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.

