Fabio 

 Challenge       My challenge was determining the reason for significant changes in demand in the first two 

 weeks of planning . I work in Supply Chain for a very large company . We recently implemented an   Advanced Planning and Optimization ( APO ) tool for planning inventory levels in the field ( 300 locations )   and at our Distribution Centers ( 3 locations ) . At its core , the APO system helps us plan the right   inventory , at the right place , at the right time , and in the right quantity . It does this by determining the   target inventory levels based upon pre - defined settings ( parameters ) . These settings define how much   inventory we want to store as safety stock ( inventory above exactly what is needed in case of   fluctuations in demand ) . 

 The field orders inventory each week . For the first time , we were letting a system make these 

 order quantity recommendations for us . Since correct inventory levels are critical , we built in a two - day   window where the field could review the system generated plans and adjust as needed . However , these   adjustments were happening significantly and in multiple directions . During the review process some   items were being increased while other were being decreased . To compound the issue , each of the   three different departments ( Demand , Supply , Distribution ) were making changes to try and correct .   These last - minute changes were causing us to send incorrect order quantities to the Manufacturers that   have long lead times . This means significant changes happening each week would take months to   correct . 

 Selection 

 The cause of the issue was not clear due to system complexity and multiple groups trying to 

 correct it so I ended up using most of the tools . Learning Launch was my last chance to find a solution . I   started with Data Analysis but could not pinpoint the issue . I then went to the customer ( the field   making the changes ) using Storytelling to understand what they were doing and why they felt like that   was the right decision . This resulted in a handful of possible causes . The only way to know for sure was   by making adjustments , understanding the impact , then iterating until something worked . Learning   Launch let me fail fast to get to the correct solution by testing my hypothesis ’s . 

 Application       I selected 10 locations to use Learning Launch with . This left the other 290 locations as a 

 baseline to compare against . The 10 locations were chosen based upon how they were making their   adjustments . Some were making only a few changes while others were making the most . I initially met 

 with them as a group to introduce what I wanted to try . I reviewed what I was seeing , what the system   was trying to do for them , and walked them through why the system was making the recommendations   it was . 

 I then met with each location weekly during their adjustment window . Instead of them adjusting 

 their orders to what they wanted , I adjusted the settings to them . This accomplished two things . First it   confirmed that the settings were not correct for what the field wanted . Second , it built trust in the   system for the field . After several iterations , the system reached a steady - state of un - adjusted , system   generated orders . Absolute quantity adjustments went from over 70 % each week to under 5 % resulting   in fewer expedited orders and smoother signals to the Manufacturers . After a few weeks I convinced the   field that inventory levels could be reduced to normal levels without the risk of running out . This led to a   multi - million - dollar reduction of inventory levels in the field , especially important during times of   constraint . 

 Insight       Without Learning Launch I would not have been able to definitively settle on what was causing 

 the issue and implement the correct solution . By making the changes , reviewing the results , then   making additional changes each week I determined not only that the settings ( not demand or other   factors ) were causing the issue but which ones and how to resolve them long term . 

 Approach 

 I was using most of these tools before knowing about Design Thinking so some of my 

 approaches were not technically sound . In a similar situation I would use the full suite of tools to better   document the journey of discovery for future use . The use of Learning Launch in itself would not have   solved this issue but was absolutely key in its final resolution . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: Fabio
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Fabio 

 Challenge       My challenge was determining the reason for significant changes in demand in the first two 

 weeks of planning . I work in Supply Chain for a very large company . We recently implemented an   Advanced Planning and Optimization ( APO ) tool for planning inventory levels in the field ( 300 locations )   and at our Distribution Centers ( 3 locations ) . At its core , the APO system helps us plan the right   inventory , at the right place , at the right time , and in the right quantity . It does this by determining the   target inventory levels based upon pre - defined settings ( parameters ) . These settings define how much   inventory we want to store as safety stock ( inventory above exactly what is needed in case of   fluctuations in demand ) . 

 The field orders inventory each week . For the first time , we were letting a system make these 

 order quantity recommendations for us . Since correct inventory levels are critical , we built in a two - day   window where the field could review the system generated plans and adjust as needed . However , these   adjustments were happening significantly and in multiple directions . During the review process some   items were being increased while other were being decreased . To compound the issue , each of the   three different departments ( Demand , Supply , Distribution ) were making changes to try and correct .   These last - minute changes were causing us to send incorrect order quantities to the Manufacturers that   have long lead times . This means significant changes happening each week would take months to   correct . 

 Selection 

 The cause of the issue was not clear due to system complexity and multiple groups trying to 

 correct it so I ended up using most of the tools . Learning Launch was my last chance to find a solution . I   started with Data Analysis but could not pinpoint the issue . I then went to the customer ( the field   making the changes ) using Storytelling to understand what they were doing and why they felt like that   was the right decision . This resulted in a handful of possible causes . The only way to know for sure was   by making adjustments , understanding the impact , then iterating until something worked . Learning   Launch let me fail fast to get to the correct solution by testing my hypothesis ’s . 

 Application       I selected 10 locations to use Learning Launch with . This left the other 290 locations as a 

 baseline to compare against . The 10 locations were chosen based upon how they were making their   adjustments . Some were making only a few changes while others were making the most . I initially met 

 with them as a group to introduce what I wanted to try . I reviewed what I was seeing , what the system   was trying to do for them , and walked them through why the system was making the recommendations   it was . 

 I then met with each location weekly during their adjustment window . Instead of them adjusting 

 their orders to what they wanted , I adjusted the settings to them . This accomplished two things . First it   confirmed that the settings were not correct for what the field wanted . Second , it built trust in the   system for the field . After several iterations , the system reached a steady - state of un - adjusted , system   generated orders . Absolute quantity adjustments went from over 70 % each week to under 5 % resulting   in fewer expedited orders and smoother signals to the Manufacturers . After a few weeks I convinced the   field that inventory levels could be reduced to normal levels without the risk of running out . This led to a   multi - million - dollar reduction of inventory levels in the field , especially important during times of   constraint . 

 Insight       Without Learning Launch I would not have been able to definitively settle on what was causing 

 the issue and implement the correct solution . By making the changes , reviewing the results , then   making additional changes each week I determined not only that the settings ( not demand or other   factors ) were causing the issue but which ones and how to resolve them long term . 

 Approach 

 I was using most of these tools before knowing about Design Thinking so some of my 

 approaches were not technically sound . In a similar situation I would use the full suite of tools to better   document the journey of discovery for future use . The use of Learning Launch in itself would not have   solved this issue but was absolutely key in its final resolution .
### Output: 