Under the supervision of Prof. Dr. Stefan Minner, Leander Zimmermann and Patrick Menzel are writing a thesis at the Technical University of Munich. The goal of this study is to compare inventory optimization software. Lokad did receive their questionnaire, and with the permission of the authors, we are publishing here both their questions and our answers.
1. When did you introduce your optimization software to the market?
Lokad was launched in 2008, but as a pure demand forecasting solution at the time. We started to do end-to-end supply chain optimization in 2012.
2. For which company sizes is your software suitable?
We have clients ranging from 1-man companies to companies over 100,000 employees. However, below 500k€ worth of inventory, the statistical optimization of the supply chain is frequently not worth the effort.
3. For a midsized company of around 50-250 employees and for sales of around 10-25 million euros per year. What would be the price of your standard software package?
This would be our Premier package at $2500 / month. However, the package covers a lot more than just software. Pure software is only 1/5th of our fees or so.
The bulk of the fee goes into paying a data scientist at Lokad who manage the account, leveraging our technology stack to get the final results. That’s what we call an inventory optimization as a service.
4. Is your software suitable for different industries? (e.g. pharmacy, metal, perishable goods, …)
Yes, we support diverse verticals from aerospace to fashion with fresh food in the middle. However, our software is primarily a programmatic toolkit tailored for quantitative supply chain optimization. While we do address many verticals, it usually takes a data scientist to craft the finalized solution.
5. What characteristics of your software differentiate you from other optimization software? (Unique selling proposition)
Classic forecasts, and by extension the classic inventory optimization theory, work poorly, surprisingly poorly even. It took Lokad years to realize that the main challenge - statistically speaking - was related to the extreme cases and that is what costs money in reality. Lokad delivers probabilistic forecasts. Whenever inventory is involved, probabilistic forecasts are just better than the classic ones.
6. For which computer platforms is your software applicable? (e.g. Microsoft, Apple, Linux, …)
Lokad is a SaaS (webapp) built on top of a cloud computing platform (Microsoft Azure). Our clients are very diverse. However, in supply chain, there are still more IBM Mainframes out there than OSX setups.
However, without a cloud computing platform, it would be very impractical to run the machine learning algorithms that Lokad routinely leverages. Thus, our software is not designed to run on premise.
7. Does your company provide standardized or personalized software solutions?
Tricky question and subtle answer.
Lokad delivers a packaged platform. We are multi-tenant: all our clients run on the same app. In this respect, we are heavily standardized.
Yet, Lokad delivers a domain-specific language called Envision. Through this language, it’s possible to tailor bespoke solutions. In practice, most of our clients benefit from fully personalized solutions.
Lokad has crafted a technology intended to deliver personalized supply chain solutions at a fraction of the costs usually involved with such solutions by boosting the expert’s productivity.
8. If it is a standardized software, which features are included in the standard package of your software?
We have over 100 pages worth of documentations. For the sake of concision, they won’t be listed there.
9. Are there add-ons available? If yes, which? (e.g. spare parts, …)
We don’t have add-ons in the sense that every single plan - even our free plan – include all features without restriction.
10. For which stages/levels can your software optimize inventory management? (e.g. factory, warehouse, supplier, …)
We cover pretty much all supply chain stages - warehouses, point of sales, workshops – both for forward and reverse logistics.
11. Is your software solving the problems optimally or heuristically?
Computer Science tells you that nearly every non-trivial numerical optimization problem can only be resolved approximately. Even something as basic as bin packing is already NP-complete, and bin packing is far from being a complex supply chain problem.
Many vendors - maybe even Lokad (I try hard to resist to marketing superlatives) - may claim to have an “optimal” solution, but, at best, this should be considered Dolus Bonus; aka an acceptable lie, akin to TV ads boasting unforgetable experience or similar semi-ridiculous claims.
I advise to check my earlier post about top 10 lies of forecasting vendors. Any vendor who would seriously claim to deliver an “optimal” solution - in the mathematical sense - would either be lying or delusional.
12. Which algorithms is your software using? (e.g. Silver-Meal, Wagner-Within, …)
Both Silver-Meal and Wagner-Within come from the classic perspective where future demand cannot be expressed as arbitrary non-parametric distributions of probabilities. In our book, those algorithms fail at delivering satisfying answers whenever uncertainty is present.
