 Good morning, good afternoon, good evening from wherever you join. Just allowing a few people to join us in the webinar, I can see people are just connecting. This webinar won't contain any references to football despite my friend here on the call from Italy and me being a staunch previous England supporter we won't be mentioning in football. What football? Yeah what exactly What football? Congratulations again, well done. Thank you very much Neil, very appreciate it. Okay so I think we're a couple of minutes after now so I know we've got a lot to go through today so we'll start hopefully people can keep joining if there's still some connection issues hopefully not but today we have another webinar in our series I'm pleased to welcome two people to join us for a panel and discussion today it's a continuation in our webinar series which we've been doing over the last six months and this part of this mini series today we're going to look at predictive analytics as a risk mitigation tool for food so we'll have as part of the session we'll have a short presentation from Alessandro and also one from Yanis we'll try to make this interactive where we can and have some discussion and debate between ourselves there'll be also one two two or three polls and also hopefully if we have time at the end we'll get into some Q&A and have some discussions maybe from the audience or from from the chat so that's the plan for today so further more let me just further introduce the speakers sorry if we just go back one slide sorry about that so on the call today we have Alessandro Ruggieri who's the head of global supply and management at Barrillo Alessandro is Italian as we were just joking there and a keen italian supporter and congratulations for winning the euro 2020 stroke 2021 he's a food technologist as a background and as a passion for food in general and he's worked at Barrillo for over 10 years mostly spent in the r&d function and then sometime also in the new product development for the last three years as Alessandro calls it he's moved over to the dark side of the moon and joined the quality and food safety teams to bring his expertise into that area and supplement their team and as part of the global supply and quality team they ensure that Barrillo suppliers have the right regulatory compliance and quality specifications the facilities are to the right standard to deliver safe food as part of the Barrillo range of products so thank you again for joining us Alessandro and also today we have Yanis Stottis or sometimes known as John to use his English American name I guess who's the CTO Chief Technology Officer for agronome and the partner also in that company and he today will take us through his detail and knowledge around predictive analytics and the whole food account platform Yanis should be well known to everybody already from these previous webinars so next slide please yeah so today the goal for the webinar is to dig in a bit further again about predictive analytics but this time looking particularly around lab test planning how you can adjust that how you can use technology to support your decision making and obviously hopefully to minimize any kind of recalls that you don't want to suffer from your company next slide please yeah we've talked about this again so this is just a reminder for everyone before you know the big challenge for consumers out there is about the trust in the information that you you can find there's so much information on the internet the food companies have lots of information but in general the if you poll people across the system and the internet you'll find that the governments and the regulators are still regarded as the most trusted source of data we can challenge some of that because obviously within agronome and the food archive platform would capture all that data as some of the data sources but the big challenge for everyone is around you know the impacts of food and the food safety on everyone when we've still got 17 percent of the world's population who continue to be worried or experience issues from food when they're eating you know from from challenges in a safe food supply so we want to use data and technology to support that and help us to remove incidents particularly major incidents and also gather that data we keep talking about the amount of information that's out there there's so much information and how do you distinguish or pull from the from the wheat from the chaff to find the right information everybody in every company's got limited time responsibility and resources to do that but it's a huge responsibility for everyone and what we're trying to do again through this information chair and webinars and our portion and support of the use of technology is to optimize the amount of money we spend everybody's been focused and stressed with the whole covid pandemic over the last 18 months and everyone's reviewing their budgets but we don't want you to spend money on crisis management and crisis recall recalls we're trying to save the money and invest in a different way and that's one of the things that we're also going to try and share today and discuss can you use some of that money that you're investing elsewhere and use it to using technology to support your test plans so look at what ingredients you're testing how do you assess your suppliers reallocating some of that budget that's one of the challenges we're trying to share today so next slide please and just as a reminder the three pillars of risk prevention that we'll be talking through today obviously you need to ensure you've got risk monitoring scanning inside through that guide there's a whole system for scanning the