My personal favorite remains https://www.ubisoft.com/en-gb/game/anno/1800.
The entire Anno series is outstanding to understand basic concepts of raw material - semi finished - finished product transition + lead times.
Would be keen to know whether there are other games that come to mind!

Just to clarify the terminology that I am using the following: the EOQ (economic order quantity) is a quantity decided by the client, the MOQ (minimal order quantity) is a quantity imposed by the supplier. Here, my understanding is that the question is oriented toward EOQs (my answer below); but I am wondering if it's not about picking the right MOQs to impose to clients (which is another problem entirely).

The "mainstream" methods approach EOQs, especially all of those that promise any kind of optimality suffer from a series of problems:

• ignore variations of the demand, which is expected to be stationary (no seasonality for example)
• ignore variations of the lead time, which expected to be constant
• apply only to "simple EOQ" that apply to a single P/N at a time (but not to a EOQ for the whole shipment)
• ignore macro-budgeting constraints, aka this PO competing against other POs (from other suppliers for example)
• ignore the ramification of the EOQs across dependent BOMs (client don't care about anything but the finished products)

Do not expect a formula for EOQs. There isn't one. A satisfying answer requires a way "to factor in" all those elements. What we have found at Lokad for better EOQs in manufacturing (not "optimal" ones, I am not even sure we can reason about optimality), is that a certain list of ingredients are needed:

• probabilistic forecasts that provide probability distributions at least for the future demand and the future lead times. Indeed, classic forecasts deal very poorly with irregular flows (both demand and supply), and MOQs, by design, magnifies the erraticity of the flow.
• stochastic optimization, that is the capacity to optimize in presence of randomness. Indeed, the EOQ is a cost-minimization of some kind (hence an optimization problem), but this optimization happens under uncertain demand and uncertain lead time (hence the stochastic flavor).
• financial perspective, aka we don't optimize percentages of errors, but dollars of error. Indeed, EOQs is typically a tradeoff between more stock and more overhead (shipment, paperwork, manhandling, etc)

In my series of supply chain lectures, I will be treating (probably somewhere next year) the fine print of MOQs and EOQs in my chapter 6. For now, the lecture 6.1 provides a first intro into the main ingredients needed for economic order optimization, but without delving (yet) into the non-linearities:
https://tv.lokad.com/journal/2022/5/12/retail-stock-allocation-with-probabilistic-forecasts/

It will come. Stay tuned!

An incredibly perceptive discussion that reflects my own experience with data science in general.

A small side software project dedicated to inventory counting.

The earliest occurrence I could find of the concept is 2016 with the presentation:
https://www.slideshare.net/BramDesmet/supply-chain-innovations-2016-strategic-target-setting-in-the-supply-chain-triangle

The 2018's book is available at:
https://www.amazon.com/gp/product/B07CL2MCWS/

ATP (used in the article) stands for Available-To-Promise.

I am very much in agreement concerning the list of stuff that didn't work: Consolidations Decimated Value, Consultants Failed to Deliver Value Through Software Models, Barney Partnerships Bled Purple, not Green, The Saga of Venture Capitalists and Private Equity Firms, New Forms of Software Marketing Creates Haze not Value

Concerning the value of cloud and NoSQL. Well, yes, but it's a bit of an old news. Lokad migrated toward cloud computing and NoSQL back in 2010. A lot did happen since then. For a discussion about what a modern cloud-based tech look like https://blog.lokad.com/journal/2021/11/15/envision-vm-environment-and-general-architecture/

Excel is ingrained in day-to-day work of current working generation. It will be difficult to replace (barring select-few companies).

I strongly feel that post-generation-Z (born post 2001) workforce will expect (and work towards developing) better decision engines - powered by better technologies. This generation is using some form of AI/ML in day-to-day life, in schools, colleges. They wouldn't expect anything less in work. This will get progressively better.

A couple of relevant links:

• The action reward is a mini-framework that has been designed by Lokad to implement such prioritized ordering strategy. See https://docs.lokad.com/reference/abc/actionrwd.reward/
• The longer version of the topic through my lecture 6.1 at https://tv.lokad.com/journal/2022/5/12/retail-stock-allocation-with-probabilistic-forecasts/
• A discussion Why Safety Stock is Unsafe at https://tv.lokad.com/journal/2019/1/9/why-safety-stock-is-unsafe/ as a critic to the traditional perspective.

