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• 
  @arnoldkakas63432 сағат бұрын

  very nice explanation. Is geometric mean being used for evaluation as well? You could get more "typical" error compared to average.

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  @premnbusinessКүн бұрын

  is there any way to connect you?

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  @j.b.13426 күн бұрын

  Great speech! Where do you recommend people learn more about ML as it pertains to forecasting?

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  @nicolasvandeput-SupChains6 күн бұрын

  Hello, if you want to learn how to make your own models: www.amazon.com/Data-Science-Supply-Chain-Forecasting/dp/3110671107 (It's a step by step approach starting from 0, so don't worry if you are not an expert today.) If you want to understand how ML impacts demand planning and how your teams should work with it. www.amazon.com/Demand-Forecasting-Practices-Nicolas-Vandeput/dp/1633438090

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  @Terracotta-warriors_Sea21 күн бұрын

  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! 😢

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  @ai7849Ай бұрын

  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?

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  @ArkantosiАй бұрын

  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?

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  @totti5557Ай бұрын

  🎯 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

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  @Terracotta-warriors_SeaАй бұрын

  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!

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  @joshuabradshaw1647Ай бұрын

  Second this! Well said 👏

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  @joshuabradshaw1647Ай бұрын

  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!

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  @ademakgul67682 ай бұрын

  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.

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  @nicolasvandeput-SupChains2 ай бұрын

  I would try not to do any statistical outlier detection. I would invest more in data cleaning. If you remove outliers from the test set, you are somehow overfitting - so I would not do it.

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  @p-lf5gy2 ай бұрын

  Beautiful and intelligent Forecasting session

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  @daviddiazpereyra84873 ай бұрын

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

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  @ellyothim3554 ай бұрын

  This is really nice.

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  @user-jz5vn4pi1j4 ай бұрын

  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 .

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  @nicolasvandeput-SupChains4 ай бұрын

  Hello, you can apply it to MTO but it might require some differences. For examples you could focus more on forecasting raw material, or include as a features in your ML engine preorders. Or contractual terms/budget.

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  @user-jz5vn4pi1j4 ай бұрын

  @@nicolasvandeput-SupChains Is that what we call supply planning ? Thank you in advance.

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  @sevilayvural38964 ай бұрын

  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.

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  @nicolasvandeput-SupChains4 ай бұрын

  Hello, sorry I specialize in supply chain demand planning - I don't think I am legitimate or have the experience required to advise you regarding how to conduct medical research. All the best!

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  @user-oz7pp1lc3u5 ай бұрын

  Amazing! Thank you for sharing Nicolas!!

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  @motmot7845 ай бұрын

  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?

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  @nicolasvandeput-SupChains5 ай бұрын

  Hello, this is quite a complicated case. You could compute bias in two different flavors, with and without NPIs. The idea is that you don't want to bring the message that bias is close to 0% whereas obviously, you missed 10% of NPIs. But the responsibility for these NPIs might lie with another team.

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  @motmot7845 ай бұрын

  Thank you Nicolas. Actually what happens is that if I include NPIs I get an unbiased forecast overall because NPIs compensate unpredictable phase out products, while if I don’t include NPIs I get a positive bias (globally on the product portfolio). Maybe global metrics in this case are not meaningful and I should look at the distribution of Bias/MAE of product time series.

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  @ravichaturvedi87265 ай бұрын

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

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  @anujhihbti16 ай бұрын

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

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  @nicolasvandeput-SupChains6 ай бұрын

  You can register here to be informed of future webinars: mailchi.mp/supchains.com/newsletter

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  @muhammadhammadmasood87286 ай бұрын

  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.

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  @lextor9917 күн бұрын

  use static rules based on competitor price

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  @Terracotta-warriors_Sea6 ай бұрын

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

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  @nicolasvandeput-SupChains6 ай бұрын

  I already have this for you if you're interested: nicolas-vandeput.medium.com/how-to-forecast-intermittent-products-c5d477b90176

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  @84pablomp7 ай бұрын

  Good summary! I used this to explain to my colleagues

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  @danielessiet40637 ай бұрын

  Please which certification would you recommend for a demand planner?

