Tying results to individuals
I was at a client this week and had the opportunity to present how a business intelligence solution using QlikView could provide needed insight to readily available, but difficult to analyze, supply-chain data. The extract of sample data provided (we like to use real data from each prospect in our demos whenever practical) showed how a variety of products moved through the manufacturing supply chain, specifically, when an order was received, when it was sent to the factory for production, when it was scheduled for build, when it was completed, when it was shipped to the target market, when it was received at the port, when it was shipped to the dealer, and when it was sold. By looking product line by product line, the total time from order to delivery can be easily analyzed. Everyone understood the power in completing this type of analysis, as they had been struggling with a variety of home-grown macros in Excel to do this type of analysis. During the dialog I picked up on a few points we’ll use in future discussions.
In this case, the people feeling the most pain were the ones that had to take flak from the dealers as to why their product took so long to arrive after the order was placed. So, when I showed them how great our analysis was, how easy it was for them to see where the delays occurred, they responded with, “that’s great – but doing this analysis and addressing the delays is not our job.” I picked up on that point and realized that when speaking with potential clients about the power of business intelligence, the benefits should be mapped as specifically to the user community as possible, not addressed in aggregate from an organization perspective. The dialog progressed into how an empowered, independent group that could address delays across the supply chain would be of great value.
Further, concerns identified through analysis should be as closely tied to individual performance as possible. If, for example, certain product lines took longer to ship from the factory to the market than others, and additional data such as shipping line and person who contracted with the shipping line could be added to the analysis, it would be easy to show that Product Line A that was Tom’s responsibility took, on average, 1.5x as long to transport as Product Line B, that was Keiko’s responsibility. What gets measured gets attention, and what gets attention gets fixed.
When developing business intelligence solutions, look for data sources that tie the results of the analysis to individuals, and when talking about the benefits such analyses offer, make sure those benefits are relevant to each specific audience.