Parkinson's law is the adage that "work expands so as to fill the time available for its completion."

In most of Western Europe, my (tough) take is that, career-wise, those certifications are worth the paper they are printed on. The vast majority of the supply chain executives that I know have no certification.

More specifically, the example exam questions are ludicrous, see
https://www.theorsociety.com/media/1712/cap_handbook_14122017133427.pdf

MCQ (Multiple Choice Questions) is the exact opposite of the sort of problems faced by supply chain practitioners. MCQs emphasize super-shallow understanding of vast array of the keywords. Worse, it treats those keywords (eg data mining, integer programming) as if they were encompassing any kind of cohesive body of work (or tech). This is wrong, plain wrong.

Minor: Edited the title to avoid the question mark. I am trying to reserve the question titles to actual questions addressed to the community.

Also, discussed at https://news.lokad.com/posts/349/ (1 comment)

This is great! We need to hear more stories about “the day of small beginnings” as I like to call it. Too often we read marketing buzz and press releases on the glory of a great achievement. That’s great and all, but every human achievement starts with people and there are usually some interesting dynamics around how something goes from zero to something. We need more stories around the underlying climate preceding the best innovations. THAT will inspire and spark excitement to build. There are plenty of lessons learned that can be shared in that regard (Paul Graham and Y Combinator essays do this). Who else has an early day supply chain story to share?

“In 2011, Amazon’s total revenue reached nearly $48 billion, and it was already clear to the senior leadership that the company’s scale would require the automation of buying and the management of inventory; monitoring spreadsheets was not a long-term solution. Indeed, even then the sheer range of products offered by Amazon meant the “illusion of control” was already kicking in among the groups managing inventory, says Bhatia. In fact, Bhatia notes, the sheer complexity and scale meant the challenge was beyond the scope of any team, let alone an individual.”

Archive https://archive.ph/2022.10.25-042811/https://www.bloomberg.com/news/features/2022-10-24/gopuff-layoffs-signal-instant-delivery-s-demise

Light retrospective on the evolution of Amazon automated decision-making tech for supply chain. Interesting nugget, Amazon appears to be still using their multi-horizon quantile recurrent forecaster (1) as it appears to have taken several years to cover the full scope (which is not unreasonable considering the scale of Amazon).

(1) A multi-horizon quantile recurrent forecaster
By Ruofeng Wen, Kari Torkkola, Balakrishnan (Murali) Narayanaswamy, Dhruv Madeka, 2017
https://www.amazon.science/publications/a-multi-horizon-quantile-recurrent-forecaster

The book can be purchased from
https://www.amazon.com/Profit-Source-Transforming-Business-Suppliers-ebook/dp/B099KQ126Z

The main message by Schuh et al. is that a collaborative relationship with suppliers can be vastly more profitable that an oppressive one solely focused on lowering the supply prices. While the idea isn't novel, most companies still favor aggressive and antagonistic procurement strategies which leave no room for more profitable collaborations to emerge.

I recommend Jason Miller (MSU SCM head) and Warren Powell’s posts from LinkedIn. Both are widely recognized thought leaders in the quantitative SCM space.

10 years ago, Amazon was acquiring Kiva Systems for $775 million.

The quote is from The Testament of a Furniture Dealer by Ingvar Kamprad, IKEA founder. The original document can be found at:
https://www.inter.ikea.com/en/-/media/InterIKEA/IGI/Financial%20Reports/English_The_testament_of_a_dealer_2018.pdf

Forecasting and S&OP initiatives almost invariably turn into bureaucratic monsters.

The visual maps can be found at https://supplychaininsightsglobalsummit.com/event-2022/

A team from Lokad took part in the M5 competition. The method, which landed No1 at the SKU level, has been presented at https://tv.lokad.com/journal/2022/1/5/no1-at-the-sku-level-in-the-m5-forecasting-competition/

The average project completion time is greater than what is predicted by the average durations of the underlying tasks, because the project is not done until the last task is done.

The average profit is less than the profit of the average demand, because there is no upside if demand exceeds quantity ordered.