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  @user-pr3ck6yu1f8 ай бұрын

  I was waiting for it, Thanks

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  @DarkTobias78 ай бұрын

  Do you have the github python code available for these?

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  @nicolasvandeput-SupChains6 ай бұрын

  No, but I share them in books available here: www.amazon.com/stores/Nicolas-Vandeput/author/B07KL86HMV?ref=sr_ntt_srch_lnk_2

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  @motmot7849 ай бұрын

  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.

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  @nicolasvandeput-SupChains6 ай бұрын

  This might add value but will require a lot of data maintenance. I am not sure about the tradeoff.

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  @user-se5os5yx6s9 ай бұрын

  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.

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  @nicolasvandeput-SupChains9 ай бұрын

  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.

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  @joshuabradshaw164711 ай бұрын

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

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  @sercanyildirimtugcann9 ай бұрын

  any code or tutorial?

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  @nicolasvandeput-SupChains6 ай бұрын

  I share the code in my books: www.amazon.com/stores/Nicolas-Vandeput/author/B07KL86HMV?ref=sr_ntt_srch_lnk_2

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  @user-jz5vn4pi1j11 ай бұрын

  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 )

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  @nicolasvandeput-SupChains11 ай бұрын

  Je vais probablement publier une vidéo à l'occasion cette année à propos des KPIs.

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  @learntoswim51211 ай бұрын

  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!

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  @nicolasvandeput-SupChains6 ай бұрын

  I usually don't use equations to clean shortages. Nowadays, I just censor them. nicolas-vandeput.medium.com/forecasting-demand-despite-shortages-fee899120c08

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  @marcelonunes3831 Жыл бұрын

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

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  @nicolasvandeput-SupChains6 ай бұрын

  Thank you!

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  @nwabuezeprecious457 Жыл бұрын

  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.

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  @nicolasvandeput-SupChains6 ай бұрын

  Track historical orders (and even preorders) and censor periods with shortages: nicolas-vandeput.medium.com/forecasting-demand-despite-shortages-fee899120c08

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  @bobthedruid9378 Жыл бұрын

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

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  @SuperDoc3000 Жыл бұрын

  Great content !!! Thank you for sharing.

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  @danielhoraciocordovazambra8773 Жыл бұрын

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

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  @nicolasvandeput-SupChains Жыл бұрын

  I don't advise measuring COV. Best to track forecastability as we do here: www.skuscience.com

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  @danielhoraciocordovazambra8773 Жыл бұрын

  Great. Thanks And I can use this analysis for the procurement of supplies needed for production who have dependent demand?

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  @neelamyadav533 Жыл бұрын

  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.

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  @nicolasvandeput-SupChains Жыл бұрын

  With only 8 months, it'll be difficult. But I will make more content on this ;)

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  @prasvasu4217 Жыл бұрын

  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?

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  @nicolasvandeput-SupChains Жыл бұрын

  Hello Pras, You have two problems: - On which horizon should you focus your forecasting effort - On which horizon should you focus your planning effort For both, if you use models (anything automated), you could do as much as possible. But if you need human resources (to do the baseline or enrich a model), you'll have to focus on what's the most important. You only have limited time/resources.

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  @davidtiefenthaler7753 Жыл бұрын

  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.

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  @nicolasvandeput-SupChains Жыл бұрын

  Hello David, long story short: MAPE is never a good idea. MAE is fine for comparing different products.

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  @davidtiefenthaler7753 Жыл бұрын

  @@nicolasvandeput-SupChains how would you use MAE to compare different product on different scale? Since the MAE does reflect the different scale and is therefore hard to use for comparison or as an aggregated metric of multiple products.

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  @davidtiefenthaler7753 Жыл бұрын

  I just noticed your definition of MAE might be different to the standard one (en.wikipedia.org/wiki/Mean_absolute_error) since you represent it as a percentage value. Would be great if you can clarify this.

