Many articles discuss how to measure forecastability for deterministic forecasting. However alot for in the trap of suggesting to simply use a coefficient of variation (CV) measure - even though it will count forecastable patterns as season and trend as variation and therefore mistakenly set them as hard to forecast.

The linked article here by Stefan de Kok does a good job of explaining the trap of pure CV and come with an alternative.

I'm a bit split though whether to use this type of measure or to compute the FAA of a simple benchmark (such as a moving average).
The FAA gives you a minimum acceptable accuracy level, but the proposed method here gives a measure which (typically) can be reported from 0 (unforecastable) to 1 (no noise).

Do any of you here have experience in implementing this and can share any experiences? Especially on the stakeholder/change management side.

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I have seen this so many times while working in FMCGs. Some people made that with multiple companies in sequence and landed CXO positions.

Steps for the new supply chain decision systems:

• Pick expensive consultants to devise a 100 pages RFP. Gather all requirements, especially the imaginary ones.
• Select 20 vendors, shortlist 2 ultra-expensive big names plus 1 cheap startup (they won't make it to the final round, but those guys are more fun to talk to)
• Pick the big name vendor that has the most features. An excess of 1000 screens is desirable.
• Plug the latest bleeding edge AI toolkit. The important part is the "bleeding" part, that's a sign of real innovation.
• Customize all UIs so that everything becomes collaborative. Numbers were bad before, but now, it costs a fortune to produce them.
• After 6 months, declare the initiative a success, and change job immediately afterward.

Simple, really.

1-1-1-1
Looks easy-peasy. Where did they found so many well educated people who can't add numbers?

Let's show that supply chain practitioners can add.
Post your result in comment like X-X-X-X where X is either 0 or 1, where 0 means incorrect answer and 1 - correct one. So 1-1-0-0 would mean that only first and second questions were correctly answered.

The article proposes three ways, namely:

Building supply chain resilience by managing risk
Using technology to increase supply chain agility
Identifying and promoting ways to be more sustainable

However, the analysis is a bit all over the place.

• For risk management, the example of RFID at Nike is given. However, RFID has nothing to with with risk management at the supply chain level.
• For supply chain agility, the example (which features a plug for a planning software vendor) of AI / ML, is a double-edged sword. Historically, software has been a great force to rigidify systems, lowering their operating costs, but usually making them less agile too.
• For sustainability, frankly, this is pure virtue-signal, both from the article itself, and from the respondent of the survey. I am not saying that sustainability isn't a worthy goal, however, very companies are in any position to do much on this front as far as their supply chain is concerned.

Afaik, those types of ships are typically referred to as bulk carriers

A bulk carrier or bulker is a merchant ship specially designed to transport unpackaged bulk cargo — such as grains, coal, ore, steel coils, and cement — in its cargo holds.

From https://en.wikipedia.org/wiki/Bulk_carrier

The interesting element is the extra option that COSCO gains by being able to leverage one extra type of ship. This method is probably inferior cost-wise to regular containers, but if a bulk carrier is the only ship that happens to be available, then, it becomes very valuable to have the option.

The post points out that competing a "demand" needs to factor-in the delivery date (requested) vs the shipped date (realized). However, I am afraid, this is a very thin contribution.

Demand is an incredibly multi-faceted topic. Demand is never observed. Only sales, or sales intents are observed. The sales are conditioned by many (many) factors that distort the perception of the demand.

First, let's start with the easy ones, the factors that simply censor your perception of the demand:

• No having the right product to sell. The sales never happen, yet, demand was there.
• Not having the right price. Idem, demand exists, just not for this price.
• Not having the right position (bad image, bad description). Visitors miss what they could have wanted.
• Not having the right delivery promise. Visitors give up if out-of-stock or if delivery date is too far away.

Then, we have all the big factors:

• Say's law: Supply creates its own demand, demand isn't prexisting, it's engineered as such.
• Branding: take two physically identically product plus/minus the brand, demand changes entirely.
• Cannibalizations and substitutions: demand covers a whole spectrum of willingness to buy. Demand cannot be understood at the product level.
• etc

Looking at the demand through the lenses of time-series analysis is short-sighted.