horizon for government alerts lab test data internet information lab data country alerts it's all captured in there in the risk monitoring system you can also use that for risk assessment you have to assess your suppliers you have to look at the hazards different risks but you need to capture that and use it into your own model but most importantly as well now for a future thinking is using risk prevention how can we prevent issues in future as we've said previously how can we see round corners how can we use priscellius digital crystal ball to help us look for issues before we've experienced them so today again we want to use that and use Alessandro's experience to help us discuss from his real-life challenge from Barilla so with that I'm going to pass over to Alessandro just with a few questions so next slide so welcome again Alessandro thank you Nils you can see the questions on the screen there so you can be prepared so these are the questions we like to add and obviously Janis can join in as well but how do you use what kind of process to use for decision-making today for your assessments well today in Barilla we have two streams about risk assessment the first one is very concerned and focused on emerging risks so what is in the bottom part of the iceberg and so this is more our research and development and long-term approach while what you're seeing here on the chart is to manage what we called emerged risk so we have three streams of a risk assessment the first one is about the high-gimpot supply chain that we manage as Barilla so for instance durian wheat for making our semolina and then pasta, basil and tomato for our sauces or the vegetable oils we use both in bakery and in sauces production so we have this multifunctional amazing team and we try to put together knowledges and experiences from purchasing from scientists from external consultant in order to deep dive from a supply chain perspective what could be the risk for the Barilla business the second point is about what happened with our suppliers so my focus of my unit is to work with our suppliers in order to make our self-sure that our businesses are well protected and we have this process of supply risk assessment that we do on all our vendors and we use as Barilla's group to review this risk assessment every year or during the audit based on audit evidences and the last point is a very focused on innovation we have a portfolio of projects and new product development and so we want to make sure that each single new raw material and ingredient that we introduce during innovation project is well assessed and so we are prepared in order to mitigate the related risks this is basically what we do in thermal risk assessment yeah so Alessandro you said about you know I think you just said you normally review your plan every year is that historically always been that way or this is the routine I mean then of course we as all the friends joining this webinar we will have also the chance to review our risk assessment based on incidents okay this is I think one of the main reasons to improve and to use the lesson learned then we have the continuous relation with our suppliers so putting together the common knowledge in order to anticipate eventual future risk then we have a good network of external relation both with policy makers for everything about new regulation and modification to existing regulation but also with the scientific community I remember that we started the discussion internally so I was not in Barilla but my colleagues told me that they discussed they started discussing about acrylamide in the early 2000-2001 so this kind of network among industries and scientists is very effective when it comes to prevent and to work on possible future risks sure yeah and do you use different assessments for different ingredient times because I know I think when we previously discussed you said you know some you have time to control over some of your supply chain ingredients then you do of all this maybe so you maybe have slightly different approaches depending on the ingredient yeah yeah you elaborate a bit on that yeah yeah of course as you mentioned we are using more than 40 42 what we call families of ingredients and packaging and so the supply chain are very diverse so for instance we we use only for our pesto we use only fresh basil and we source this fresh basil with by five big farmers very close to our factory so we have a daily relation with them and so the dynamics about the risk assessment and mitigation is I mean very very simple because we know them we talk with them etc while if it comes to spices turmeric or cumin etc we we don't have this production in Europe and so the supply chain are a little bit more complex and so that's why we need different approach to risk assessment or maybe when it comes to food fraud of course we don't have all the knowledge internally and so we are used to call experts from the industry in order to source from their knowledge so you're right there is different techniques depending on on the risk and the split sure maybe just transition into another question then so if you can share of course which data and tools do you use for updating your plans you know what do you use specific tools or the internal tools what do you use we're both internal external for about the external I can mention it is the the tools allowing Barilla to monitor the alerts the ongoing product recalls so we we are daily monitoring these sources on of information then as I mentioned we have a good scientific networks so thanks to the knowledge of the food scientist or the other companies we are able to review our risk assessment based on external