What this blog covers:
- Understand what-if analysis and how does it work.
- Know why organizations need what-if analysis and what are the best use cases.
- See how AI-powered what-if analysis with Kyvos Viz can change the game.
The modern business environment is experiencing constant fluctuations, limiting how well businesses can predict future outcomes to stay flexible and competitive. What-if analysis acts as a strong force against these obstacles. It establishes a systematic way to investigate the possible effects of various scenarios by changing one or more input variables of a model and then observing the output.
Through advanced simulation, organizations can understand possible results and unexpected situations that might arise. With what-if analysis, business leaders can assess how changes in elements like market state, pricing methods, resource distribution and operation variables affect the key performance indicators (KPIs).
How Does What-If Analytics Work
It’s a given: forecasting business scenarios isn’t a piece of cake. Predicting a clean pattern or a path of action needs more than just making assumptions based on some numbers. What-if analysis defines the journey to the best-case outcomes for even the worst-case scenarios.
The process entails simulating various situations and analyzing their impact on demand forecasting, supply chain management, marketing campaigns or financial planning, among others. Using the results of this simulation, users can adjust variables, such as market demand, advertising budget, inflation, product mix and inventory levels, to test different situations and factors, measuring their impact on business.
In short, it equips organizations with the right tools to explore potential outcomes of key decisions and make informed choices. Shifting and steering the variables in or outside their control, they can navigate the rough waters of current economic ecosystem.
Why Do Organizations Need What-If Analysis?
Analyzing different scenarios helps in identifying and assessing possible risks related to factors, such as changes in markets and customer preferences, adjustments to rules or laws, and even risks related to politics around the world. It gives enterprises a chance to create strategies that can reduce these risks before they arise or go out of hands. Knowing about the possible results from the choices they make decreases their exposure to risk and helps them better prepare for unpredictability.
Another important concern is how resources can be best used, as this impacts both efficiency and profitability. Organizations can use what-if analysis to see how different scenarios of resource allocation affect important measurements like production output, cost-effectiveness, and profits.
Types of What-If Analysis to Meet Diverse Business Needs
When organizations look at potential changes—negative or positive—to variables or assumptions, they can analyze the interplay between different scenarios, sentiments and outcomes. Based on the purpose of queries, the complexity of data and the time horizon being examined, they can choose the best tool to reach the desired results. The types of what-if analytics that ease this transition are:
Scenario Analysis
Multiple changes happen around the markets simultaneously. They may vary from known eventualities—like tax adjustments—to unknown variables. Looking at these changes and their overall influence helps detect any upcoming risks, find untapped opportunities and strategize better. For example, it can help with financial forecasting to find the right portfolio mix for maximum returns. Similarly, scenario analysis for different sales projections can help launch a product with more clarity.
Sensitivity Analysis
What if a variable brings substantial changes to other variables in a model or several variables collectively impact the outcomes? Sensitivity analysis can gauge variables in both instances to evaluate the merits and demerits of each choice.
Understanding how a change in decision can affect annual sales plans or budgets can be the eye-opener an organization needs to achieve its goals. Let’s say that when analyzing material spending for new product development, a change was proposed for the pre-set amount. However, it may now trigger immediate changes in profit, revenue and the total cost of goods sold.
Some Real-World Applications
What-if analysis offers decision-makers an understanding of various situations and their results, from examining the outcomes of business mergers and takeovers to enhancing the effectiveness of supply chain activities. They can recognize the most encouraging routes for growth while lessening possible dangers.
In supply chain management, it helps optimize operations by simulating disruptions, like supplier delays, transport congestion and natural disasters. Hospitals and clinics can use what-if analysis to predict the number of patients they might have, ensure resources are used well and improve their staffing levels to meet changing needs.
Insurers can approximate possible losses while fixing suitable premiums for coverage and reinsurance schemes by simulating situations with different levels of seriousness. Similarly, banks can test their portfolios under tough economic situations by changing variables like interest rates, credit quality and market volatility. In the retail industry, what-if analysis proves efficient in improving pricing, promotions, and inventory plans. It helps retailers adjust according to changing customer choices and market conditions.
Value Creation by What-If Analysis: An Example
Let’s now understand the true value of what-if analytics for modern enterprises with a simple, real-world use case. A retail organization always looks for optimum ways to improve its inventory management. Multiple stores may cater to different products, demographics and locations. However, each store has a specific budget to procure inventory. How will the organization minimize stockouts, excess inventory and wastage while maximizing profits from each store?
For this example, we’ll analyze a change in the inventory reorder point. This is the level at which the company has to place restocking orders to maintain a smooth operational flow.
- Scenario 1: What if they maintain the current reorder point of 50 units?
Analysis: This will maintain a steady inventory flow, but stocks might occasionally run out of best-selling products, leading to lost sales. - Scenario 2: What if they increase the reorder point to 80 units?
Analysis: The stores will rarely run out of stock, but excess inventory might tie up their capital and storage space. - Scenario 3: What if they reduce the restocking point to 30 units?
Analysis: The company will minimize the excess inventory but risk stockouts and missed sales at this point.
Recommendation:
Considering all these scenarios, the retail teams can observe the effect of adjusting their reorder point to maximize revenues without losing any sales opportunities. This can also help find an optimal balance between inventory costs, sales, inventory flow and customer satisfaction.
How Does AI-Powered What-If Analytics Up the Ante
What-if analysis involves changing the input variables for different scenarios and adjusting the static models accordingly. All this happens manually, taking up too much time for data teams. Enter AI-powered ML models. Using machine learning technologies, these models learn, adjust and improve. Advanced algorithms help them make autonomous assumptions, identify trends or patterns and conduct comprehensive tests before sending an output.
AI/ML-enabled platforms can also help organizations handle larger datasets than manual analysis and traditional tools. When variables are based on the entire spectrum of data, they lead to more accurate results. Together, what-if analytics on an AI-powered platform is the recipe for forecasting with full confidence.
In the same example as above, when the retail enterprise changes its inventory reorder point to a higher number, it changes a key variable that may affect the supply chain management.
However, the static models are not always designed to consider other variables affecting the supply, such as transportation delays, supplier reliability, production disruptions and unexpected events like geopolitical issues.
ML-powered models are trained to process such comprehensive and complex variables as well, leading to dynamic modeling for more accurate forecasting.
Kyvos Viz for Faster and More Accurate What-If Analysis
Organizations can take the guesswork out of their business forecasting with Kyvos’ AutoML capability. They can scour the full length and breadth of enterprise data, find hidden patterns in it and derive outputs they can rely upon. The process includes auto-selection of the best-fit model to meet changing business needs while factoring in external constraints, such as seasonality, sentiments, campaign inputs, customer interactions, business scenarios and other elements.
With Kyvos, ML models can also be trained on any dataset size and any time range of historical or real-time data. The platform allows users to import external algorithms as well into the system for faster, more comprehensive results.
Looking Ahead
What-if analytics has always been and will continue to be a transformative tool that can help enterprises strategize with a focus on data-driven insights. AI/ML technologies give it an upward boost. Kyvos brings forth advanced data modeling capabilities and automation so that business users with minimal technical expertise can also create accurate business forecasts and make data-informed decisions.
Contact our team to explore our capabilities in detail.