Previous article: How Much? – BI General Question (Part 3)
Next article: How Likely & How Quickly – BI General Question (Part 5)
This is the fourth part of a general introduction to BI using a case study of a mobile phone store.
How Often? – this relates to trends that appear in the analysis of the previous two question groups. These are also measured over fixed periods of time (e.g. monthly, yearly or even since the start of the enterprise). How often does a certain trend/event occur? For example, looking at the high street phone shop, we can take a look at the stock values of the shop at the end of each month, along with the new stock delivery in the first week of each month:
This is where data representation becomes more difficult. The tables 3.1 and 3.2 show the records of stock of phones for the shop for the entire year. It is very difficult to see any trend whatsoever from the tabular report. Plotting this data would not make much sense either that it would not tell us much. However, let us assume that the executive management has decided that it’s high street shop should aim to maintain at least 25% of monthly phones sales as stock at all times. This would represent 1/4 of monthly sales, or more importantly 1 week’s sale figures, sufficient buffer time to order a new stock. Let us also assume that at the end of the month, a shop should not have in excess of 50% of sales numbers as stock. This is a precaution in order to ensure that not too much liquid assets are locked into stock. Now, if we analyse the stock figures as a percentage of monthly sales, we can see how the shop is doing relative to the directives of the executive management. This is shown in figure 7.
How often does the stock fall below the prescribed limit and potentially exposing the shop to a potential loss of sales opportunities? How often is the shop overstocked and therefore exposing itself to a cash flow problem? These questions are clearly answered in the representation of the data in figure 3.1. Here, a new type of analysis would be needed to create a stock KPI (Knowledge Performance Indicator) that would enable the shop to better control it’s stock. Such a KPI could be built from 12 month rolling sales data allowing for a better analysis of current trends. This analysis could also be turned into a stock order tool.