knowledge while when it comes to internal knowledge we have been working on the digitalization of our internal labs analysis so we have everything into a limbs we can elaborate through dashboard so reviewing constantly reviewing data about analysis no conformities it is an important internal source of information in order to be effective in risk reviewing and who are the main recipients of that data in your company in Alessandro the recipients meaning like the procurement people the plan operations we we have in place a multifunctional team that is our unit quality and for safety bore both my part managing supplies but also the colleagues managing the the the manufacturing quality then there is the advanced research laboratories that is a unit dedicated in developing new analytical techniques when we see our gap in the market and we see some emerging risk then we have a new unit dedicated to develop internally new analysis methods and so we have these multifunctional culture and and competencies that is very crucial yeah very good thank you for sharing that information thank you now so I think now we're gonna well I'm sure you've done this as well Alessandro but myself and Yanis posted on LinkedIn over the last few days week some polls so I think we're gonna try and move to a poll now for the audience so we can move to the poll so the question being what percentage of your current lab testing budget would you invest in a technology or technologies so you could optimize optimize your plan you know maybe now we can also bring uh Alessandro some questions and also Yanis can join us maybe for a debate while waiting for the answer one important point please vote now yeah one important point Neil is when I mentioned the input or the source of information for reviewing risk assessment I always talked about past data past facts even incident while I I know that on the market maybe there is some guys that could help us in order to introduce prediction in this kind of risk assessment review so I think that a safe approach could be starting from the first option so 10 percent 10 to 20 percent in order to test to try on your supply chain because everybody knows is on a specific supply chain supplies etc so I maybe it could be a safe approach starting point yeah yeah starting point and based on benefits it could be even further increased yeah I think one thing to add just from my reflection as well is you know this is not necessarily additional funding or additional expense this is reallocating funds from your existing program to use it to use technology to give you a different view so that's what we're really suggesting so we're saying you can use your existing budget and let's see if people agree so 30 percent are saying 45 percent uh up to 45 percent so that's quite quite high so that's the winner in this poll okay interesting I don't know if you guys wants to review that when it gets into the next slide let's just move forward yeah so I'd like to introduce you guys as we said before and no doubt it'll take us through this thinking and also the demo so thank you Jens thank you it's always awesome it's always a pleasure especially today that we have an expert from the industry like alexa alexandro thank you so much alexandro for joining us it's really interesting the the results of the last poll are really interesting for me it's very important independently on the exact percentage is very important to make here which is the benefit and which is the return of investment of making such investment so this is what I will mainly comment during the next slides and I will try to demonstrate through live predictions for some of the ingredients so we can move to the next slide from working with numerous food companies we have identified and we have we have been analyzing which is the digital risk prevention maturity of the of the companies which is the readiness level of adopting new technologies so starting from level one we see that there are companies that they are revisiting the risk assessment and the laboratory program when a major incident occurs so this is a first level and there are several companies there that are following this approach there are other companies that are starting considering more tools and more digital solutions and they are regularly revisiting risk assessment and I heard alexandro mentioned in several techniques used for the regular and periodic review of the risk assessment there are also companies that are applying more systematic approach and they are also asking the help of experts for performing such a revisit of the risk assessment and to be able to even prevent new emerging risks that appear in the industry in the food supply chain so these are the companies as alexandro mentioned that are constantly trying to improve the risk assessment they have a systematic approach in place and then we have already companies that are using some kind of third party risk prediction service one solution is one of the solutions they may be using both other prediction services or predictive analytics solutions and then we have also companies that are trying to build own risk prediction software which is very interesting it's very much connected with having a already a digital solution for predictive analytics so these are the levels that we see and one question that is very interesting and we would like to post here and get your feedback is right now how do you revisit and update the priorities of your food risk prevention plan when you're revisiting your risk assessment plan and we have these five options which as nearly mentioned we have also posted in several channels but it's it would be very interesting to get your