Demand patterns, supply chains, and logistics – not only did the past couple of years highlight pressing gaps in global networks but also expose the fragility of traditional forecasting and planning tools. Alongside dealing with short-term operational changes necessitated by the pandemic, such as workforce planning, inventory reconfigurations, and transportation constraints, businesses have also had to examine viable methods of ensuring agility and resilience in the longer term.
Creating and iteratively modelling a range of options to understand the impacts of business decisions has become the need of the hour and, as a direct result, what-if analyses and scenario modelling are now increasingly being used to inform a wide range of business decisions. 
Interestingly, several large organizations continue to rely on spreadsheets for scenario planning, leading to multiple assets that must be tracked and maintained. While spreadsheets are known to be an effective and speedy means of conducting basic what-if analyses, they are typically used when a hypothesis needs to be tested quickly, when data volume and scenario complexity is low, when documentation on the model is available, and when businesses have the know-how to address the risks created by this method. 
However, multiple spreadsheets to create, edit, and update makes data governance and traceability challenging. Further, with manually listing assumptions, copy-pasting data from various sources, and other time-consuming tasks, data complexity, reproducibility, and explainability become major limitations for stakeholders. Considering the shift toward automation in large businesses, the specific focus on scenario simulation has similarly shifted towards using analytics iteratively, and deriving the best insights from available data. 
While qualitative scenario analyses are commonly used across industries, AI-based simulations that quantitatively explore the causal relationships between drivers and develop contingent plans of action have only recently come to prominence, following the pandemic. 
Moreover, model-based AI—which uses available data as opposed to historical data—saw rapid adoption in the past year. According to the World Economic Forum (WEF), as more data was generated, data-rich and model-rich approaches were then combined, creating hybrid solutions. The WEF further recommended focusing on capturing the inter-relationships of multiple domains (e.g., demand, production, supply, finance) as well as agile data science methods that account for the speed, urgency, and uncertainty of decision-making.  Two scenario planning types—system dynamic and agent-based models—allow for this:
System dynamic (SD) models: These models can be applied to a range of scenarios including global markets and economies, climate change, product and service life cycles, and supply chain and inventory management  to derive insights on business performance. With stock and flow sequences being the two key inputs for this model, decision-makers can get holistic insights on business dynamics as well as how cause-and-effect sequences occur within systems to inform business performance over time,  for instance, with the impact of purchases (flow) on inventory (stock) in a given period. This method involves intuitive, graphical representations of models—making it easier to use and understand than complex spreadsheets—and also allows for quality control and real-life insights,  enabling truly informed decisions.
Agent-based models (ABMs): These models follow a bottom-up approach—the actions of individual agents within systems are examined and mapped to effects on the larger system, for instance, in terms of modelling the impact of human behavior on COVID-19 transmission in communities. In moving from simple theoretical models to representations involving real-world data, ABMs are significantly aiding scenario planning and can be used to examine a range of factors, from the impact of harvesting and wood prices on furniture markets to the economic impacts of climate change in the remote Canadian north.  ABMs are set to become useful techniques in post-pandemic scenario planning, with use cases ranging from traffic optimization and energy consumption to land use and economic recovery studies.
Scenario analyses have become a pre-requisite to informed decision-making on contingencies, including for supply chain operations, demand planning, and workforce planning. They allow for sensitivity analyses, drill downs, and thorough examinations of data to explore how different scenarios can impact businesses in the short-, medium-, and long term. For instance, a business user can use what-if scenario analyses to examine how project\ timelines can be impacted by minute factors such as supply shortages or a fractional increase in labor costs.
In addition to providing specific and detailed views on business information and areas of improvement, what-if analyses also present a range of overarching benefits, including the following:
Let’s take a look at some use cases where what-if scenarios can be used to improve business decision-making:
1) Customer retention: If a company forecasts $14 million in sales for the upcoming year, based on a deal close rate of 35%, the impact of customer retention can be studied through what-if analyses. For instance, if the close rate is increased to 40%, the revenue projections can improve to $14.5 million.
2) Reducing risk: A Chief Risk Officer (CRO) can use a model that captures potential risks, such as demand shocks, as well as the causal links between these risks (poor advertisement reception leading to drop in demand). In addition, the CRO can identify potential triggers, such as adverse weather events and drops in workforce numbers (including due to diseases and pandemics). With this information, a range of what-if scenarios can be applied to examine business impacts, for instance, weather events or disease leading to absenteeism. High-impact changes, as well as surprises, can subsequently be identified using what-if analyses. The CRO can then refine these scenarios by generating additional hypotheses involving other risk factors.
3) Weather impact: Agricultural businesses can use what-if scenarios to examine the impact of weather on harvest quality. This can help them forecast sales as well as explore avenues for future investments, including in terms of infrastructure, damage prevention, and storage.
4) Hiring outcomes: Individual functions can examine the effect of new hires on revenue. For instance, if 6 people are added to the team over the next 6 months, what would the impact on revenue be? Such questions can be effectively explored using what-if analyses.
5) Shifts in demand: For an F&B business, tracking changes in consumer demand is essential to business decision-making. Using what-if analyses, newly emerging consumption patterns, such as those for vegan or meatless products, can be factored in to examine the impact on the supply chain and sales, thus informing future production needs.
What-if analyses present actionable insights across functions, helping businesses make informed decisions in the short- and long-term, pivot effectively in the aftermath of disruptions to explore a range of scenarios, and allowing them to gain a comprehensive overview of key drivers impacting operations. Asking “what if” questions and making the best use of available data therefore becomes an essential starting point in ensuring both resilience as well as competitiveness, unlocking new business opportunities across a range of contexts.
A data scientist-turned marketing professional who has led Retail, Pharma, and BFSI engagements as well as spearheaded internal and external marketing initiatives, Vignesh Subramanyam’s career has been as diverse as it has been long. In his free time, he can be found travelling, trying his hand at painting, and sampling Indian cuisine.