The average operating cost is greater than the operating cost of the average demand.

Yes, continuous improvement is the key to make a new technology become economically efficient, but also it may become enabler as you've mentioned. Though, by breakthrough we usually mean a change of principle how the same objective is achieved better/faster/cheaper etc. This is qualitative change in the first place. Then continuous improvement starts.

As written in the article, you have to choose between two models when developing a startup : either grow at all costs, and optimize later (like Uber), or keep the notion of cost and frugality as an essential part of every initiative. Amazon is doing that in its warehouses : Grow, but make sure to optimize for efficiency.

On the other hand, groundbreaking innovations are often made possible by incremental innovations in other areas (such as the improved precision of steel machining tools allowing the creation of cost-efficient gasoline engines).

A video explaining why this forum was launched.

While Reddit targets a young category of users and Wikipedia is bound to contain old and somewhat deprecated information, LinkedIn remains one of the only trustworthy source of information in the field, due to the experience of the professionals that are sharing their experiences. However, this information does not remain easily accessible after a while. Therefore, it would be interesting to have a place to discuss the specifics of the Supply Chain field.

For this purpose, Lokad has launched a new page called https://news.lokad.com/, an aggregator that consolidates interesting links to various Supply Chain topics and where the community can discuss and submit their own subjects. The final goal is to share links of interest for Supply Chain-minded people, giving them the chance to debate and contribute to further achievements in the field.

Non paywalled https://archive.ph/wIGLB

Interesting nuggets of this interview with Ryan Petersen, CEO of Flexport:

- 20% of the Flexport workforce is software engineering. The rest is sales and account management.
- The P95 transit time is a 95% quantile estimate of the transit time; part of the core Flexport promise.

Overall, a very interesting discussion, although the simplify part really refers to the Flexport product itself.

Most supply chain initiatives fail. Dead-ends are a given, although my understand differs a little bit concerning the root causes. Among the top offenders, the lack of decision-centric methodologies and technologies ranks very high. In the 'future' section proposed by the author, I see layers of processes to generate ever more numerical artifacts (eg: Market-driven Demand, Demand Visibility, Baseline Demand and Ongoing Analysis of Market Potential, Unified Data Model Tied to a Balanced Scorecard, Procurement/Buyers Workbench).

A decade ago, Made.com - along with a couple of similar ecommerces - took extensive advantage of the payment terms of their oversea suppliers (mostly in Asia). Their supply chain execution allowed them to sell their goods while goods were still in-transit. This worked well for furniture as customers - at the time - were OK waiting a month or two to receive their order. I don't know where they stand now, but I suspect that the supply chain tensions (sourcing problems in Asia + surge of transport fees) do pose significant challenges to this business model.

If we look at other fields we can realize safety stock will never get buried completely.
We have modern medicine, but healers still do exist.
We have modern agriculture, but growing 'organic' movement (which is in fact less sustainable).
Etc, etc, etc.
Those advocating for "supply chain is not for tech, but for people" will always seek for dysfunctional but simplistic recipes.

My previous take on safety stocks:
https://tv.lokad.com/journal/2019/1/9/why-safety-stock-is-unsafe/

In short, not only the normal distribution assumption is bad, but the whole approach is very naïve. It made (somewhat) sense before the advent of computers, but at present, safety stocks should be treated as a method of historical interest only.

This work is done by Paul Peseux who is currently doing a PhD at Lokad. In terms of research, it's a convergence between machine learning, mathematical optimization and compiler design; fields that are usually considered as fairly distinct - but that end up being glued together in the context of differentiable programming.

Inflation pressure in Europe may not only be fueled by increased energy prices but in addition by increased logistics costs.

Fully agree. The only case which might be relevant for "real time" data is dynamic repricing for SKUs pending stock out or promotion/discounting within the day for perishable goods. This might be relevant for e-groceries and other e-commerce businesses with tight competition where they closely tracking each other and have overlapping highly substitutable assortment.
For replenishment and purchasing "real time" data is overkill and waste of compute power. Daily batches are enough.

Demand sensing = marketing tosh; nothing new or clever . . . I can't see evidence to the contrary.

Not to say, of course, that there is no benefit in getting an earlier feed of data on something such as demand, if - and only if - it can result in a better decision that is executed earlier than otherwise - giving an economic benefit.