Ps: thanks a lot for being one of the first SCN contributors!

I think Lokad's supply chain lectures are an excellent source. They are all available on YouTube.
MIT offers a MicroMasters in Supply Chain Management through the edX platform. I haven't taken any of those courses, but maybe you can find something interesting in the syllabus. They are free and you can have the option of earning a certificate for a small fee if you complete all the requierements. This program can be half of the on-campus Supply Chain Management master's program.
Maybe someone that has taken this program can comment and tell us if it is worth it?

I had the chance to play it at university (it was very chaotic!). It was a session on "bullwhip effect".

In 2011 Lidl made the decision to replace its homegrown legacy system “Wawi” with a new solution based on “SAP for Retail, powered by HANA”. [..] Key figure analyzes and forecasts should be available in real time. In addition, Lidl hoped for more efficient processes and easier handling of master data for the more than 10,000 stores and over 140 logistics centers.
[..]
The problems arose when Lidl discovered that the SAP system based it's inventory on retail prices, where Lidl was used to do that based on purchase prices. Lidl refused to change both her mindset and processes and decided to customise the software. That was the beginning of the end.

Disclaimer: Lokad competes with SAP on the inventory optimization front.

My take is that the SAP tech suffered from two non-recoverable design issues.

First, HANA has excessive needs of computer resources, especially memory. This is usually the case with in-memory designs, but HANA seems to be one of the worst offenders (see [1]). This adds an enormous amount of mundane friction. At the scale of Lidl, this sort of friction becomes very unforgiving - every minor glitches turning into many-hours (sometime multi-days) fixes.

Second, when operating at the supply chain analytical layer, complete customization is a necessity. There is no such thing as "standard" decision taking algorithm to drive a replenishment system moving billions of Euros worth of good per year. This insights goes very much against most of design choices which have been made in SAP. Customization shouldn't be the enemy.

[1] https://www.brightworkresearch.com/how-hana-takes-30-to-40-times-the-memory-of-other-databases/

This spreadsheet contains a prioritized inventory replenishment logic based on a probabilistic demand forecast. It illustrates how SKUs compete for the same budget when it comes to the improvement of the service levels while keeping the amount of inventory under control. A lot of in-sheet explanations are provided so that the logic can be understood by practitioners.

another branch of discussion on LinkedIn
https://www.linkedin.com/feed/update/urn:li:activity:6966330168063725568/

Excel is the Swiss army's knife of any supply chain practitioner. While it is definitely not the most appropriate tool for managing supply chain it is important to be able to convey ideas through it. Lokad tried to build simplified educational version of decisions optimization with probabilistic forecasts in Excel. See the LinkedIn post by the link.
You can ask to receive the file in the comments either here or on LinkedIn under the post.

This is a nice readily accessible implementation, no sign-up, no login, create a new game and play. For those who are not familiar with the beer game, it's 4 stage supply chain game with 4 roles: manufacturer, distributor, supplier, retailer. Each player fills a role, and tries to keep the right amount of goods flowing around. It's a nice - and somewhat brutal - way to experience a fair dose of bullwhip. If you don't have 3 friends readily available, the computer will play the other 3 roles.

Ps: I never got the chance to experience this game at university. If some people did, I would love to hear about their experience - as students - of their first 'Beer game'.

Online version of the beer game, allowing multiplayer or computer controlled

2 years are a long way in ML.

These days CV models can handle trivial tasks like barcode recognition in near real-time even on commodity smartphones. Mostly thanks to the dedicated hardware like neural engines/tensor blocks etc.

Since vision is quite popular these days (cameras, AR/VR etc), things should progress even more quickly on the hardware front these days. E.g. building more affordable robotic assistants for the warehouse that are procured from cheaper parts but minor inefficiencies in the gear drives and motors are compensated by the software. This is similar to what Ocado Group has been aiming for when they acquired HaddingtonDynamics for their tech.

Also NVidia Omniverse, as a bet for creating digital twins for the reinforcement learning.

It looks like climate change effects on economics might be accelerated in the mid term. E.g. exceptionally dry summer in Europe alone wouldn't be that bad for the supply, but war in Ukraine and imbalance of supply chains that started 2020 - it all adds up.

