Using computational thinking for business problem solving

CC: People Analyzing Data on a Digital Tablet by AlphaT

Computational thinking is often mistaken for a skill set that a computer science expert should master in order to develop solutions via computer code. While that is true, this skill set need not be limited to them. Incorporating computational thinking to science, arts, enterprises etc. brings in vast possibilities and problem-solving capabilities so that newer, more complex problems can be tackled with ease. The modern workplace is all about problem solving. It is the underlying aspect of every business which ultimately defines its success or failure.

How can I learn more ?

Before getting started, we advise getting familiar with “Computational Thinking: What is it? How is it used?”. If you have a gist of 4 principles of computational thinking, then let’s commence on solving the challenges identified in the case study of “Business Challenges”.

Read the case study.
The first step is “Decomposition” which is the process of breaking down complex problems into smaller and manageable parts. Using “Expense List” in the resource section, create a spreadsheet with columns for each month and rows for each expense category and include a row for total monthly expenses and average costs per category. Now, review the last six month’s expenses and identify the top three areas where the company spends the most money.
The second step is “Pattern Recognition”. As it sounds, it’s all about recognizing patterns. To this aim, we want you to examine the expense trends over time. Are there any recurring spikes in certain categories? To find out, use a line graph to visualise spending trends in the identified top expense areas – you can use Microsoft Excel, Google sheets or online graph makers.
The third step is “Abstraction” also called “Pattern Generalization” which enables us to identify the patterns or details that matter and that are relevant to solving the problem and being able to ignore the details that don’t matter. Go back to the graph and sheet and determine which expenses are essential for operations and which could be potentially reduced without harming the business.
The last step is “Algorithm” which is being able to take what was learned to design a solution or steps that achieve the desired outcome every time. You’ll create an algorithm for expense reduction by developing a step-by-step plan to reduce expenses. This could involve renegotiating supplier contracts, finding more affordable marketing channels, or optimising energy usage: draft a proposed action plan, outlining each step this company should take to reduce costs in the identified areas. This plan can involve following sections:
1. Introduction: a brief section for background information on the analysis.
2. Strategies for expense reduction in the identified areas.
3. Conclusion: summary of proposed strategies and expected outcomes.
4. Implementation timeline: A table for planning when each action step will be undertaken.

Test yourself

What have you learnt?

Completed a comprehensive review of the Closets & Blinds company case study.
Employed the steps of Decomposition, Pattern Recognition, Abstraction, and Algorithm Design to tackle business financial challenges.
Analysed financial data to create an informative data dashboard for expense tracking and trend visualisation.
Created an action plan for cost reduction and business optimization.
Explain the concept and application of computational thinking in solving business problems.
Comprehend the significance of computational thinking steps in breaking down complex problems and making strategic decisions.
Utilise computational thinking to identify key areas for a business problem, specifically cost reduction and process optimisation.
Create algorithms or action plans to methodically reduce expenses and increase operational efficiency.
Cultivate an analytical mindset that leverages structured problem-solving approaches.
Promote a culture of data-centric decision-making and collaboration to drive business optimisation and sustainability.

Conclusion

In this comprehensive module centred on applying computational thinking to tackle "Business Challenges," you commenced on a strategic journey, utilising the four pillars of computational thinking—Decomposition, Pattern Recognition, Abstraction, and Algorithm Design—to dissect and navigate complex business issues. By methodically breaking down complex financial data into manageable components, recognizing spending patterns, abstracting actionable insights, and creating a detailed plan for cost reduction, you must have gained invaluable skills. This process has highlighted the importance of a structured approach to problem-solving in business, ensuring that decisions are not just reactive, but proactive and informed by a deep understanding of the underlying issues.
To take these skills further, you can deepen your understanding of computational thinking and apply the same steps to the business problems in real businesses on Business Case Studies.