feedback as well on that and yeah it's maybe while they're waiting for the poll so that is that a way that you're also using to categorize your customers as you assess you know the industry you like putting them into the the five blocks of the maturity model yeah it helps us very much and it helps also very much the companies to identify the maturity level where which is the readiness level because then we see the very small steps that we can do in order to to get to a successful risk assessment and a risk revisiting plan and to develop very very well the prevention measures like for instance the laboratory testing program so this helps a lot Neil yeah I was going to say sorry to drop you I was going to say that I'm sure Alessandro has seen that as well before because most food companies will have used a similar model for culture or for quality assessments of their own plans or for supplier assessment and our code for me we use that regularly in those five buckets you know to show progression from from different stages of maturity from developing right through to being you know expert so I think it's a good it's a good model yeah and what we see here and thank you so much for providing your view on this is that the majority of the experts and of the companies are having a regular revisit risk prevention and this is very very interesting and it was also commented by Alessandro but Alessandro would you like to comment something about this result? Yeah I think we could expect the I mean the distribution of the answer where everybody's talking about digitalization as a food manufacturer is in the very middle of this digitalization transition and what the beauty here is that we see a clear path to improve so to go through to the different stages there is no disruption but I think each company could gently try to improve year by year webinar by webinar and sharing common common knowledge because we see clear benefits in introducing prevention and forecasting prediction when assessing the risk as I mentioned all the inputs we have today are coming from the past or from yesterday and there is no abstract approach in order to predict the future so I see a clear need then we have to understand Neil was mentioning the percentage of investment because of course it is easy to scary some finance guys and colleagues to ask more money because there is a new food safety trade etc but as a manager I think we should be very disciplined in putting on a table benefits costs in order to select the right path company by company. Thank you so much Alessandro and thank you all for giving us the answer to this question so what what we are saying and when we are discussing with food safety experts from companies is that independently on the level on the maturity level and we see that the most of the companies are on the second step using a periodic periodic revisit of the risk but independently on the step we can help and the digital solutions can help to make the next step to make one more step so for instance even if you are more at the reacting mode revisiting the risk when a major incident occurs by adopting a very good risk monitoring solution you can go and you can apply better of the periodical risk update and if you have also an automated risk approach you can have a more systematic process of updating your risk or of achieving a live risk assessment that we are mentioning and then making the next step that Alessandro just mentioned that it's it's very good to have a very good historical knowledge to know exactly what happened in the past and all the things that have happened in the past but it's also very important to be able to see behind the corners to see to predict things that may happen based on all the different parameters that we are monitoring in the global supply chain. So in the next slide I in the next slides I will try to describe how digital solutions that provide predictive analytics and can help you in making these steps how these such tools can help in optimizing a very important preventive measure like the laboratory testing program and especially how we can adjust and we can refine the program based on the predictions and the insights the very important insights that we can have from a digital solution. So for that I will ask you just to assume that we are talking about a company that is using different numerous ingredients we can assume a company that is mainly in the bakery industry but it may be for any other industry just for the sake of having an example here we can assume that we have company a company that have key ingredients like oregano peanuts black pepper wheat butter honey and many more and that this company has a specific laboratory testing plan in place that is based on three parameters and the number of ingredients that the company wants to test for the parameters the chemical the physical the biological the fraud parameters that the company wants to test and there are numerous parameters of course as you know and also for each ingredient which is the sampling that the company wants to apply so based on these three parameters we can perform a sizing of the laboratory testing program so how big is the investment that we need to make and if we can if we assume the specific and simple numbers for such a case in the next slide so for instance we can have a company in the bakery industry that has 100 ingredients for each ingredient on average the company tests 10 parameters like the food safety parameters but also food fraud parameters and for each ingredient the company is getting one sample per month and send it to the laboratory to the laboratory either this is internal laboratory or an external laboratory and