Design Thinking for Innovation  June 12, 2020  Elizabeth Pedraza          Challenge    Our consulting team worked with a Planning and Scheduling Division of a family-owned pasta  company with inventory problems resulting in lost revenue. The company had times when they  had either too much inventory (food was going bad - obsolesence) or too little inventory (back- orders or shortages), both situations resulting in unhappy customer relationships and lost  profits. Our team was brought in to understand and find solutions to the company’s inventory  problems.    Selection      The team decided that a time-series visual view of the historical actions taken by the relevant  parties would illuminate opportunities for improving inventory-related decision-making.  Seeing  what happened, people could then discuss what happened and why.  This should lead to ideas  for process improvements to solve the shortage and oversupply issues.  We believed that doing  this would help reduce waste, increase margins, increase revenues and please customers.    What the team chose, after careful consideration, was the business intelligence visualization  tool Microsoft Power BI. Power BI allows the creation of interactive graphs, which a user can,  without any skill or training, manipulate or filter to show multiple scenarios or views of the  underlying data.  This was a simple, visual way for everyone to gain insight into the problems  and thereby trigger useful conversations leading to collaborative solutions.

Application   In addition to quantitative data gathering (and cleansing), particularly sales order, production,  shipment, and inventory status data, the team spent many hours interviewing officers and line  people.  They wanted to include multiple perspectives on what happened, hoping to reveal the  thinking that lead to poor decisions in the past and suggest better ways to handle similar  situations in the future.    The choice of Power BI and its graph of inventory data told a visual story. Because the graphs  were simple, easy to understand, and single-page, everyone could look at a chart and quite  easily see which weeks the company should have made more of a particular type of pasta and  which weeks they should have sold pasta from existing inventory.  They could also see order  patterns and production patterns, or lack thereof.  By seeing these patterns visually and  intuitively, it was easy for the stakeholders to offer improvement recommendations.    Insight

The visual tool was a nonverbal way using colors, symbols that could convey messages to all  stakeholders, create conversations leading to alignment and explaining patterns which lead to  ideation and prototypes of solving the problem as we learned from overall design thinking.    One of the biggest insights from the ideation sessions was how thirsty people are for more  information when asked to make decisions or improvements.  The most common questions  from the team usually began with “Can we …..?”.  They wanted more information to overlay  with what we presented.  Can we look at this by customer?  Can we look at this by region?  Can  we look at this by salesperson?  Etc.  In Power Bi, if the data exists, the answer is almost always  “yes”!  Curiosity was palpable in every session.  Easy visualization of complex data stimulates  creativity.      Approach  The clients (pasta company and customers) were very satisfied with the visual solutions our  team offered and demanded more. Our consulting team were satisfied with the platform we  used and created more graphs showing different pasta skus and applications of company  inventory data.   The clients have enhanced the tool themselves since the first workshops.      Next time our team will definitely use Power BI again, but we might spend more time up front,  collecting (and cleaning) more ancillary data, [e.g. forecast (past and future), raw materials  supply, or sales/marketing promotion calendars] and moving it into the Power Bi database so  that more “what if” scenarios could be run on the spot.  We under-estimated how curious the  team would be and how immediately thirsty they would be for more scenario modeling. Having  that instant response while the team is fully engaged seems to be much more productive than  re-convening at a later date to take the next steps.


Design Thinking for Innovation June 12 , 2020 Elizabeth Pedraza Challenge Our consulting team worked with a Planning and Scheduling Division of a family - owned pasta company with inventory problems resulting in lost revenue . The company had times when they had either too much inventory ( food was going bad - obsolesence ) or too little inventory ( back- orders or shortages ) , both situations resulting in unhappy customer relationships and lost profits . Our team was brought in to understand and find solutions to the company ’s inventory problems . Selection The team decided that a time - series visual view of the historical actions taken by the relevant parties would illuminate opportunities for improving inventory - related decision - making . Seeing what happened , people could then discuss what happened and why . This should lead to ideas for process improvements to solve the shortage and oversupply issues . We believed that doing this would help reduce waste , increase margins , increase revenues and please customers . What the team chose , after careful consideration , was the business intelligence visualization tool Microsoft Power BI . Power BI allows the creation of interactive graphs , which a user can , without any skill or training , manipulate or filter to show multiple scenarios or views of the underlying data . This was a simple , visual way for everyone to gain insight into the problems and thereby trigger useful conversations leading to collaborative solutions . Application In addition to quantitative data gathering ( and cleansing ) , particularly sales order , production , shipment , and inventory status data , the team spent many hours interviewing officers and line people . They wanted to include multiple perspectives on what happened , hoping to reveal the thinking that lead to poor decisions in the past and suggest better ways to handle similar situations in the future . The choice of Power BI and its graph of inventory data told a visual story . Because the graphs were simple , easy to understand , and single - page , everyone could look at a chart and quite easily see which weeks the company should have made more of a particular type of pasta and which weeks they should have sold pasta from existing inventory . They could also see order patterns and production patterns , or lack thereof . By seeing these patterns visually and intuitively , it was easy for the stakeholders to offer improvement recommendations . Insight The visual tool was a nonverbal way using colors , symbols that could convey messages to all stakeholders , create conversations leading to alignment and explaining patterns which lead to ideation and prototypes of solving the problem as we learned from overall design thinking . One of the biggest insights from the ideation sessions was how thirsty people are for more information when asked to make decisions or improvements . The most common questions from the team usually began with “ Can we … .. ? ” . They wanted more information to overlay with what we presented . Can we look at this by customer ? Can we look at this by region ? Can we look at this by salesperson ? Etc . In Power Bi , if the data exists , the answer is almost always “ yes ” ! Curiosity was palpable in every session . Easy visualization of complex data stimulates creativity . Approach The clients ( pasta company and customers ) were very satisfied with the visual solutions our team offered and demanded more . Our consulting team were satisfied with the platform we used and created more graphs showing different pasta skus and applications of company inventory data . The clients have enhanced the tool themselves since the first workshops . Next time our team will definitely use Power BI again , but we might spend more time up front , collecting ( and cleaning ) more ancillary data , [ e.g. forecast ( past and future ) , raw materials supply , or sales / marketing promotion calendars ] and moving it into the Power Bi database so that more “ what if ” scenarios could be run on the spot . We under - estimated how curious the team would be and how immediately thirsty they would be for more scenario modeling . Having that instant response while the team is fully engaged seems to be much more productive than re - convening at a later date to take the next steps .