Month: January 2022

https://spacetechnation.com/2021/12/startup-of-the-week-progressive-labs/

COVID-19 is perhaps the one inescapable factor in our lives at the moment. And arguably nowhere is it more felt than the global supply chain, especially now in the swing of the holidays across much of the world. One company seeks to build a solution to the global beast, and they are using cutting-edge artificial intelligence to do it. CEO of Progressive Labs Yaniv Dinur took some time to sit down with SpaceTechNation and look at what the supply chain really needs for a stable future economy.

STN – With the global supply chain issues as they stand, what does Progressive Labs do to address the complexity inherent in the system?

STN – That sounds like it should have been something we were already focusing on… or do I have the wrong idea?

YD – The issue that we have uncovered at Progressive is that everyone relies heavily on forecasts, which have a few issues. One, they are only as good as the data that goes into them. This is a problem because most forecasting happens at higher levels. The second is that forecasts at high levels are only accurate with large data sets, and even then, there is error. The nature of complexity is that you can’t predict things for certain, but human nature is to apply these forecasts like they are the absolute truth. And they just don’t apply at the lower levels.

STN – So it sounds like the solution is to look at all levels, like you mentioned. How do you manage to do that?

YD – Well, we use genitive algorithms and machine learning, AI essentially, to develop models at every level of demand. So the lead time of the retailer maybe a few days, and they have their demand, and the wholesaler might be a few months, and they have demand, on up to the manufacturer where the lead time is often years. So we integrate demand sensing at all levels to allow businesses to react quickly.

STN – That seems like it would only work if you had the full supply chain market data. How do you work with companies that don’t have vertically integrated supply chains?

But we can us cascading demand signals to essentially provide very accurate forecasts at each echelon of manufacturing, wholesaling, and retail. This obviously needs a translation to some industries, but the principle is the same.

STN – Ok, I think I’m beginning to build a picture here. So, if you had to sum it up, what does Progressive Labs do for supply chains and companies?

So, Progressive uses propriety machine learning and genitive algorithms to let companies dynamically manage buffers at all levels of a supply chain. This increases efficiencies across the board, and gets the right amount of product to the right people at the right time, with minimal overhead.

STN – Thank you so much for your time and energy. Would you answer just a few more questions for our readers?

• What is the origin story of your company? Why was it founded, and how?

I was a supply chain consultant for many years, working globally and never at home. I hoped to take all the knowledge and experience I gained in the consulting years working with the biggest companies in the world into a one SaaS solution and travel less. Well, one thing happened (SaaS solution) and one thing not (be more at home). I founded the company in 2014, teaming up with a powerful technological partner.

• Is this your first experience with deep tech/aerospace?

No, in my days as a consultant, I worked a lot with the aerospace world, and in my corporate days, I was working in the defense and later the communication tech industry for many years.

• Who are the team members, and how was the team formed?

The company’s CTO is Pini Usha, a very experienced software engineer who worked for many years to develop top-of-the-art fin-tech solutions. Also, our head of development, Vicko Saban, came from the same world of financial software solutions.  

• In the future, would you sell (exit) or rather build a large company, and why?

We trust our focus is to develop breakthrough technology and methodology for the supply chain. Eventually, it is a game of the big players, and there are consolidations all the time in this market. If your mission is to make a change and really help the industry, teaming up with the big players will eventually be the right direction.

• What impact did COVID have on your decision to start the company, or on your current activity?

COVID19 created much interest in intelligent supply chain solutions. The uncertainty that typifies supply chains grew dramatically, and more and more interest from all players, from customers to VCs, was created. 

• Who are your ideal partners?

Today we are working with some market giants. We have some huge customers that used to use the old traditional solutions, realizing that our expertise as ERP agnostic add-on solution is needed. We trust that growing more and more will generate the interest of some major players in the tech world and some major players in the SI world to work with us.

• Where do you see your company five years from now?

Selling $100-200M

• What is your definition of success?

Change enough customers to depart the old way of managing the supply chain by adopting a dynamic, demand-driven, constraint-based solution. So much waste will be eliminated, so much value will be created.

Read more about Progressive Labs at www.progressive-labs.com

General –

Many companies invest a lot of effort and money in improving their supply chain. The ultimate constraint of flourishing any supply chain is disturbances to flow in the chain. Finding these disturbances is the key to improving them. Most actions to seek improvements don’t increase flow but decrease noise.

 

We are all trained and programmed to solve problems, from kindergarten to school, from expectations at home at a young age to the best MBA schools. They all expect us to identify problems and solve them. We created a culture in which solving problems is what is expected from managers. Consequently, when managers encounter a new problem or a decision they must make, they react with a solution that seemed to work before, as they believe they must solve the problem and solve it fast.

 

A problem is a symptom of a deeper core problem/root cause. We focus on solving it, instead of taking the system perspective, looking from a far distance, overcoming our instinct to “focus” and solve the “problem at hand”, and identify the core problem beneath our symptoms to ensure a robust, sustainable change that will eliminate the negative symptoms (effects) we see in the system day in and day out.

 

The correct process for “problem” solving –

If I need to give one piece of advice, I will highly recommend going through the challenging process of analyzing the supply chain as a system. We may find that what we see as “the problem” is only an effect. Solving an effect will bring the problem (or a different manifestation of the problem) again.

