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Nicholas that’s a good advice! I saw that you wrote an article on How to Forecast Intermediate Demand on medium but it’s behind the paywall! 😢

Hi I am an S&OP manager based in Pakistan, I wanted to know how can I get access to your book? also I have been working to create baseline forecast in my organization but since there are no historic demand driver details available its very difficult to generate baseline forecast, any mathematical approach that I can use to atleast begin with forecasting for longer period of months?

Hi Nicholas, when it comes to feature engineering for future covariates, which features are a must according to you? The only future feature I've been able to implement is the lag features, however one is then constrained by the lowest lag feature, i.e if you have lag 7 day feature, you can't predict further than 7 days into the future. What other future variables are there that one would know in the future, apart from holidays and company specific things like marketing costs, promotions etc?

🎯 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.* Made with HARPA AI

Thanks for the vides Nicolas! I have read all your books - what a fresh take! Please make a video for niche demand planners and inventory controllers to which I belong. That is MRO spare parts inventory which is fraught with intermittent demand and skewed probability distributions. If you ever revise your books 'Data Science for Supply Chain Forecast' and 'Inventory Optimization' please include these topics. I read your article on Croston Method in towards data science and it was very well written with a practitioner's perspective. Whenever possible please make a video on demand forecasting and inventory control for spare parts. Thanks!

Love it! 🙏 Thanks for sharing Nicolas! So once we have a great forecast using these three steps, how does one implement the inventory optimization element? I’d be curious to hear your top 3 on the IO portion. Of course, your IO book goes into this quite well already!

Hi Nicolas, it is a very explanatory webinar about outliers. I have a question. Do we need to apply outlier detection process based on train data, or whole data (train+test)? I hope my question is clear.

Beautiful and intelligent Forecasting session

Muy buen vídeo; la explicación quedó clara con el ejemplo desarrollado en Excel

This is really nice.

Hello dear , I want to congratulate you for the content and the skills that you teach people . I have a question : the forcast ,like you describe it , is applicable for the retail demand planning and the MTO strategy , that's right .Because I don't think that this kind of forcasting is relevent in an MTO industry where the demand is not stable .

Thank you for your webinar. I have a question regarding outliers. I am conducting a serum biomarker research (medical research) consisting 50 patients vs. 50 controls. I have 3 cases having non-detectable values (above the detection level) in the same group. This group is already have higher levels than the other one. I do not want to remove those cases and lose the data. Which strategy should I use ? Should I imputate them with the mean value of the relevant group? or Should I enter the measured highest value/s instead of undetectable ones ? or else ? Thank you in advanced.

Amazing! Thank you for sharing Nicolas!!

Hi Nicolas, to calculate the bias in case of many unpredictable new products introduction and phase out, the latter are not considered because the time series are not available in the forecast period while the former are considered and demand is greatly overestimated, as the 2 cases do not compensate, the overall bias on the product portfolio is always positive. How do you recommend managing this case? Should I consider as a forecast error also the forecast 0 on NPI even if their time series were not available at the time of the forecast?

Hi Nicholas, you explained it very well. Could we access the PowerPoint presentation you used?

Thanks Nicolas for amazing content.. how can i join your live sessions?

Awesome session! I'm curious, how would we forecast zeroes? lets say we have inventory for such items but they do no sell at particular time period may be.

please make a detailed video on forecasting slow moving intermittent demanded items!

Good summary! I used this to explain to my colleagues

Please which certification would you recommend for a demand planner?

I was waiting for it, Thanks

Do you have the github python code available for these?

Hello Nicolas, what do you think of training a ML model using as input in addition to past demand also the previous month ML forecast enriched by sales? For example, to predict December 2023 demand (M+2) I would use as input features summarizing historical demand + the forecast submitted last month so in september for december (which was M+3) possibly enriched by sales. So if sales enrich a forecast because they are aware of future trends, the following month this information will be captured by the model.

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.

Created one year ago, and is still relevant today! Watched the whole thing and probably will watch it again. Thanks Nicolas! Love this!