The use cases do exist though. My (real) example is in the supermarkets world, specifically on the day that a new set of weekly promotions start. On this 'day one' of the promotional week regular and promotional pricing are reset, promotional 'aisle ends' are built as well as other changes in layout being implemented. For all the forecasting that is done beforehand, the actual sales by 11:00 on the first day provide a very accurate view of how all the changes - overlayed with each other - are being interpreted by the customer, resulting in sales. This early read on sales - which is net of all the inter-SKU relationships - 'could' result in a different POs being placed with suppliers a day earlier than if the daily sales were interpreted in an overnight batch process. This 'could' result in availability and sales better in line with demand, and fewer overestimates/overstocks in store which are also negative in terms store productivity. The challenge in this example is not so much 'the software' but rather the data flow and the timing of the execution of the calculation (e.g. purchase order calculation), in line with other time windows that need to be hit in order to get a PO out a day earlier. In this sense there is no difference between 'real time' and 'timely' - e.g. a batch process is perfectly OK as long as it can be executed quickly and when you need it.

Probabilistic forecasting should be considered as state of the art for supply chain planning.
Though, as we recently have shown it is doable to explain supply chain decisions optimization with use of probabilistic forecasts (we explained it using purchasing decisions) even in Excel:
https://www.linkedin.com/posts/alexey--tikhonov_supplychainmanagement-probabilisticforecasting-activity-6962747294081298432-T-OH?utm_source=share&utm_medium=member_desktop
If there is a substance behind Demand sensing - looking forward to see the Excel with simplified approach showing what value this approach adds.

impossible to get a real sense of what's offered without some sort of PoC

Fully agree. This is why I abhor those made-up terminologies: it's pure vendor shenanigan's.

The 'real-time' part is an overstatement for sure. What they offer are batch job controlled short term (deterministic) forecasting.
All these buzzwords in planning/forecasting software is just annoying as a customer - its absolutely impossible to get a real sense of what's offered without some sort of PoC. Because only then you start to have the talks that shows, what's under the hood.

Demand sensing article on Wikipedia:
https://en.wikipedia.org/wiki/Demand_sensing

Compare it with probabilistic forecasting for instance:
https://en.wikipedia.org/wiki/Probabilistic_forecasting

Q: Why a new buzzword if it's about repackaging techniques that already have proper names?
A: Occam Razor: to make the tech appear more attractive and valuable than it really is.

According to [1], SAP recognizes 'demand sensing' as 'a forecasting method that leverages [...] near real-time information to create an accurate forecast of demand'.

• Why would a 'near real time' provides a forecast that is any less accurate than a batch forecast happening with a lag of, say, 10min?
• Why should gradient boosting be even considered as a relevant technical solution for 'near real-time' tasks?

Remove demand sensing from the picture, and you still have the exact same tech with the exact same processes.

[1] https://blogs.sap.com/2020/02/09/sap-integrated-business-planning-demand-sensing-functionality/

It would be cool to have a Lokad version of the beer game. I.e. focusing on the elements of the stock reward function https://docs.lokad.com/library/stock-reward-function/

When I look at the market, I see major contributions of GroupThink:
- Failure of IT Standardization. SAP and IBM failed the market. The recent gains in market share of Kinaxis, o9, and OMP are largely due to the failure of SAP to drive thought leadership in planning.
- Private Equity M&A. Software mergers & acquisitions also slowed innovation. The technology roll-ups of INFOR, JDA (now BlueYonder), and E2open improved investors’ balance sheets, but did not drive value for their clients.
- Event Companies Are the Nemesis of the Industry. Event companies take large sums of money from technology companies and host events based on the Rolodex of a prior supply chain leader

A spot-on analysis. Low level IT standardization is moving forward nicely (think federated identity management), but it's not the case for high level IT (think workflows). The success of products like Tableau reflects that there is a major need to cope with the lack of standardization.

M&A in enterprise software is almost always resulting in large about of technological debt. It's very hard to get good software engineers motivated about clean-up millions of lines of code of haphazard codebases where stuff has just been "thrown together".

