In the analytics world, data is practically useless until you can interpret it. This is why Excel’s solver tool is so revolutionary. Whether you are projecting budgets, fine-tuning resources, or sorting out patterns, the solver function can indeed tweak the efficiency of your Excel work. Below are the top six solver functions for a data analyst to add to their skillset:
Every solver model must have an objective. It is a specific function that you want to minimise, be it costs or time, or maximise, such as revenue, profit, or, as in linear programming, efficiency. The objective is a direction for your Solver, as it gives the programme a measure to calculate the optimal answer based on your input.
As a data analyst, your objective is to ensure the final solution matches your plan. For example, if your company’s goal is to reach profitability, the objective function might indicate total revenue minus expenses.
Decision variables are the inputs that Solver can change to achieve the objective. For instance, these could be prices, production volumes, time, etc. It is important to get the right decision variables because Solver finds its solution within the range of these inputs.
Reasonable and measurable variables should be chosen in the model. Too many variables can make Solver take a long time to process data, while too few can affect precision.
Constraints are the principles that make your solutions realistic. These guidelines make sure the Solver does not recommend anything that is not feasible or realistic. For instance, you can limit the number of working hours or establish a budget cap in your model.
By implementing carefully considered constraints, you determine the optimal level of the Solver’s flexibility. In turn, this step helps accurately recreate the real-world factors that impact your analysis.
Solver’s GRG Nonlinear is the method for solving complex models related to the nonlinearity of relationships. It changes the variable slowly and recalculates, because it also checks to see if you use all the restrictions of an optimal solution.
For instance, if you are an analyst who operates by thinking about dynamic pricing or forecasting models, GRG Nonlinear can give very precise real-world evidence and evidence. Most of the time, data in finance, marketing, and operational analysis do not follow simple direct lines, and it is very important to understand this.
When all your problems are linear, as well as your equations and constraints, always use Solver’s Simplex LP method. It is inarguably the fastest and most credible option, especially when your decisions are about resource distribution or production planning.
Simplex LP provides the analysis with a framework of equality, which finds the available balance. Ultimately, it helps your firm to eliminate waste resources and increase the productive ones.
This step is the most important in terms of assessing your model’s risk and its tendency to adapt. Likewise, sensitivity analysis discloses the most sensitive components and the areas that require alteration.
For the analyst, sensitivity analysis is about having faith in the recommendations you provide. It means you no longer test how adjustments influence output, but you forecast them.
Solver is more than an Excel add-on—it is essentially a data analyst’s quiet little helper. When you familiarise yourself with its basics, you will be able to conduct experiments and extrapolate outcomes while honing your accuracy in this.