BonjourMr , pourquoi vous ne fairiez pas une formation en ligne (payante bien entendu) où vous enseignez le demand planing d'une façon théorique et pratique avec des cas réels , des exercices de prévisions sur excel .....? Nous sommes une génération qui n'aime pas trop recevoir l'information en lisons (même si je ne doute pas que le contenu du livre est pertinant )

Thank you for posting these webinars. Even with all the Q&A on shortages I'm still confused on figuring out unconstrained demand. On your slide you say to bypass it, but in your book it says to censor it. Are you meaning the same thing for both the slide and book? Also, on the slide in this webinar, that's also book, it looks like you're using a default value for demand, which looks to be the last demand value before the shortage for the duration of the shortage. Is that what you use? Your book mentions forecasting techniques that might help estimate unconstrained demand, but I can't find any examples. Can you share those techniques? Do you use machine learning techniques, or an equation? Thanks!

What great content! I just finished reading demand forecasting best practices and found this video in the footnotes. Very cool, several learnings, thank you!

How do you capture a demand for a manufacturer in a b2b setting. As orders are been placed and stored in the erp system. Do you use the quantity of order placed as the are intermittent in nature.

what if my lead time is different from material to material?

Great content !!! Thank you for sharing.

Hi there I have a question, exist a tool like COV for trends or seasonality.

Please make video on forecasting intermittent time series data. I tried croston, tsb etc but results are pretty bad.I have only 8 months data . Will you please suggest some methods.

I just have a question on the first one; why do we focus til' M5; why not further and then how further do we forecast? Like a dynamic programming problem; we can keep focusing til' the end of the planning horizon to assess what's a good position at M5, M4,... right?

Would be interesting to get you opinion on MAPE to compare multiple forecasts (or to use as performance metric for to evaluate multiple time series), since RMSE, MAE are not suitable to do so.

please make a video on forecasting of slow moving intermittent and lumpy demand patterns such as those encountered in MRO parts demands. How to use Croston method to forecast mean demand and its variance/std dev and then how datascience forecasting can help in such cases.

Great presentation.

Excellent

Hello Nicolas , Kindly i need your email to contact you

/watch?v=kkpFZA1sVSA&t=376s

In the next edition please include the forecasting and inventory control models for intermittent items especially spare parts. For example MCROST and (S,S-1) and how to implement them in Python.

Do you have any Kaggle notebook or GitHub repo for this work?

Thanks for this webinar. You did a great job giving a high level explanation on the ML concept. I was expecting to have comparaisons between models. Data Scientists should focus more on showing the results of experimentation then advertising the concept. I still don’t know of I should invest money and time to build a POC

What are the best models in your experience?

😂 ᵖʳᵒᵐᵒˢᵐ

Thanks Nicolas, this is very good. I am always a believer of Simulation, and with all the cloud computing getting more affordable, using Simulation to optimise inventory seems to be the way forward

Hi Nicholas read both your books and am introducing them in curriculum in our organization. We forecast and order spare parts for MRO to support a wide variety of equipment. The kind of demand we encounter is low, intermittent and lumpy. Please include methods for forecasting such demands in your new book or in revised editions of your previous books.

Hi Nicholas read both your books and am introducing them in curriculum in our organization. We forecast and order spare parts for MRO to support a wide variety of equipment. The kind of demand we encounter is low, intermittent and lumpy. Please include methods for forecasting such demands in your new book or in revised editions of your previous books.

Hey Nicolas, thanks so much for sharing your knowledge! Have a little question for you, let's say there is this company X for which I have done a demand forecast for 2022. Now, this company has to go through a re-branding process in July, what would you say is a sensible way to adjust/modify/include that event in my forecast? Because I feel that my forecast will over estimate demand for Q3 and Q4. Thanks!

Data quality and incorporating relevant inputs/predictors based on expert domain knowledge are extremely important. But the dude constantly rubbing his face and fidgeting had me on the edge lol. Which models have you found work best? RandomForest, XgBoost, Facebook Prophet, ESRNN, and DeepAR models all seem promising to me.