Impossible to predict the future in this situation, but at least the actors could try to manage their risks across a range of future probabilities.

With all the opaque vocabulary you're dreaming of!
Sometimes I think of my professors always repeating "If you can explain it to your little siblings and answer all their questions then you understood it!" Who could do that with all the vocabulary used in the video?

The TSP is definitely a problem we encounter (almost) every day... With countless variants!

According to this paper https://www.researchgate.net/publication/337198743_A_comparative_analysis_of_the_travelling_salesman_problem_Exact_and_machine_learning_techniques, Google's tool seems pretty robust! I'd really like to give it a try when I need it.

For those wondering about this VizPick technology, there is a short video demo from two years ago at
https://www.reddit.com/r/walmart/comments/l0bn4m/for_those_wondering_about_vizpick/

The UX isn't perfectly smooth. You can feel the latency of the recognition software. Also, the operator has to move relatively slowly to give a chance to the acquired digital image to stabilize. However, it still beats the human perception by a sizeable margin.

Thanks and welcome! Don't hesitate to submit a link of your own, and/or to post a question. You will benefit from an extra level of care being on of the first non-Lokad contributors. :-)

I am looking forward to some good discussions here.

Unfortunately, S&OP processes - at the very least those I had the chance to observe in my career - had already devolved into tedious iterated sandbagging exercises, exclusively moving targets up or down without - ever - touching any actual decision. Yet, the insight remains correct, without putting the decision front and center, it devolves into idle speculations about the future.

That's a bit like the S&OP Process in general : it should be based on the important decision you need to take and those decisions only.

It is often the case in software that deciding on a solution based on a checklist of features leads to feature bloat. Picking a few core features, and wrapping them in a system that allows easy customization, is quite more effective.

Side story: this discussion board has been a long-time project of mine. For years I have been looking for a minimalistic discussion board, but all I could find was bloated pieces of software that did 10x what I wanted, and were missing the few things I cared about - like mathematical notations, (hey, have a look at my EOQ, $Q=\sqrt{\frac{2c_l\cdot k}{c_s}}$). More recently, the big platforms started doubling down on both fact checking - as if such a thing was possible when operating platforms that discuss everything and the rest - and monetization. Both being quite toxic to healthy open discussions. This renewed my sense of urgency for this project.

Yet, I believe that an online community does require neither a big platform to operate, not a heavy handed moderation policy. As long you don't have to deal with millions of users, a few careful design decisions can alleviate the bulk of the usual problems that plague online communities (spamming, trolling, brigading).

About 10 days ago, I started actually working on the Supply Chain News project. For the tech-inclined, I have implemented this webapp in C# / .NET 6 / ASP.NET with minimal amount of JS. The persistence is performed via Lokad.AzureEventStream, an open source project of Lokad. I might open source this very forum at some point, once the webapp is a little more battle tested.

A system is a whole that contains two or more parts, each of which can affect the properties or behavior of the whole [..]. The second requirement of the parts of a system is that none of them has an independent effect on the whole. How any part affects the whole depends on what other parts are doing. [..] A system is a whole that cannot be divided into independent parts.

Supply chain is a system, in the purest sense as defined by Russell Ackoff. Approaching supply chains through systems thinking is critical to not entirely miss the point. Most practitioners do it instinctively. However, most academic treatments of the supply chain entirely miss the point.

There are three different ways of treating a problem. One is absolution. That's the way we trust most problems. You ignore it and you hope it'll go away or solve itself. [..] Problem resolution is a way of treating a problem where you dip into the past and say what have we done in the past that suggest we can do in the present that would be good enough. [..] There is something better to do a problem than solving it and it's dissolving it [..] by redesigning the system that has it so that the problem no longer exits.

Dissolving problems is incredibly powerful. A decade ago, Lokad (mostly) dissolved the forecasting accuracy problem through probabilistic forecasting. Instead of struggling with inaccurate forecasts we embraced the irreducible uncertainty of the future, hence mostly dissolving the accuracy problem (not entirely, but to a large extend).

late 15c., "any small charge over freight cost, payable by owners of goods to the master of a ship for his care of the goods," also "financial loss incurred through damage to goods in transit," from French avarie "damage to ship," and Italian avaria.

