How do you adjust forecasting models for changing business operations?

Pharma industry required to consider below drivers. a. Product portfolio management b. Substitute products launch. c. Launch of advance products than existing products d. IP concerns (off Patent/About to expiry/expiry after 5 years) e. ROI issues f. Leverage product life cycle g. Demand & supply in National & International markets h. Study on overall firm strategy(product/people/market/Finance) and its impact on the growth strategy i. Monitor & implementation of automation along with digitation. j. EHS k. Core values/Ethics

Adjusting forecasting models for changing business operations involves a combination of data analysis, model adaptation, and strategic decision-making. Here's a a few process 1. Data Analysis - Continuously monitor and collect relevant data on key business operations. 2. Model Evaluation: - Regularly assess the accuracy of your existing forecasting 3. Model Selection 4. Data Preprocessing: - Ensure that the data is used in the forecasting. 5. Incorporate New Information: 6. Regular Updates: - Update your forecasting models. 7. Scenario Analysis 8. Expert Input: 9. Continuous Monitoring - Implement a monitoring system to keep track of how well your forecasting models are performing. 10. Feedback Loop: 11. Documentation

In my opinion, the first step and most critical step in effective and efficient forecasting is to ensure that the model itself is as automated and standardised as possible - this includes data sources, calculations and other assumption adjustments. All too often, I have seen models in excel with clunky/manual calculations and data source inputs which are inefficient. Once you have a smooth model, identifying performance drivers, updating assumptions and producing reliable forecasts become a lot easier!

Adjusting your resources and production line is important to ensure the business is profitable. Running different models to ensure you have the right amount of resources to handle the current and forecasted business needs. Like if you get 10 new clients a month and by the end of the quarter you will have 30 new clients so you hire someone now to ensure by the end of the quarter, a resource is present to handle the 30 new clients and ensure business continuity. Similarly many models are available to predict the production line and the resources.

You must know and understand the environment within which you operate. Start off by creating a few broad macro scenarios which have easily identifiable leading indicators. These should guide your thinking on the fundamentals of the business operations and model. They should be tested regularly to ensure you not going off track at fundamental level.

In my experience as a senior business operations leader, it's crucial to consistently review data sources and methods. Rely on fresh sales and market data. For example, when I noticed a surge in online sales, I shifted emphasis to digital trends in our forecasts. It's all about staying relevant.

Review data trends using time-series analysis for sales. For example, review seasonality pattern at channel or brand level to identify opportunities Also, implement methods to identify outliers and remove impact of promotional sales to get accurate baseline forecast

Reviewing data sources and methods is essential because it: 1. Ensures the accuracy and reliability of data. 2. Helps maintain data consistency and integrity. 3. Facilitates informed decision-making. 4. Ensures compliance with regulations. 5. Enhances data security and user confidence. 6. Optimizes data management processes. 7. Reduces the risk of errors and costly mistakes. 8. Supports scalability and future growth. 9. Maximizes the value of CRM investments.

When updating assumptions and scenarios, always ground them in real-world events and current data. For example, if there's a sudden shift in industry regulations, revisit your assumptions to ensure they align. Keep them relevant and actionable. Trust your experience but be data-driven.

Updating assumptions and scenarios is essential because it: 1. Reflects accurate data and insights. 2. Adapts to changing business conditions. 3. Mitigates risks and improves decision-making. 4. Optimizes resource allocation. 5. Maintains relevance and flexibility. 6. Supports a customer-centric approach. 7. Provides a competitive advantage. Updating assumptions and scenarios is crucial because it ensures that your CRM strategies and business decisions are based on the most current and accurate information. This is essential for adapting to changing conditions, making informed choices, mitigating risks, and maintaining a competitive edge in a dynamic business environment.

Most organizations use a robust S&OP process to create a single, unified forecast by considering various assumptions and scenarios. Therefore, it's advisable to develop both optimistic and pessimistic demand plans before S&OP meetings. During these meetings, the following guidelines help review scenarios and determine the final demand plan: - Compare statistical, demand, and sales forecasts. - Evaluate the budget plan against the consensus demand plan. - Analyze year-to-date revenue versus projected revenue. - Compare the last vs current consensus demand plan

When communicating changes in forecasting models, it's vital to be clear and concise. Always explain the 'why' behind any alterations. For example, if a new market trend affects sales predictions, inform stakeholders of this shift and its implications. Being transparent builds trust and ensures everyone is on the same page.

RevOps can communicate changes and their implications by: 1. Preparing a clear message. 2. Identifying key stakeholders. 3. Customizing communication. 4. Using multiple channels. 5. Providing context and benefits. 6. Addressing concerns proactively. 7. Encouraging two-way communication. 8. Offering training and resources. 9. Sharing a timeline and milestones. 10. Providing continuous updates. 11. Measuring and communicating impact. 12. Celebrating successes. 13. Gathering feedback for future improvements. 14. Maintaining documentation for reference.

Communication is a key and often overlooked aspect is communication to the last mile stakeholders. It pays to communicate the reasons behind the change in forecast, underlying reasons and to take their buy-in to ensure that the actionables generated are understood and acted upon by the team.

Keep it simple, precise and clear for the targeted audience when adjusting the forecasting models. One of the cases can be to effectively communicate forecasting strategy for new products to sales, marketing and leadership to achieve effective alignment

When monitoring results, remember it's not just about numbers. Regularly hold team check-ins to discuss forecast outcomes. For example, if sales projections were off, chat with the sales team to understand market shifts. This human touch, combined with data, refines accuracy for future forecasts.

Use robust S&OP Process to monitor, evaluate and communicate the updates. Capture risks, concerns, opportunities and decisions required as part of regular S&OP meets to achieve better results

RevOps should adjust forecasting models and monitor results to: 1. Improve accuracy. 2. Adapt to changing conditions. 3. Mitigate risks. 4. Optimize resource allocation. 5. Evaluate performance. 6. Achieve goals. 7. Enhance operational efficiency. 8. Enable data-driven decisions. 9. Promote transparency. 10. Foster continuous improvement.

Here is what I recommend - Always factor in the agility of your operations. Rapid shifts in business can outpace forecasts. For example, if a key supplier faces an unexpected shutdown, how quickly can you adapt? Ensuring operational flexibility can make your forecasts more resilient to real-world disruptions.

Adapting forecasting models to evolving business operations is a critical aspect of data-driven decision-making. In my experience, I've found that a multifaceted approach is key to staying ahead of these changes. First, it is imperative to effectively ingest continuous data and maintain its quality to ensure that our forecasts are grounded in real-time insights. We also need to explore various scenarios to account for different operational changes, including market expansions, product launches, or supply chain disruptions. To strengthen our forecasting, we conduct sensitivity analyses to gauge the impact of various factors.