of this risk is 3 because
we do not have the domain knowledge to know the distribution of recipe information. However,
the impact is only 2 because the recipe data characteristics can be changed without too much
difficulty in Phase II.
This risk can be mitigated by conforming the testing to data processing and performance
and not to the properties of the recipe information.
5. FDA Guideline Changes
- Probability:
- Impact:
1
3
The FDA has been known to change its dietary recommendations, especially in recent
years. This could affect Sous Chef adversely if the FDA requires a new piece of nutritional
information that we do not already have stored in our database. The probability for this risk is
only 1 because the FDA does not change their dietary recommendations frequently. Also, we
will be able to modify our database to adapt to the changes before it adversely affects the product.
The impact can be as high as 3 because we might be giving our customer incorrect or incomplete
data and the changes might require the database tables to change.
We plan to mitigate this risk by contracting the recipe company to be responsible for
modifying the recipes according to FDA’s rules and regulations and supply us with an updated
recipe database. We will also have a full time dietician in Phase 3 who will ensure that our
recipes are up-to-date with all the FDA’s dietary guidelines and recommendations.
6. Database Storage
- Probability:
- Impact:
1
2
If our database has inadequate storage, we will be unable to store all of the recipes or
customer information. This means that the database will not be able to solve the problem. The
risk probability is only 1 because we estimate the database storage requirement to be about 2
gigabytes while each database server, of which there are two, has 80 gigabytes of storage. That
means we have a total storage capacity of 160 gigabytes. The impact is a 2 because we can
simply add more storage to our servers to solve the problem with only a minimal impact to the
budget.
This risk is mitigated by carefully analyzing the hardware need for when the product is
fully implemented and functioning. After the analysis, we will purchase adequate hardware to
accommodate future storage increase. We will also monitor and analyze the system usage
frequently to prevent any inadequate resource problems.
Risk Diagram
Impact
Probability
1
4
3
6
5
2