 

While one of a manager’s most important responsibilities is solving problems, Finding the answers to difficult questions that are sometimes a source of great perplexity and distress for the organization often falls to an organization’s leaders. A company’s success depends on managerial problem-solvers. Issues arrive in all sizes, ranging from daily nuisances to organizational crises. The common belief is that managers who can systematically think through the facts, diagnose the situation, and find an accurate and workable solution will help the business thrive and prosper. Effective problem-solvers are teaching in MBA courses, believing it guides students towards achieving goals by eliminating frustration, confusion, and misunderstandings before they become unmanageable. They build cooperation and collaboration between individuals; trust eliminates rework and fosters continuous improvement. Instead, there is a belief that the best managers can often “sense” problems with keen insight. They may notice a deviation from standard team performance, such as a missed deadline or an unmet sales goal—and when the team’s plans go off the rails, these managers automatically begin the problem-solving process. Well, yet, all the companies I am working with, excellent successful companies, full of brilliant people with good intentions that are investing hours of hard work in their jobs, are facing a reality that is extremely far from where they want to be.

 

Post COVID19 modern SC solutions

The need for speed, agility, personalization, and cost-effectiveness

General –

The ripples of the COVID19 crises are still evident in the global supply chain. Companies struggle to get their raw materials and parts and deliver their goods downstream. More and more companies are investing in understanding different approaches for managing supply chains and overcoming the inherent uncertainty and variability in supply and demand. These uncertainties and variabilities result in severe shortages in some SKUs and, at the same time, some significant surpluses in others. It is evident that the common practices of relying only on forecasts, Min-Max methods, or “sell one, get one”, are totally not effective anymore.

 

What should be our considerations when evaluating modern SC planning and execution systems? We used to say, “forecast is always wrong”, but the belief is that forecast is a MUST in SC planning and execution systems. Indeed, the forecast is a must for long-term planning in SC upstream aggregation points. This is needed because of the vast difference in lead times upstream (slow or very slow) to downstream (fast or very fast). But, trying to plan the POS where the forecast accuracy of the SKU/Stock location is inferior is a big mistake. Reality is calling for a different approach. Moving from push to pull systems (based on actual consumption and timely demand sensing) is a significantly more effective SC solution. 

 

 

What is needed from modern SC systems?

1. Dynamic buffers – to deal with the inherent uncertainty and variability in the SC, the system must calculate the SKU/Stock location targets (Buffers) daily, based mainly on actual demand and affected by the available forecast (primarily to be able to react to time events like promotions, etc.).
   1. Setting the initial buffers – this first challenge is met mainly by using state-of-the-art AI and ML to go over 2-3 years of SKU/Stock location consumption to learn the consumption patterns. This should learn patterns like seasonality, special events (like high weekends sales), etc. The algorithms should consider out-of-stock events, bulk orders, and much more.
   2. Calculating the buffer –the SKU/Stock location inventory target based on consumption today, near-future forecast (while calculating & considering the forecast accuracy), and additional inputs. 
   3. Correction mechanism – such a mechanism should be in place to check the buffer calculation vs. the actual consumption, so if the buffer was too small (high buffer penetration), the buffer will be increased automatically. If we have much more inventory compared to the buffer size, the buffer will be reduced automatically. Doing that in millions of buffers without overshoot or undershoot is a real art.
2. Specific constraints modeling – Calculating the required replenishment and calculating the dynamic buffers in not synonyms. For each particular supply chain, different constraints need to be modeled (and fast) into the system.
   1. Being able to customize FAST all the relevant supply chain constraints is a MUST in modern systems. The constraints changes between different customers are meaningful, and without the ability to model them, one cannot produce a relevant, effective system.
   2. There are different types of constraints in different systems, to mention a few –
   3. Physical constraints – MOQ, Full truck, WH starvation, WH capacity, picking capacity, distribution days, and more.
   4. Policy constraints – Max category, Full shop, trigger point, economic order size, transfer between shops, packaging, and more.
   5. SC architecture constraints – Omni Channel: today’s SC is typified by a challenging combination of physical shops and hyper online activity. Seamlessly working with shops, online, wholesalers, and distributors. Vertically integrated customers (from production to shelves). 
   6. Merchandise constraints – all kinds of marketing constraints like “complete set” (of measures) in fashion, the timing of returning SKUs back to the warehouse, and much more.
3. Cost-effectiveness – The lockdowns put massive pressure on resellers (mainly retailers) to cut costs. Any modern system must be a SaaS system with very fast integration to present a speedy ROI.
   1. Any system should present the following characteristics to give a swift ROI.
   2. Speedy time to value – fast integration both on the data level (using WebAPI or secure file exchange) and the ability to model the relevant constraints.
   3. SaaS model – Software as a service, moving from the Capex pocket to Opex. A monthly fee with no strings. If the system does not bring value, we do not use it.
   4. ERP/MRP/WMS agnostic – as an add-on system, it should be integrated with any system and not be limited to specific infrastructure.
   5. Easy to use – The majority of the work is done automatically. Millions of calculations and receiving back the results are seamlessly done within seconds. Nevertheless, capabilities of a working tool to planners with strong “what if” simulation capabilities must be in place.

 

 

Progressive Labs Unique Value Proposition –

1. Many companies today are already offering dynamic buffers. This is a significant step forward! Nevertheless, this is only the first step in creating a capable system to deal with the market’s needs. The same goes for better systems working on the cloud. The SaaS business model and modern AI and ML capabilities are all needed to answer the market’s needs, and they are all offered by some other companies. But the ability to create, and fast, Specific constraints modeling, is the real challenge!
2. Progressive Labs technical capabilities with the extensive knowledge in working with so many customers all over the world, big and small, in a different position along the supply chain, with different areas of products and processes, allowing us not only to model any specific new customer fast but also offer new relevant constraints, customers and prospects never could use in their day-to-day life as they did not have a way to consider in the past.