this is just for the sake just to have a very clean numbers of course this can be hundreds of ingredients the parameters can be more or less and the sampling can be different but in this simple case an average investment annual investment in the laboratory testing program is more than one million is 1.2 million with very applying a very simple multiplication formula it's even more complex and of course it can be more the budget can be more alessandro this is this a reasonable amount of money that the company can invest it is a small amount yes this is an example but it is not crazy at all this is a one thing to consider as well is of course you've got the internal sampling testing on laboratories and then external third parties which is really where the company costs go and different suppliers deliver in the same ingredients so the the matters could be even more complex that's that's very interesting very important thank you so moving to the going deeper to this specific example we see that there are several challenges for such a company in order to optimize the laboratory testing plan challenges like how often should the company revisit the test plan which are the trigger events that causes this revisit should i focus more on increasing the testing on high-risk ingredients or do less testing for ingredients with low risk or the ones coming from the trusted local suppliers so there are different this simple equation that is a very something very simple that we mentioned gets more complicated and there are different challenges that the company should face. Alessandro would you add something to these challenges is there anything more that we should consider here? I mean again as manager I think we must be sure that each single euro or dollar that we invest is well spent spending more is not meaning that we are spending the better as we the european food industry has been invested from the ethylene oxide the crisis considering the number of recalls it looks that we didn't exactly spend perfect money so I think that a crisis like this should trigger a deep review of our way of doing and all these three questions I think they are the question that we're asking to our side daily and then definitely agree with you Yanis the the the risk factor and the prediction of future risk should definitely be one new input to our risk assessment processes. Yeah Yanis can I add something? I agree with Alessandro there obviously definitely but one of just reflection again when I'm just listening to you both talk I think about the full mapping of the supply chain as well so not if I'm just Barilla receiving ingredients but you know the supplier the suppliers testing sometimes at third parties they've got their own internal labs testing we've got Barilla testing in line production we've got their own testing there sometimes second labs and third party labs so if you look at the holistic mapping of what testing's done if you're almost say from the farm from the field to the final product it's huge we don't connect that together because it's not all in one company it's multiple companies and then if you also look at the whole point of if there is an issue like ethylene oxide like Alessandro said everyone's testing that from every company there's got an issue so the ones who are making a good business out of that naturally are the external labs who are doing the testing and they're only going to do what they're asked to test for but no one's really fully connecting this together this data to reduce the testing and do more on the prediction that's what we're trying to get to yeah it's an interesting way when you look at the whole supply chain and think how complex it is how can you reduce the testing without reducing your assurance level that's what you're trying to get to yeah it's very important trade-off and I will further analyse this trade-off that you are mentioning later in the slides so thank you very much Neil for the intervention and we can move to the next slide of what the predictive analytics can tell us for the ingredients of such company and how they can help in designing the best plan here we activate and we use the power of the data but also new methods that can be applied in order to predict events in order to identify anomalies in the series of events that we have in the supply chain and we use something that we call the intelligence equation that is based on selecting the most appropriate data in order to provide answers to such questions to such challenges to select we are also selecting the best prediction method either this is a regression method or this is a classification method it depends always on the problem of the question that we want to answer and using the best combination of these two we are trying to predict the events and predict what will increase or what are the new things that it seems will come up will potentially come up in the global supply chain so in the next slide we will now move to the live demo just I will show you how just give me a moment to share my screen okay I would like to in a very very short demo I would like to show you how in practice predictive analytics could help us in order to identify which are the ingredients that are right now in at high risk or at low risk which are the hazards that will likely to increase for these ingredients which are the risks the risk for these or the risks for each ingredient that are potentially will be increasing during the next month and how the finished products the best store or the my serial bar is affected because there is such an increase so the first part is very much connected in such a prediction predictive analytics dashboard the first part is connected