Supply chain has been shaping the terminology of mathematics.

The M5 forecasting competition was notable on several fronts:

• It uses a sizeable real-world store-level sales dataset provided by Walmart. With 40k SKUs, it is - to date - the largest publicly assessible dataset for retail sales data.
• It features a probabilistic perspective letting the participants compete over a series quantile estimates. To date, it's the only forecasting competition featuring a non-average non-median scoring criterion.
• With 1,137 participants, it was very sizeable event. To date, I don't know any other forecasting competition that did even approach this level of participation.

The findings are not overly surprising: gradient boosted trees and deep learning models - which dominate the vast majority of the Kaggle competitions - end-up dominating the M5 as well.

Caveat emptor, those models are quite dramatically greedy in terms of computing resources. For a retail network of the scale of Walmart, I would not recommend those classes of machine learning models.

This video has ~47k at the time of the comment which is a shame, because this video is pure gold.

Brands have long known the power of the Made In Your Country sticker. It seems that the practice is over 4000 years old
https://en.wikipedia.org/wiki/Country_of_origin#History_of_country-of-origin_labelling

The Librem example is striking because they literally quantify the extra willingness to pay of their clients to benefit from the right country of origin.

I've seen several retailers completely give up on mustard. Rather than have an empty mustard section (with a paper explaining the situation), there is no mustard section anymore, the available space taken by enlarged mayonnaise and vinaigrette sections.

https://divan.dev/posts/animatedqr/

Someone invented an animated QR-code format. If you film the animation long enough, you can download an arbitrarily large file.

For another discussion about the bullwhip effect, check this discussion with Prof. Stephen Disney (University of Exeter):
https://tv.lokad.com/journal/2021/6/2/the-bullwhip-effect/

More generally, I am trying to consolidate a whole series of lectures about SCM that you can see at:
https://www.lokad.com/lectures

Shaun Snapp (principal at Brightwork Research) delivers an analysis that matches my own empirical observations about HANA, an analytics platform sold by SAP. The in-memory paradigm is expensive, pretty-much by design, due to both CAPEX and OPEX costs associated with the ownership of terabytes of DRAM, the class of devices that hold the memory in modern servers. Among the in-memory options for enterprise analytics, HANA appears to be one of the worst offenders of the market in terms of costs. Unfortunately, it does not appear to deliver features that can't be replicated in much cheaper systems. Nowadays, PostgreSQL, with a proper columnar setup, is a superior alternative in every dimension that I can think of.

Ryan Petersen is the CEO of Flexport, a startup that raised $2.2B. Flexport is a supply chain platform to track and manage orders.

Indeed, barcodes predate QR-codes, but QR-codes aren't necessarily superior. Information density comes as a tradeoff when it comes to the scanning apparatus and the need for ambient lighting. If you want to convey more information, then RFID is more appropriate than (hypothetic) higher dimensional barcodes. Alternatively, a QR-code is enough to encode a URL, and all the relevant information can be pulled from the internet instead of trying to cram the data into the label itself.

Close to three-quarters of supply-chain functions rely on the simplest method: spreadsheets. In addition, more than half use SAP Advanced Planning and Optimization (APO), a popular but antiquated supply-chain-planning application that SAP introduced in 1998 and will stop supporting in 2027. The portion of APO users in certain industries is even higher—75 to 80 percent of all the automotive, retail, and chemical companies we polled.

This 3/4 estimate for the supply chain functions that rely only on spreadsheets feels right. This is also matching my experience. Furthermore, even when some kind of planning tool is present, the tool almost invariably relies on the alerts & exceptions design antipattern which ensures a very low productivity for every employee that touch the piece of software.

However, I disagree with process suggested for the vendor selection. More specifically, the section that outlines the suggested process for the client company:

A list of business requirements.
Clear evaluation criteria.
Two or three “must have” use cases.

Companies invariably do an exceedingly poor job at any of those three tasks which are exceedingly technology dependent. This process guarantees a bureaucratic selection which favors whoever can tick the most boxes in the RFP document. Bloatware is the enemy of good software.

