## 4 Application: Case Study
### Strategic Planning  
During the SP process for the Madison Grill restaurant, a team was integrated with opinion leaders who were responsible for transmitting the actions that must be applied to achieve the enterprise's objectives. Implementing the strategic stage involves the OM of SM because the amount of information, its diversity, and the richness of the content are necessary attributes to formulating a strategic plan, and in this case, a BSC. The classic strategy formulation processes involve manual mechanisms that consume considerable time and effort [92]. This situation presents an automatic system that operates using unstructured information in a short time.  
After processing, the framework presented a word cloud (tokens) with the highest polarity and frequency in SA (Figure 6).  
Each comment was oriented to a specific KPI of interest (KPI\({}_{1}\)-KPI\({}_{9}\)) previously validated by the planning team. Using Python, the Jaccard similarity index (Equation (5)) was used to determine the relationship between the intersection referring to the set of tokens present in the opinions and the set of tokens present in the KPIs. The union between the two sets was analyzed. Jaccard distance (Equation (6)), used in the Python code as jaccard_distance = 1--jaccard_similarity, was used to calculate the distance of the tokens present in the comments to the KPIs and categorize each of the BSC perspectives. The planning team validated the KPIs and their orientations to each perspective using an affinity diagram. Figure 7 shows a representation of the BSC architecture, which was automatically generated according to valuable information from customer reviews.  
Figure 6: A word cloud illustrating the most frequent tokens, the final processed dataset.  
The customers' opinions of SM were mainly oriented to perspective 2 (P\({}_{2}\)-Costumers), which automatically generated KPIs 3, 4, and 5. The planning team should consider this perspective as a priority. The reason for this is that the customers do not necessarily know either the financial status or the internal process of the restaurant. Still, their opinions were considered an alternative for visualization in monitoring the indicators. It is essential to mention that the SP team manually incorporated the financial perspective (P\({}_{1}\)-KPI\({}_{1}\) and KPI\({}_{2}\)) and the learning and growth perspective (P\({}_{4}\)-KPI\({}_{9}\) and KPI\({}_{10}\)) by the SP team with the aim of creating the complete BSC. To complete the above, it was necessary to follow up with experts in order to validate the results. Table 4 shows the percentage of opinions categorized by KPIs and perspective.  
It can be seen that the customer perspective was the one with the highest categorization of comments, mainly in KPI\({}_{4}\), which indicates that attention should not be lost in this category since the customer tends to like or dislike it to a greater extent. Based on the automatic configuration of the restaurant's BSC, the proposed system was considered adequate for the company because it was aligned with its mission, vision, and objectives.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Perspective** & **\% Comments** & **KPI** & **\% Comments** \\ \hline \multirow{3}{*}{(P\({}_{2}\))} & & KPI\({}_{3}\) & 12.0\% \\  & & KPI\({}_{4}\) & 45.1\% \\  & & KPI\({}_{5}\) & 17.5\% \\  & & KPI\({}_{6}\) & 7.3\% \\ \multirow{3}{*}{(P\({}_{3}\))} & 12.8\% & KPI\({}_{7}\) & 1.8\% \\  & & KPI\({}_{8}\) & 3.8\% \\ \multirow{3}{*}{(P\({}_{4}\))} & 10.7\% & KPI\({}_{9}\) & 10.7\% \\  & & N/A & 1.9\% \\ \cline{1-1}  & & Total & 100.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage of opinions categorized in the KPIs and perspectives.  
Figure 7: Representation of the automatic generation of the balanced scorecard.