Event Companies are a severe form of epistemic corruption. I discussed the case in https://tv.lokad.com/journal/2021/3/31/adversarial-market-research-for-enterprise-software/

As it was suggested in https://news.lokad.com/posts/167/live-virtual-tours-of-amazon-fulfilment-centres, there is also the visit of Amazon warehouses in different countries

I tried visiting their American facilities, it was great!

Full text article https://archive.ph/wlCqS

Interesting, would be cool to have a collection of warehouse visits to give new professionals a better idea of what an actual warehouse looks like. I came across this one from Maersk https://www.youtube.com/watch?v=Qhcheew5kxU&ab_channel=Maersk

The sheer scale of this piece of engineering is incredible. We are talking of a machine that is 400m x 100m x 12m big. The whole thing maximizes that can be done in terms of inventory storage and inventory throughput while taking advantage of every cubic meter that is available.

Digital talent remains the Achille heel of many (most?) supply chain initiatives. Nonsensical tech decisions keep being made due to a lack of understanding of what is at stake. The challenges pointed out in this post a few years back have only become more acute since then.

Q5: There is a comment in the file recommending using this approach for educational purposes only, because such a forecast will be simplistic and will not account for seasonality or trends etc. How to use this approach beyond educational purposes for real inventory optimization problems?

A5: First, it should be noted that probabilistic forecasts in the file are synthetic probability distributions that are built using Excel functions.
It is possible to build probabilistic forecast that would be based on historical sales data and would take into account all systematic causes of variation like seasonality, trend etc.
We intentionally left it behind the scenes for two reasons:
1) We would have to provide historical sales data that probabilistic forecast would be based on. Here we have a problem that all users are from different industries and got accustomed to see different sales patters. We wanted to avoid "this doesn't look like my sales data" kind of impression.
2) Building production grade probabilistic forecast in Excel is technically speaking a nightmare and an open problem since we've never seen anybody done it so far. Taking into account Excel capability limitations it should be also very slow to rebuild the forecast once you update the data.
At Lokad we use domain specific programming language called Envision where probabilistic forecast can literally be build with several lines of code. Those who are interested about the procedure can play the recording of our past supply chain lecture:
https://tv.lokad.com/journal/2022/3/2/probabilistic-forecasting-for-supply-chain/

Q4: Can this approach of decisions prioritization with expected ROI be used for allocating scarce products from DC to stores?

A4: Absolutely.
This file was about purchasing optimization, but the same approach could be applied for DC to stores dispatch problem. ROI ranking perfectly works when there is limited stock at DC and inventory manager wants to make sure that dispatch plan is optimal.
The way it works is the first unit of the first SKU (can be arbitrarily chosen, but can also start from top sellers) and ask in which store (given current stock in each store and probabilistic demand forecast for this SKU for each store) it will have the highest ROI and/or fill rate gain? Then you rinse and repeat for all SKUs at a unit level for all the units (same as with purchasing). This enables to best allocate the existent stock on hand across all the stores in the network. Ranking procedure here is a bit different because at every step we make final decision to which store we send Nth unit of a given SKU. At the end of the process after considering all micro level decisions we chose termination criteria (no more stock at DC or target ROI or target fill rate etc.) and aggregate numbers per store per SKU to get dispatch lists.

Q3: What goes into engineering economic drivers? How critical it is to get them right from the very beginning or is it more like learning curve?

A3: Decision making using economic drivers in combination with probabilistic forecasts to some extent imitates human level decision making process. Costs and revenues are straightforward, but the stock out cover is not. It is completely context dependent and can be called heuristics. This is where human intelligence lies. Having them properly set enables to automate decision making so that there is no more need to say split 500k SKUs among 50 demand planners to manage 10k SKUs each. All decisions for all SKUs can be generated automatically by the single numerical recipe. Litmus test here is simple - if demand planner sees no insanity in decision recommendations, then it should be concluded that economic drivers are production grade, but for sure can be fine tuned over time.

Interestingly enough the EU exports approximately 7 million metric tons per year https://legacy.trade.gov/steel/countries/pdfs/exports-eu.pdf - which is about 4% of the production - thus the demand/supply gap in Europe shouldn't be too significant in the short term.

