Forecast Value Added: Concept and Case Studies

Forecast Value Added (FVA) is S&OP and planners' primary tool to assess if their demand planning process is running smoothly and adding business value.
This video explains what FVA is, how it works, and how you can implement it.
Moreover, two guest speakers explain how they implemented Forecast Value Added at Sanofi and Rich Product Corporation.
If you want to learn more about Forecast Value Added, you can also,
- Read my dedicated article: / forecast-value-added
- Read my book on the best practices for demand planning: www.amazon.com/Demand-Forecas...

[@totti5557]

🎯 Key points for quick navigation:
00:29 *📊 Forecast Value Added (FVA) assesses how different teams contribute to improving or worsening forecasting accuracy.*
02:05 *🔄 Demand planning processes typically involve automated baseline forecasts adjusted by teams to enhance accuracy.*
04:19 *🎯 FVA aims to ensure forecast accuracy improvements without excessive time spent on adjustments.*
05:02 *📉 FVA framework tracks how each team's adjustments impact forecast accuracy positively or negatively.*
08:16 *📈 Comparing forecasts to benchmarks like moving averages helps assess the added value of forecasting models.*
11:16 *🎯 Setting accuracy improvement targets relative to baseline performance can be more effective than absolute accuracy targets.*
14:41 *💰 Evaluating forecast errors based on value helps prioritize improvements on high-value products over low-value ones.*
19:28 *🌐 Forecasting across various time horizons (short, medium, long-term) supports strategic supply chain decisions.*
23:09 *📊 Forecast Value Added (FVA) helps identify SKU-level performance, guiding decisions on where to focus and where improvements areneeded.*
23:38 *🔄 FVA encourages a positive feedback loop by comparing market performance against statistical baselines, fostering model improvements.*
24:31 *🌐 Different forecast horizons (short-term vs. mid-to-long-term) require varying model strengths, prompting discussions on model integration.*
25:12 *🤝 Collaborative discussions using FVA help align marketing and finance teams by highlighting where judgmental adjustments add value.*
25:49 *📉 Separating positive and negative adjustments in FVA reveals insights into which adjustments enhance or diminish forecast accuracy.*
27:01 *🎯 Forecasting supports supply chain decisions, aiding in manufacturing and procurement planning crucial for business operations.*
46:55 *🌍 Different countries and industries may require tailored risk management strategies in pharmaceutical production to ensure patient needs are met without compromise.*
47:22 *🤝 Collaborative relationships between planning teams and sales are crucial for mitigating forecast overrides, emphasizing education on supply chain dynamics and outcomes.*
48:27 *📊 Presenting a range of forecast possibilities enhances decision-making by providing stakeholders with more nuanced insights and flexibility.*
49:20 *💡 Implementing statistical engines requires effective change management strategies to shift from manual to automated forecasting processes, emphasizing education and gradual adoption.*
51:12 *💼 For small to medium-sized businesses, affordability and implementation time of forecasting tools can pose significant challenges despite their potential benefits.*
54:04 *📈 Transitioning from manual to automated forecasting involves proving benefits through accuracy metrics and building confidence in system outputs to foster acceptance among demand planners.*
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[@user-se5os5yx6s]

Hi Nicolas, thank you for sharing this. I have a question for you on forecasting error metrics, I know you don’t like MAPE and I agree, but what do you think of WAPE i.e. sum of SKU (actual - forecast) divided by sum of all SKU actuals ? I think it’s a quite good accuracy metric and also easy to explain to business stakeholders as it is a percentage.

[@nicolasvandeput-SupChains]

Hello, indeed that's the one I like to use. I call it MAE%. Don't forget to look at it in combination with bias.