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  @nicolasvandeput-SupChains Жыл бұрын

  @@davidtiefenthaler7753 MAE scales perfectly if you have many products. %MAE doesn't scale across different product. it's all explained here: - www.manning.com/books/demand-forecasting-best-practices - towardsdatascience.com/forecast-kpi-rmse-mae-mape-bias-cdc5703d242d In general, no KPIs are perfect. Especially when looking at broad portfolio.

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  @Terracotta-warriors_Sea Жыл бұрын

  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.

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  @nicolasvandeput-SupChains Жыл бұрын

  Croston is not a good idea: towardsdatascience.com/croston-forecast-model-for-intermittent-demand-360287a17f5f

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  @nwabuezeprecious457 Жыл бұрын

  @@nicolasvandeput-SupChains yes I noticed it did poorly with my dataset. TSB and ADIDA did better.

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  @nicolasvandeput-SupChains Жыл бұрын

  @@nwabuezeprecious457 How do they compare to a moving average 12 months?

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  @nwabuezeprecious457 Жыл бұрын

  @@nicolasvandeput-SupChains TSB performed better. but this due to the volatility of my demand data. but if one has a relatively stable demand then a 12 months rolling forecast is suitable. But as you know @Nicolas whatever the technique for forecasting you intend to use will really depend on your case study. Thanks Nicolas for responding reading your book currently.

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  @ricardorodriguez2012 Жыл бұрын

  Great presentation.

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  @nicolasvandeput-SupChains6 ай бұрын

  Thank you!

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  @udaynataraaj2390 Жыл бұрын

  Excellent

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  @modyyassin3500 Жыл бұрын

  Hello Nicolas , Kindly i need your email to contact you

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  @Th3M0rri Жыл бұрын

  /watch?v=kkpFZA1sVSA&t=376s

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  @Terracotta-warriors_Sea Жыл бұрын

  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.

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  @nicolasvandeput-SupChains6 ай бұрын

  I've published an article on intermittent items here: nicolas-vandeput.medium.com/how-to-forecast-intermittent-products-c5d477b90176 You can see how I simulate policies in python here: towardsdatascience.com/make-your-inventory-simulation-in-python-9cb950da8cf3

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  @math3mantic Жыл бұрын

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

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  @nicolasvandeput-SupChains6 ай бұрын

  I share them in my books: www.amazon.com/stores/Nicolas-Vandeput/author/B07KL86HMV

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  @nassim59212 жыл бұрын

  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

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  @nicolasvandeput-SupChains6 ай бұрын

  I discuss results in various case studies. Here are some, Manufacturer with promotions: 20% forecast improvement nicolas-vandeput.medium.com/forecasting-case-study-ml-driven-forecasts-for-a-manufacturer-with-promotions-3a4dea8a9160 Chemical company: 20% forecast improvement nicolas-vandeput.medium.com/forecasting-case-study-with-a-chemical-company-35d02256667e Pharma distributor: 25% forecast improvement nicolas-vandeput.medium.com/an-end-to-end-supply-chain-optimization-case-study-part-1-demand-forecasting-2f071b81a490 Retailer with promotions and pricing: 30% forecast improvement nicolas-vandeput.medium.com/using-machine-learning-to-forecast-sales-for-a-retailer-with-prices-promotions-aab9b35d16a

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  @TheKuddlyk2 жыл бұрын

  What are the best models in your experience?

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  @nicolasvandeput-SupChains6 ай бұрын

  LightGBM and XGB!

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  @norabeckner98442 жыл бұрын

  😂 ᵖʳᵒᵐᵒˢᵐ

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  @joomack2 жыл бұрын

  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

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  @Terracotta-warriors_Sea2 жыл бұрын

  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.

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  @nicolasvandeput-SupChains2 жыл бұрын

  Hello Khurram, Thank you for your support! This is a very good question (that I get often). Unfortunately, there are no silver bullets against intermittent, lumpy, low demand. Instead of looking for a better forecasting model, you might have to spend time optimizing your inventory policies.