EU steel plants suffer from weak demand and high energy costs, decreasing competitiveness of EU steel producers. Shut downs are across all EU countries. However, in comparison to 2022 the steel production is only reduced by 6.7%

The stochastic gradient descent (SGD) is used for a whole variety of supply chain problems from demand forecasting to pricing optimization. From a software performance perspective, the crux of the SGD problem is to increase the wall-clock rate of descent while preserving the determinism of the execution. Indeed, as far as parallelization is concerned, indeterminism is the default; it takes effort to achieve a reproducible flavor of the algorithm. The report introduces a technique that delivers a 5x speed-up at a 6x increase of compute costs.

10 years is a good ballpark assessment to produce a good software product - assuming there are people who will stick around for a decade to see it through. See https://www.joelonsoftware.com/2001/07/21/good-software-takes-ten-years-get-used-to-it/ Written 20 years ago, but the points are still largely valid.

Acknowledgement from that the probabilistic approach is superior, or at least will offer more resilient decisions. How long to get to market in a core product that actually works? 10 years?

Conformal predictions are one of the flavor of probabilistic forecasting, leaning toward high dimensional situations. This repository is an extensive compilation of the papers, phds and open source toolkits that are available for conformal predictions.

The blog post talks about range forecasts and probabilistic forecasts. Also they talk about relatively small number of scenarios ("at least 5-10...") so it brings the question of accuracy with such small resolution.
Feasible decisions ranking and economic drivers are absent while they are essential components for choosing decisions with highest return on investment.
Level of details is underwhelming so far.
Though this is great news. They will definitely accelerate the adoption of the term.

We strongly believe that probabilistic methods play a key role in the future of supply chain planning. They represent the right way to go for areas that contain uncertainty, including for all types of forecasting and the subsequent planning situations.
Traditional deterministic planning methods base their decisions on the mistaken assumption that uncertain values can be approximated by a single average number. As a direct consequence of this assumption, plans are often infeasible at the time they are created and manual interventions are continuously needed.

Better late than never! However, let's immediately point out that the SAP IBP architecture is very much hostile to probabilistic modeling. More specifically, the high memory consumption of HANA is going to add some massive overhead on top of methods that not exactly lightweight in the first place.

Another metric of this kind would be stock levels expressed in dollar value or in days of sales. Very easy to cut and show "benefits", especially if you are in long lead time business. Cut them, update your CV and make diagonal jump to another business. Profit :)

Service levels are probably the favorite proxy variable in supply chain. Supply chain textbooks and consultants assume that "finely tuned service levels" automatically translate into better outcomes for the company, while those service levels say very little of substance about the quality of service actually perceived by customers.

It's tempting to solve an easier problem only superficially related to the actual problem.

One of the ways bureaucracies are ignoring difficult problems is by division of labor. Once the scope of the problem is broken up across multiple roles it becomes nobody's business. Calling cross-functional teams to rescue the business is not a solution as usually those teams are composed of multiple within-silo thinkers who can't abstract from their role and look at the problem as a whole.

I had the chance to do a guided tour yesterday evening, for their American facilities. It was great! I was surprised to be (almost) the only one asking questions. They took the time to answer all of them, not very extensively I acknowledge but the effort was there. I would love to visit their French warehouses now.

Supply chains are complex systems. It's maddeningly difficult to solve problems rather than displacing them. When confronted with incredibly difficult problems, bureaucracies are also incredibly good at ignoring them altogether. In supply chain, big problems are usually big enough to take care of themselves.

How far should we go to say that we have reached a point of say realistic representation of an agent? Also when we say accuracy , what does it really mean?

Right now, as far as my understanding of the supply chain literature goes, there is just nothing yet published to tell you whether a simulation - in the general case - is accurate or not. The tool we have for time-series forecast don't generalize properly to higher dimensional settings.

For example, if a simulator of a multi-echelon supply chain of interest is implemented, and then someone decide to refine of the model of some inner agent within the simulator, there is no metric that are even known to be able to tell you if this refinement is making the simulator more accurate of not.

Stay tuned, I am planning a lecture on the subject in the future, it's a big tough question.

Thanks Joannes, would it make sense to have more intelligent agent behavior? How far should we go to say that we have reached a point of say realistic representation of an agent? Also when we say accuracy , what does it really mean? Is it like ,say , we predicted something up or down and we are correct 90 times out of 100 so our accuracy is 90%?