There is a much simpler, faster and more importantly accurate way to proceed through a vendor-on-vendor assessment:
https://tv.lokad.com/journal/2021/3/31/adversarial-market-research-for-enterprise-software/

Great invention! A barcode can be seen as grandfather of modern QR-codes. Its introduction initiated series of inventions where for the same basic idea inventors just added new dimensions. For instance, regular QR-code can bee seen as two dimensional counterpart of a barcode. But inventors didn't stop there. Somebody added third dimension via color coding. It is interesting where this trend will end and how many dimensions can be added to flat QR-code?

It is an object of the invention to provide automatic apparatus for classifying things according to photo-response to lines and/or colors which constitute classification instructions and which have been attached to, imprinted upon or caused to represent the things being classified.

Much of what makes the modern supply chain only become possible thanks to the widespread usage of the barcode technology. It's fascinating to see that the barcode predates mainframe computers which only started to get traction in the late 1950s.

The discussion on LinkedIn
https://www.linkedin.com/feed/update/urn:li:activity:6966315729650380800/

Paradoxically, data is the most under-valued and de-glamorised aspect of AI.

Lack of focus on the data plumbing spells the doom of most supply chain initiatives. While this article wasn't written with supply chain use-cases in mind, it's clearly relevant to the supply chain field. Data plumbing being "glamorous" means that it's difficult to gather resources for stuff that don't really have any kind of visible payback. Yet, data engineering is a highly capitalistic undertaking.

Ha ha. It's the pending $1 billion question that haven't cracked yet at Lokad.

Unfortunately, this article gives away very little about what Google may or may not do for supply chains. This article is written like a marketing piece. Nowadays, pretty much every single software company is using at least two cloud computing platforms to some extent. Thus, it's should be a surprise if a Software Company X that happens to do produce supply chain software runs on Cloud Platform Y. Yet, it this doesn't say why this cloud platform is better suited than its competitors.

The article puts forward Vertex AI, a recent ML offering of Google. However, as per the documentation of Google, it's state Pre-trained APIs for vision, video, natural language, and more which gives strong-vibes of being absolutely not the right quite of ML for supply chain. Furthermore, the AutoML (featured by Vertex AI) is also the sort of technology I would strongly recommend not using for supply chain purposes. It adds massive friction (compute costs, development times) for no clear upside in production.

Also, https://killedbygoogle.com/

Even with the Amazon.com behemoth dominating the e-commerce landscape, there’s room for smaller, scrappier rivals, Giovannelli says. In fact, many independent service providers are under contract to Amazon and other e-tailers to fill out the growing need for delivery vehicles.

In short, Amazon supply chain is a massive success, and just like the iPhone of Apple, it proves that there is a vast market waiting for alternatives. When considering large markets, even dominant players like Apple struggle to reach 25% market share. This is gives a lot of room for other actors. This is what those private equity investors are looking for.

Within the Lokad TV channel, this episode about DDMRP is probably the one, so far, that generated the most polarized feedback. On one hand, we got a lot of support. On the other hand, we got heavy criticized by the DDMRP evangelists. The most frustrating part of me is that the critics have been, so far, have only been shallow dismissals, or authority arguments.

A longer written discussion was produced at:
https://www.lokad.com/demand-driven-material-requirements-planning-ddmrp

Year's later, DDMRP community has still not addressed my top three objections:

• Incorrect baseline: half-backed MRPs can't be a baseline for DDMRP.
• Limited formalism: BOMs are literally applied graph theory, which DDRMP ignores.
• Real-world complexity dismissal: economics are absent from DDMRP.

It will be on September 14th at 15h00 (CET).

Watch the live at:
https://www.youtube.com/watch?v=3uqezVCMhSE

Most supply chain initiatives fail, and yet, the vast majority of supply chain case studies speak only of successes. Only the epic-scale failures become visible to outsiders. Even insiders frequently don't realize that most of the past initiatives of their own company have failed. Large companies aren't necessarily very capable when it comes to their institutional memory of past debacles.

The case of Target Canada was discussed in my supply chain lecture:
https://tv.lokad.com/journal/2021/2/3/supply-chain-personae/