Tiffin Consulting

digital business transformation

How Often? – BI Intro (4)

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by

Aurovrata V.
in

Previous article: How Much? – BI General Question (Part 3)

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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:

Table 3.1: Q1/Q2 phones stock data, Reminder at month end and In delivery as per order.
Phones Jan Feb Mar April May Jun
stock rem in rem in rem in rem in rem in rem
Bar 51 500 104 450 153 400 41 450 56 400 61
Flip 19 270 88 250 193 100 95 150 59 150 33
Touch 54 400 142 350 246 110 10 315 0 350 28
Smart 15 260 40 245 84 200 17 250 35 250 73
Android 0 270 3 280 51 300 4 283 0 300 10

 

Table 3.2: Q3/Q4 phones stock data, Reminder at month end and In delivery as per order.
Jul Aug Sep Oct Nov Dec
in rem in rem in rem in rem in rem in rem
350 31 359 0 395 0 450 10 400 0 420 33
150 27 150 32 120 16 171 0 159 0 160 32
350 75 310 76 240 18 300 13 290 4 290 31
220 91 170 63 140 15 200 16 185 25 150 22
300 0 330 15 345 40 380 33 350 34 350 28

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.

Understanding Business Intelligence with a case study
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.

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.