Lokad is using over 100 distinct algorithms, most of them having no known name in the scientific literature. Specialization is king. Most of those algorithms are only new/better in the sense that they provide a superior solution to a very narrow class of problems - as opposed to generic numeric solvers.
13. Where are the limits in terms of input quantities which can be calculated at once? (e.g. size of cargo, different products, period of time, …)
The numerical limits of our technology are typically ridiculously high compared to the actual size of the supply chain challenges. Ex: no more than 2^32 SKUs can be processed at once. Through cloud computing, we can tap nearly unbounded computing resources.
That being said, unbounded computing resources also imply unbounded computing costs. Thus, while we don’t have hard limits on data inputs or outputs, we pay attention to keep those computing costs under control, adjusting the amount of computing resources to the scale of the business challenge to be addressed.
14. How many variables can be chosen and how many are given? (e.g. degree of service, period of time, Lot size, …)
Lokad is designed around “Envision” a domain-specific programming language dedicated to supply chain optimization. This language offers programmatic capabilities, hence again hard limits are so high they are irrelevant in practice. Our language would not support more than 2^31 variables for example.
However, dealing with more than 100 heterogenous variables at once would already be an insanely costly undertaking from a practical perspective: each variable needs to be qualified, fed with proper data, properly adjusted to fit into the bigger model, etc.
15. Does your inventory management support multiple supply chains for one stock?
Yes. There might be multiple sources AND multiple consumers for a given stock. Inventory can be serial too: each unit of stock may have some unique properties influencing the rest of the chain. This situation is commonly found in aerospace for example.
16. If yes, can those supply chains be prioritized/classified? (e.g. ABC/XYZ products)
Yes. However, prioritization is usually more expressive than classification. We strongly discourage our clients from using ABC analysis, because a lot of valuable information gets lost through such a crude classification.
17. Which method of demand forecasting is implemented? (e.g. moving average, exponential smoothing, Winter’s Method, …)
Moving average, exponential smoothing, Holt and/or Winter’s methods, all those methods produce classic forecasts – aka average or median forecasts. Those forecasts invariably work poorly for inventory optimization because they can’t capture a truly stochastic vision of the future. Plus, as a separate concern, they can’t correlate demand patterns between SKUs either.
Being the counterpart of constrained optimization (detailed above), Lokad has also over 100 algorithms in the field of statistical forecasting. Most of those algorithms have no well-known name in the literature either. Yet, again, specialization is king.
18. How many past periods are considered to calculate the future demand?
The idea that past demand should be represented as periods is mostly wrong. The granularity of the demand is important: 10 clients ordering 1 unit each is not the same thing than 1 client ordering 10 units at once. Our algorithms are typically not based on periods.
Then, in terms of depth of the history, our algorithms typically try to leverage all the history available. In practice, it’s rare that looking further than 10 years back yield any gain in the future forecasts. So there is no hard limit, it’s just that the past fades into numerical irrelevance.
19. Is the seasonal change in demand included in the forecast? (yes/no)
Yes. However, seasonality is only one of the cyclicities that exist in the demand: day of week and day of the month are also important, and also handled. Then, we have also made recent progress on quasi-seasonality: patterns that don’t exactly fit the Gregorian calendar such as Easter, Chinese New Year, Ramadan, Mother’s day, etc.
20. What kind of performance measures can be analyzed? (e.g. waiting time, ready rate, non-stockout probability, degree of service, …)
As long as you can write a program to express your metric, it should be feasible with Lokad. Yet again, Lokad offers a domain-specific programming language, so we are flexible by design. In the end, there is one metric to rule them all: the dollars of error.
21. Does your software support the implementation of penalty costs? (e.g. cost for “out of stock”, “capacity limits reached”, …)
Yes, it’s one special case of the many business drivers that we take into account. Those penalties can take many numerical shapes: linear or not, deterministic or not, etc.
22. Which are your three strongest competitors in your market segment?
Excel, Excel and Excel. Number 4 is pen+paper+guesswork.
23. Do you have a list of companies (mid-size to large-size) using your software?
See our customer’s page.