Holy, surprisingly its popular in south korea which was unexpected for me.

Interesting to see that that the major congestions that were deemed to create backlog for all 2022 have mostly vanished

Lokad tries to push a lot of (hopefully) quality supply chain materials in the open. Unlike many vendors, we don't attempt to shroud our technology in a veil of mystery. However, we need backup. If you think that you help us produce videos, guides, articles ... then, drop your resume at j.vermorel@lokad.com

Q2: After we made and executed purchasing decision there is no more uncertainty, then how would we evaluate whether the predicted probability forecast is the correct one?

A2: Even after we made and executed the decision in reality there is still uncertainty. We don't know when our order will be received because lead time is also probabilistic. Quality of goods can be probabilistic as well (consider fresh food for instance). Also we will not be able to estimate accuracy of our probabilistic demand forecast until the responsibility window (over which we built probabilistic forecast) becomes the past.
With respect to the metrics that are used for probabilistic forecast quality assessment you can check this lecture:

https://tv.lokad.com/journal/2022/3/2/probabilistic-forecasting-for-supply-chain/

Use time stamps to watch the lecture parts where the metrics were discussed.

Answering a question on YouTube:

As per my understanding the following are the core concerns -
1) Accuracy
2) Doesn't necessarily represent reality based on Agent behaviour
3) Gives me insights, ok now what should i do? Don't give me numbers, tell me what to do. If an employee sits down and tweaks parameters then how do i make sense if the decision is correct?

Yes, in short, the two big gotchas are (a) your digital twin may no reflect the reality (b) your digital twin may not be prescriptive.

Concerning (a), measuring accuracy when considering the modeling of a system turns out to be a difficult problem. I intend to revisit the case in my series of supply chain lectures, but it's nontrivial, and so far all the vendors seem to be sweeping the dust under the rug.

Concerning (b), if all the digital twin delivers are metrics, then, it's just an elaborate way to waste employee's time, and thus money. Merely presenting metrics to employees is suspicious if there is no immediate call-to-call. If there is a call-to-action, then let's take it further and automate the whole thing.

This is brief summary of my Q&A with one of the users of Excel file. I will provide each question and answer as separate comment.

Q1: How are we creating the probability distributions for the products in this file? If I have historical sales data for a particular SKU, how should I get the probability distribution for that?

A1: We haven't made capabilities to build probabilistic demand forecasts using historical data in this Excel file. This is very hard to implement in Excel due to its limitations. Programming language like Envision is more appropriate for that, but one could also use Python or any other language. How to build Probabilistic forecast with historical data was discussed in this lecture:

https://tv.lokad.com/journal/2022/3/2/probabilistic-forecasting-for-supply-chain/

This file uses synthetic distributions via built-in Excel functions for normal and negative binomial distributions. Changing parameters you can change the distributions and the ranking of micro level decisions respectively. This is educational tool and the primary goal was to show how having probabilistic demand forecast and economic drivers demand planer can optimize purchasing decisions.
Though, the question of building probabilistic forecast based on historical data still remains valid. It is just not in the scope of this document because it is harder to understand, but also harder to explain and show through Excel.

Indeed. Although, as aircrafts get dismantled, it tends to introduce a lot of spare parts into the market. Thus, most of the time, the parts of weaning aircraft types become cheaper despite the lack of production of parts. However, as you correctly point out, there are parts that become rare and very expensive, making the aircraft type economically unviable.

Making noise and making money out of the noise, especially consistently, is not the same. I think that growing number of companies that adopt probabilistic perspective through appropriate software is the only adequate metric that tells anything about the adoption of the term.

That's is interesting. At some point there will be very few aircrafts of this model so servicing parts for them will become nightmarish as demand will be even more sparse than it is now meaning the parts rotation will slow down and carrying cost at some point will make it unprofitable for those remaining aircrafts to fly.

