Data Analytics And Business Expectations

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• The application of the temporal probabilistic reasoning to SBITA as competitive business intelligence.

• Illustrating to business researchers and practitioners how to use concrete examples to align businesses with IT through presentations.

• Accounting for inevitable complexities and uncertainties embedded in business environments as a strategy for making timely, correct, and astute business decisions.

A. Data Analytics And Business Expectations

This section presents the analysis of real-life experiments from validation studies of our ESA technology using meat-packer data obtained from a local South African butchery company. This analysis is carried out with the intention of acquiring competitive business intelligence regarding the butchery market, which requires taking appropriate responsive actions on any anticipatory planning and risk situations. We therefore monitored the production and the distribution of four different meat types to 17 sales outlets, based on customers’ demand and supply in the local butchery enterprise.

More than 45,000 MTS sales observations were captured over the period of time January to December for the year 2005, where the ESA evolves. Each month consists of about 3,500 observations obtained from the 17 South African sales outlets. The multivariate time series dataset is described using the following attributes: Sales-Outlet, Product-type, Quantity, and Sales. These attributes are likely influenced by external factors such as public holidays and possibly affected by climatic conditions such as: Temperature, Wind and Rainfall. New future factors or conditions are considered when necessary. The main objectives of sales managers are to improve the performance of their business, as follows:

• maintaining TQM (total quality management) especially among customers [83],

• understanding the strengths and weaknesses of their sales-outlets,

• minimising risk of revenue losses in sales and productions and,

• being aware of the situations with their business competitors.

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With ESA, the managers can, for instance, identify products with similar sales patterns, the best-selling outlet, the fast-, and slow-moving products, and will know how environments impact on their market.

These real-world economic results reveal the uncertainties inherent in the butchery business, which consequently facilitates better defensive and astute decisions. ESA thus learns from the meat-packers’

MTS and evolves the DBN shown in Figure 6.12. Observe that this is a global behaviour (or knowledge) of the business environment, as the ESA truly evolves temporal networks (frames) over time.

The true evolvement or variations is a result of changes in probability distributions and networks of ever-changing sales over periods of months. Based on our theory of situation calculus in the preceding chapters, especially Figure 3.4, some actions act upon a situation at a time step t to evolve next situation t+1. In practice, hidden current sales patterns in January gives rise to the frame model revealed for the month. In February, there have been some actions such as: less/more holidays, excessive rainfall, or increase/decrease in weather temperature, which impact significantly upon the sales. The environmental

Figure 6.12: An Evolved DBN Model for Sales Management using the ESA

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changes are captured and revealed with a varied frame model for February and this occurs over the rest of the months. The next paragraphs illustrate the awareness of sales situations as intelligence to make astute and correct decisions.

B. Intelligence In Customer Buying Patterns

The ability of sales analysts to understand customer buying patterns becomes essential in order to keep pace with ever-changing customer preferences. An analyst may issue any probabilistic queries of interest similar to the samples in equations (6.11) and (6.12) which act upon Figure 6.12 and create awareness about business situations, as shown in Figure 6.13.

pr (Product-Type ^t = Coldmeats | Sales-Outlet ^t = Paarl, PublicHoliday ^t = Holiday)? (6.11)

pr (Product-Type ^t = Lamb | Sales-Outlet ^t = Paarl, PublicHoliday ^t = Holiday)? (6.12)

In equations (6.11) and (6.12), we want to understand the similarity of sales patterns between Lamb and Coldmeat in the Paarl outlet during public holidays with respect to customers’ preferences. The revealed business situation results by the ESA are interactive, as shown in Figure 6.13. For simplicity, an interesting characteristic of the ESA is that it enables decision-makers to reason about one situation at a time.

January to December (time steps or x-axis) indicates the total monthly sales for the situations in the first and second results of Figure 6.13. The values on the left (y-axis) or on top of each situation indicate the corresponding percentage sales of each meat out of the four meat types. By answering the four guideline questions of the ESA technology that follow Figure 6.13 using the situational knowledge revealed over the months, it will facilitate better anticipatory planning and decision-making. This strategically aligns business and IT.

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Figure 6.13: Business Situations of Coldmeats and Lamb in PAARL outlet during public holidays.

Detailed understanding and decision-making process of the situations in Figure 6.13:

Q1: What is happening?

A1: By comparing the pattern of sales of coldmeats in the first situation results with the corresponding sales patterns of Lamb in the second situation results, one can observe nine similarities out of 11 patterns. This is 82% level of agreement or correlation between the two meat types. This implies that irrespective of the percentage of sales, there are increases in sales for both meats from January to March, and drops in April, for instance. These similar patterns continue until December.

Q2: Why is it happening?

A2: In the Paarl outlet, it is evident that customers who buy Lamb are likely to buy Coldmeats or vice versa. This might be due to competitors’ prices, cultural beliefs, and social influences etc., in the context in which the outlet is situated.

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118 Q3: What will happen next?

A3: Similar patterns are likely to reoccur since these business situations are revealed from sales of two years, unless there is a promotion declaration on either of the two meat types.

Q4: What can I do about it?

A4: It is a clear indication that the Paarl outlet must ensure that they stock both Lamb and Coldmeats, especially during public holidays. It is an astute decision to declare discounts on both meat types at the same time so as to influence customer acquisition and retention.