Reﬂection

Applying Learning Launch Tool

for the system development

Challenge

I'm a leader of the software engineer team. We'd mainly developed a business support

system. Although an experienced system development team, we're not familiar with the

viewpoint of user experience. We decided to apply the process of design thinking to an

upcoming project.

The new project aims to improve the in-house inventory system, with the electronic

components' search interface and database. All the hardware engineers (over 1000

members) have to search the electronic component on this system. This system holds

all of the speciﬁcations of each electronic component. However, the hardware engineer

often takes too much time to select the components. It up to 40% of the electronic

apparatus design process period. The target problem is how to show and recommend

the electronic components which the hardware designers really need.

Selection

We ﬁrst started the interview with a few hardware engineers to get the project's hint

about the preliminary investigation. They need the parts not only a similar component

which they already selected but also the components which the other engineers

selected. And the same component not always works well in each circuit because of the

condition parameter on the circuit and the compatibility between the components.

Then, we applied the Learning Launch tool to this situation (the lecture #4 video).

Before accepting the Learning Launch tool, we faced a complicated problem: our team

members had many ideas for implementing the system. Each member has a diﬀerent

viewpoint and opinion. In some cases, our team deeply discussed the diﬀerences

between the opinions. During the discussion, I understand that we're cognitively blinded,

and we surely have a conﬁrmation bias (which was introduced in the #4 video). For

getting a slight forward, we decided to do the simple experiments based on the

assumption.

Application

We focused on the analysis of user operations on the component-search interfaces.

The process was as follows:

1. INTERVIEW: over 80 engineers

2. ASSUMPTIONS: making by each member

3. IMPLEMENTATION: some function on the interfaces

4. EXPERIMENT: by the randomly picked engineers

5. DISCUSSION: verify the assumption and eﬀectiveness of the implementation

6. SELECTION: the implementation for the ﬁnal system

(continue to repeat 3-5 process)

To avoid the impact of cognitive blindness in Step1, each team member made a

plan(what they want to know and the reason) for the interview independently. The

engineers took an interview at least two times by our members. And the engineer's

comment was recorded as it was.

In Step2, we also focused on a speciﬁc engineer as a persona and made assumptions

to improve the interfaces, what kinds of information is needed, what operations are

needed to improve their daily operations…etc. And implemented interfaced in Step3.

In Step4, we randomly selected 5 engineers for the experiment and included the

engineer who is the personas' model. The test was held online, and the operation of the

interface(button, comment, and so on ) was recorded on the database as data. The

data can use for the analysis of This data can be used afterward.

And in Step5, we discussed each experiment in the team. We focused on the

diﬀerences between the assumptions and experiment results regarding the comments

at the interview. We could avoid the conﬁrmation bias with the engineers' operation

data on the database. And we selected the ﬁnal implementation in Step6.

We made a total of over 30 assumptions and improved 30 parts of the interfaces on

the inventory system. Finally, we achieved the half-reduction of the period of the

component selection process in some cases.

Insight & Approach

During the discussion and veriﬁcation of the assumption, we decided to adopt or reject

each of the implementations once, but we never threw away both assumption and

implementation, just tried to keep it to the "idea pool" (referenced video on lesson #4).

Indeed, we often took back to the idea pool when the selected implementation seems

not eﬀective, and there were some cases the other implementation in the idea pool

works well. We realized that our tentative decision surely aﬀected cognitive blindness

and discovered the importance of observing engineers' operation as it is.

On the other, our team faced the situation: the more we concentrate on a problem that

ﬁts the speciﬁc personas, the more we seem to lose the big vision we truly would like to

achieve at the beginning of the project. We want to apply the storytelling tool to the

beginning of the project to understand the problems and build the concept before

starting the experiments.


Reﬂection Applying Learning Launch Tool for the system development Challenge I 'm a leader of the software engineer team . We 'd mainly developed a business support system . Although an experienced system development team , we 're not familiar with the viewpoint of user experience . We decided to apply the process of design thinking to an upcoming project . The new project aims to improve the in - house inventory system , with the electronic components ' search interface and database . All the hardware engineers ( over 1000 members ) have to search the electronic component on this system . This system holds all of the speciﬁcations of each electronic component . However , the hardware engineer often takes too much time to select the components . It up to 40 % of the electronic apparatus design process period . The target problem is how to show and recommend the electronic components which the hardware designers really need . Selection We ﬁrst started the interview with a few hardware engineers to get the project 's hint about the preliminary investigation . They need the parts not only a similar component which they already selected but also the components which the other engineers selected . And the same component not always works well in each circuit because of the condition parameter on the circuit and the compatibility between the components . Then , we applied the Learning Launch tool to this situation ( the lecture # 4 video ) . Before accepting the Learning Launch tool , we faced a complicated problem : our team members had many ideas for implementing the system . Each member has a diﬀerent viewpoint and opinion . In some cases , our team deeply discussed the diﬀerences between the opinions . During the discussion , I understand that we 're cognitively blinded , and we surely have a conﬁrmation bias ( which was introduced in the # 4 video ) . For getting a slight forward , we decided to do the simple experiments based on the assumption . Application We focused on the analysis of user operations on the component - search interfaces . The process was as follows : 1 . INTERVIEW : over 80 engineers 2 . ASSUMPTIONS : making by each member 3 . IMPLEMENTATION : some function on the interfaces 4 . EXPERIMENT : by the randomly picked engineers 5 . DISCUSSION : verify the assumption and eﬀectiveness of the implementation 6 . SELECTION : the implementation for the ﬁnal system ( continue to repeat 3 - 5 process ) To avoid the impact of cognitive blindness in Step1 , each team member made a plan(what they want to know and the reason ) for the interview independently . The engineers took an interview at least two times by our members . And the engineer 's comment was recorded as it was . In Step2 , we also focused on a speciﬁc engineer as a persona and made assumptions to improve the interfaces , what kinds of information is needed , what operations are needed to improve their daily operations … etc . And implemented interfaced in Step3 . In Step4 , we randomly selected 5 engineers for the experiment and included the engineer who is the personas ' model . The test was held online , and the operation of the interface(button , comment , and so on ) was recorded on the database as data . The data can use for the analysis of This data can be used afterward . And in Step5 , we discussed each experiment in the team . We focused on the diﬀerences between the assumptions and experiment results regarding the comments at the interview . We could avoid the conﬁrmation bias with the engineers ' operation data on the database . And we selected the ﬁnal implementation in Step6 . We made a total of over 30 assumptions and improved 30 parts of the interfaces on the inventory system . Finally , we achieved the half - reduction of the period of the component selection process in some cases . Insight & Approach During the discussion and veriﬁcation of the assumption , we decided to adopt or reject each of the implementations once , but we never threw away both assumption and implementation , just tried to keep it to the " idea pool " ( referenced video on lesson # 4 ) . Indeed , we often took back to the idea pool when the selected implementation seems not eﬀective , and there were some cases the other implementation in the idea pool works well . We realized that our tentative decision surely aﬀected cognitive blindness and discovered the importance of observing engineers ' operation as it is . On the other , our team faced the situation : the more we concentrate on a problem that ﬁts the speciﬁc personas , the more we seem to lose the big vision we truly would like to achieve at the beginning of the project . We want to apply the storytelling tool to the beginning of the project to understand the problems and build the concept before starting the experiments .