A nice illustration the sort of stuff that characterizes aviation supply chain: aircrafts are both expensive and modular. Thus, the option is always on the table to take a component from an aircraft and move it to another aircraft. Most of time, exercising this option is pointless, but sometimes, it's an economically viable move. Here, this is what Boeing is doing with aircraft engines. Aviation supply chains are not about picking safety stocks :-)

Working in the probabilistic space, it feels that the term is becoming more and more mainstream and anecdotal evidence confirms that. However, looking at google searches this is not at all confirmed. Maybe the topic isn't as mainstream as we feel - confirmation bias? https://www.lokad.com/probabilistic-forecasting

Funfact: Since 2012 the interest for the topic has x 100

https://trends.google.com/trends/explore?date=all&q=%2Fg%2F11b90fjhmq

Fun fact: Lokad started to implement digital twins of supply chains more than a decade ago; although I don't overly like this terminology. As a rule of thumb, I tend to dislike terminologies that try to make tech sounds cool, irrespectively of the merit of said technology. There are tons of challenges associated with large scale modeling of supply chain, the first one being: how accurate is my digital twin? Tech vendors are usually exceedingly quite about this essential question.

747 have been produced for 54 years. The one most notable evolution being the introduction of the fly-by-wire tech in the 1990s
https://www.flightglobal.com/boeing-747-x-flies-by-wire/6314.article

This plane has massively contributed to the democratization of both air travel and air shipments. Considering that aircrafts are typically operated for decades, some 747 are likely to keep flying for the next 20/30 years.

Lion Hirth is Professor of Energy Policy at the Hertie School. His research interests lie in the economics of wind and solar power, energy policy instruments and electricity market design.

The document introduce marginal pricing - in the context of energy, and make three statements about it:

Marginal pricing is not unique to power markets.
Marginal pricing is not an artificial rule.
If you want to get rid of marginal pricing, you must force people to change their behavior

Three points are very much aligned with what is generally understood as mainstream economics. Those points are quite general and do apply to most supply chains as well.

I am not familiar with the specific Greek energy market.

However from a supply-and-demand perspective,

• Intermittent energy sources do not meet the Quality of Service requirement which is essential for energy.
• As long as the source can be turned into electricity, all energy sources are near perfect substitutes. Hence, one cannot isolate the price of a selection of sources vs the rest.
• When demand is inelastic and grows, as it is the case for energy demand, it's the supply that has to grow.

Due to spiking electricity prices several stakeholders are arguing that the electricity market is malfunctioning and the pricing mechanism is flawed. The merit order model, that attributes the marginal (highest production price) to all producers is nothing else than the offer/demand model that we apply in all other markets as well.

The Supply Chain Scientist delivers human intelligence magnified through machine intelligence . The smart automation of the supply chain decisions is the end product of the work done by the Supply Chain Scientist.

Excerpt from 'The Supply Chain Scientist' at
https://www.lokad.com/the-supply-chain-scientist

Transit costs to low orbit are still beyond the realm of supply chain, however, it is notable the cost per kilogram has been going down by a factor 1000 over the course of 70 years. If progress keeps happening at the same pace, in a few decades, launches will become an option. The benefits of easier access to low orbit are somewhat unclear beyond telecommunications, but specialized micro-gravity factories has been explored many time in science fiction. At this point, orbit remains too expensive to even try to investigate newer / better industrial processes in orbit.

The only way to assess "forecastability" of a time-series is to use a forecasting model as a baseline. This is exactly what is done in the article, but unfortunately, it means that if the baseline model is poor, the "forecastability" assessment is going to be poor as well. There is no work-around that.

Stepping back, one of the things that I have learned more than a decade ago at Lokad is that all the forecasting metrics are moot unless they are connected to euros or dollars attached to tangible supply chain decisions. This is true for deterministic and probabilistic forecasts alike, although, the problem becomes more apparent when probabilistic forecasts are used.

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.

Be our guest, virtually! These live, one-hour tours take you behind the scenes at our fulfilment centres, using a combination of live streaming, videos, 360° footage, and real-time Q&A to replicate the experience of our in-person tours.
Live virtual tours are approximately 1 hour long, including Q&A.
Registration closes 6 hours in advance of each tour. Last-minute registration ("instant join") is not possible. Tours will no longer appear in the calendar once registration is closed, or when they are fully booked.

Various options are available depending on the region of interest:

• North America https://amazontours.com/na
• France https://amazontours.com/fr
• Australia https://amazontours.com/au
• etc

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.