Two years ago, COVID-19 lockdowns threw economies and supply chains into turmoil. Last year, supply chain disruptions and inflation were assumed to be transitory. More recently, the crisis in Ukraine and the lockdown of Shanghai and other cities have further exacerbated matters. Amid this turbulence, it should be no surprise that management teams across industries have abandoned the notion that supply chain normalcy will return anytime soon.
But more than interrupting everyday business operations, today’s supply chain difficulties, along with market uncertainty and price volatility, are challenging the ability of organizations to create timely and accurate financial forecasts. Finance organizations must keep up with these considerable and rapid changes to ensure that the decisions being made across the organization are well-informed and timely. That is why it is more important than ever that finance teams leverage flexible and agile planning approaches, such as predictive modeling, scenario planning and rolling forecasts, to monitor the financial impacts of supply chain challenges and identify both short- and long-term solutions to minimize risk.
Understanding What Drives Demand
Responses from Protiviti’s recent global finance trends survey indicated that enhanced predictive modeling is increasingly being utilized as a tool for managing supply chain challenges. We know that traditional planning models rely on hindsight and internal data, whereas advanced planning models, such as predictive modeling, are focused on key drivers sourced from both internal and external data. Historical sales are not always the best predictor of future demand, and that has been especially true over the past two years given how the pandemic has muddied data. Consequently, organizations will need to incorporate more external data, such as interest rates or consumer purchasing habits, to predict future demand.
To be successful in utilizing predictive modeling for demand forecasting, it is important that organizations align on the key metrics or drivers to be tracked, and the data utilized is free from any biased overrides. A predictive model is only as good as the data used to power it; therefore, it is imperative to source information that is clean and relevant. Additionally, management teams should not override data with gut feelings derived from prior experience. This can lead to inaccuracies, less informed decisions, or the inability to react to change in a timely manner.
Predictive models, coupled with flexible technology and upskilled talent to analyze the data, enable finance teams to quantify supply chain risks and identify short- and long-term trends associated with meeting projected product demand and financial targets.
Informing Strategic Business Decisions
In Protiviti’s global finance trends survey, scenario planning was cited as the No. 1 methodology for managing the current supply chain challenges. This management tool helps business leaders identify catalysts that could affect business, predict potential impacts and outcomes, and evaluate what actions the company might want to take to address the risk.
Imagine that a manufacturer is focused on meeting higher demand for a premium product but discovers that a key raw material may not be available. It may want to run a number of scenarios, such as producing less of the lower-tier products, discontinuing an underperforming product to free up the material, or considering a substitution for the material. In each of these scenarios, the company will need to determine the impact on margins, as well as the costs related to changes in the production schedule, sales and marketing plan adjustments, and potential impact on customer satisfaction. Scenario planning is not just a financial exercise, and it requires collaboration across functions to ensure all factors are evaluated and the potential actions are adequately assessed. Based on an assessment of the impacts of each scenario, action plans may be developed.
Ultimately, scenario planning enables business leaders to quickly and decisively manage risk. Envisioning potential future events can also provide boards with insights to risks and a framework for more timely and effective decisions to address supply chain risks, the related impact on financials and financial forecasts, and business growth drivers that may be affected.
Incorporating Impacts Into Rolling Forecasts
Although organizations have had the luxury of creating financial forecasts on a quarterly basis in the past, volatile conditions today require more frequent forecasts to assess emerging or immediate supply chain risks and opportunities. This is echoed in Protiviti’s global finance trends survey, which indicated that many companies are now forecasting on a more frequent basis. The pandemic has provided a catalyst for many companies to change their forecasting approach to a more continuous rolling forecast model.
When establishing a rolling forecast, companies must assess the adequacy of their technology, cross-functional organizational capabilities and the necessary changes to be made to processes and inputs. A rolling 12 months is generally a good time frame to begin with — it is flexible enough to consider the long term, yet it still enables organizations to act in the short term. Leveraging advanced analytics and predictive capabilities, as well as scenario analysis, will further enable this process. Once scenarios are built, companies can evaluate whether and how their predictions will trigger an adjustment to financial forecasts.
New Normal
While the past two years of turmoil could be considered a temporary hiccup in decades of orderly supply chain functionality, organizations should prepare for ongoing disruptions for the foreseeable future. In this climate of growing global economic and geopolitical uncertainty, businesses have less control over any number of variables that influence the supply chain. But by remaining nimble, utilizing scenario planning and more frequent rolling forecasts, finance functions can mitigate supply chain risk and drive more timely and informed business decisions.
