How can you manage the complexity of financial forecasting for multinational companies?

A good starting point would be to understand the reality of the business and its business model and to then reflect that story on the revenues and costs with the objective of focusing first on the big building blocks and then breaking them up further if need be. The first tip is to hence aim to have the forecast reflect the story of the business. Coupling this up with regular updates from the Sales team (mainly regarding opportunities and risks) and finance team (mainly regarding expenses) would allow to regularly update forecasts and to increase their accuracy.

Through my experience, we need to have robust ERP system for integration from different sections for real time analysis. Collaboration is key. We can use different methods like acenario planning with best, worst and most likely outcomes within the forecasting models. After that, there should be regular updates to reflect the market changes. Lastly, risk management is very crucial to develop mitigation strategies from potential financial risks

Understanding the business model is important. Take a global tech giant like Apple,as exmple. Its primary sources of revenue are iPhone sales, services, and accessories, but exchange rate fluctuations can influence component costs, impacting profit margins. Apple allocates resources globally, investing in R&D across regions. So to manage risk, they maintain diverse supply chains. For instance, during covid, production disruptions in China had ripple effects, thus Apple's strategy focuses on product innovation and market expansion but faces potential risks,such as evolving regulations. With this comprehension, accurate forecasts can be made, such as predicting how currency fluctuations might affect Apple's profitability in various markets.

One solution to managing the complexity is "fractal forecasting," inspired by nature's self-similar patterns. Instead of one global forecast, create nested forecasts at regional, product, and even individual customer levels. This "zoom-in" approach captures local nuances and allows for more flexible adjustments. For example, Unilever used this method to predict sales for their diverse product lines across various markets, factoring in local trends and competitor activity. It led to a 5% improvement in forecast accuracy!

The multinational work environment comes with multiple layers of hierarchy. Before you start taking calls for your controlled scope territory, it's important to understand what are the driving factors across different geographies, how are markets interconnected across multinational organisations and what is the culture through which the financial forecasting is requested to be consolidated at not just the regional but also at global level. If you follow the right understanding, there will be reduction of duplication and you'll be able to do the job well right in the first go

Based on my experiences with a global company, providing a clear template from the global headquarters is not only practically important but also beneficial. When regional offices receive well-defined templates, it simplifies their work significantly. Additionally, it aids the group team in consolidating information. This approach can substantially increase efficiency, potentially tripling it!

Best practice is to regular publishing/tracking of all reports pertaining to P&L accounts, Revenue/Expenses which would automatically make sure an error free & efficient forecasting.

In adhering to best practices, I adopt a universal approach to financial analysis, recognizing that certain financial indicators are consistent across diverse businesses. Analogous to the shared physiological functioning of human organs, critical aspects such as cost, profit trends, industry versus company margins, cash flow, and control audits serve as the fundamental organs of any business. Implementing a standardized set of control questions based on these factors enables a comprehensive assessment, allowing for an efficient and consistent framework to gauge the overall health of any business entity.

Machine learning allows to identify drivers at a very granular level (down to SKU if required), and technology can handle thousands of models running in parallel for each product/service or category. These drivers and their contribution can than be leveraged by FP&A and/or managers responsible to establish their forecast, therefore shifting their focus from generating a number to more value-add activities, such as doing scenarios with the drivers and refine the forecast.

Financial forecasting, a critical aspect of strategic planning, relies heavily on data analysis. By granting financial professionals the autonomy to analyze data, we enable them to develop more accurate and insightful forecasts that guide our decision-making processes. This autonomy empowers them to go beyond mere numbers and explore the underlying drivers of financial performance, leading to better-informed strategies and improved financial outcomes.

The use of technology is imperative for running a standard budgeting or forecasting process for a multinational company. The use of FP&A software (such as Hyperion Essbase or Anaplan or cloud-based incumbents such as Vena or Planful) ensures a consistent approach for data collection and compilation. Furthermore, dealing with multiple currencies becomes straightforward. Another plus is using system native error checking and data validation leading to fewer iterations and post-submission queries.

Hoje como todos os mercados de negócios estão interligados pela economia mundial, temos ferramentas inteligentes que nos ajudam a fazer esse gerenciamento mais assertivo. Porém o fator humano e que está na rotina diária são as melhores pessoas para analisar esses dados e conseguir identificar os gargalos que temos mensalmente, semestral e até anual. Em conjunto com as aéreas que tenham um menor aproveitamento dessa complexa previsão consiga enxergar o todo na cadeia da organização e do mercado que a sua companhia está inserida e com isso trabalhar pontos estratégicos para a melhora desses resultado e com uma previsão financeira a médio e longo prazo mais assertivas.

Including external factors are critical, however for companies working in hundreds of different markets incorporating this could be challenging. Bucketing them based on certain common attributes could help. Another aspect that is sometimes missed out is interdependence of external factors across markets. With the interconnected world, macro factors in one market may have bearing on others. The forecast should incorporate this.

External strategic analysis is critical in financial forecasting for multinational companies. External strategic analysis can broadly include review of industry outlook and trends, consumer/ buyers behaviour, existing competition and in pipeline, availability of substituted or alternative products, economic conditions (interest inflation, exchange rates and key drivers ), stability of taxation and regulatory policies, country political and social environment, geographical conditions ( landlocked country, neighbouring country outlook ) and global environment. This initial analysis can help in formulating certain baseline assumptions to drive your forecast, and these assumptions will essentially require periodical review and its impact.

Create a structure that allows for input, where possible, from in-region staff / experts to account for market specific differences (regulatory, political, cultural). Sometimes these are subtle but often they are critical to accurately forecasting and planning.

Transparency in communication remains pivotal. Beyond highlighting key drivers, risks, and opportunities, ensure that your communication strategy is accessible to a diverse audience. Tailor your messaging to resonate with both financial and non-financial stakeholders, fostering a shared understanding of the forecast's assumptions and limitations.

Flexibility is key, but flexibility without direction is useless. Focus on the goals with the highest returns. Expect to make rapid changes as the forecasting model changes. As this will be a complex multinational model, you must guard yourself against scope creep and letting the details get out of hand.

Na minha experiencia, previsões financeiras em empresas multinacionais devem ser guiadas por drivers claros, por identificação de riscos e oportunidades, e pela incorporação de fatores externos que possam afetar o negócio. A revisão constante desses números, identificação de tendencias e trabalho proativo de busca de oportunidades é chave para o contínuo crescimento e sustentabilidade dos resultados.

In my experience, assumptions in financial forecasting are also driven by type of industry, its impact on society (ESG-related risks), country specific government policy and regulations and global regulations (like conflict minerals). The minerals mining industry is one of the example where I have seen recent cases of government take overs ( example of Zambian government takeover of Konkola Copper Mine and Mopani Copper Mine in 2019/20), and recent shutdown of Cobre Panama mine in Panama which was owned by First Quantum Minerals.