to the monitoring and based on the monitoring of all the incidents the system the predictive analytics solution can tell us for which ingredients we see here several ingredients and can tell us for each which ingredients there will be an increase of incidents and there will be hazards that will likely increase or will appear as emerging hazards and using this information we can also estimate the risk so how we are doing that we see here that we have several ingredients the system will highlight to us either in green or in red the ones that for which we will have increasing incidents the system predicts that we will have increasing issues so for instance if in personal we are using oregano we can see here that the system highlights that there will be an increase in incidents for oregano during the next the next 12 months so we can click on oregano in order to see tailor made specific predictions a model that has been built for oregano based on the previous incidents of oregano so it seems that there will be an increase in incidents and also the system highlights to us which are the companies for which there will be an increase of incidents the sourcing companies of this ingredient of the oregano and by clicking on this country we can see which will be the trend of this incident so in this way we can identify which are the ingredients at high risk and which are the countries that are exporting and from which we are importing and getting this ingredient and we need to pay attention because it seems that there will be more incidents during the next months and getting to the hazards part we can see that the system highlights to us that there will be an increase in alkaloids in past a significant increase but there are there will be also increase in salmonella and there are some new types serotypes of salmonella like the salmonella mondeca that is mentioned here that is for the first time identified in such ingredients so you need to pay attention to that and this is also reflected in the predicted trends for risk so we can see the hit map of the risk for oregano and we can see that the past is a risk the risk of past will increase and will be getting more and greater than salmonella and the system will also highlight to us which is the supplier from which we are getting this oregano this ingredient so we can immediately activate the preventive measures or if we if this is a local trusted supplier we can say that we will not activate measures or we will increase a bit or something or we will just do some ad hoc sampling for the specific lot but this helps us a lot to identify which are the suppliers and if this is a trusted supplier so in a similar way we can perform such analysis for any ingredient even if this is an ingredient that for which we see that there are less incidents predicted we can still go and use the justification of the data and the predictions that are estimated with a specific accuracy that is also shared here because we want to be very clear and very transparent on the accuracy of such predictions and again you can see that for instance in the case of cocoa the incidents will go down are predicted to go down and there will be a slight increase of 2.4 d of a pesticide used in the cocoa that you may want to focus only on this part in the parameters that we will select for lab testing so this is a very simple way that we can use predictive analytics with few clicks in order and following four steps for very simple steps in order to identify which are the regions for our lab testing that I want to increase sampling and which are the ones that I want to decrease sampling so I will get back to slide and using this information I will try to estimate the return of investment of such solution when we have this information when we know for instance that for 60 for 60 ingredients out of 100 there will be an increase of incidents and there will be parameters that are predicted to be increasing within the next few months and for 40 ingredients that we have a decrease of incidents that we have lower risk so how we can use this information in order to revisit our laboratory testing plan regularly and dynamically so we know that for the first 40 percent of the ingredients the prediction of the risk is low and we know also that for 60 percent of the ingredients there will be an increase in the risk but for 10 percent out of this 60 percent we are getting these ingredients from trusted direct suppliers so we will not increase the sampling this could be used and this is the way that we can apply this information and then the question is how much you would reduce the sampling for ingredients with low risk based on the predictive analytics and this will help us to invest better our money to have the optimal the optimum laboratory testing plan but also focusing and increasing the sampling for the high risk we are performing a risk reduction because we can be more safe and more sure that I will identify early emerging risks in the global supply chain that are affecting my ingredients and if we assume that for the reduction of the sampling we will reduce we will send for the low risk ingredients half of the samples and we were sending once the sampling was 12 samples per year now we will send six samples per year per year for this specific ingredient for a specific parameter and that we would like for some of the ingredients to increase by 30 percent such sampling rate then if we make again estimation using this new schema of the laboratory testing this new strategy of defining the sampling then in the next slide we are showing you we have a calculator that will reduce the cost of the laboratory testing plan by more than 100 000 dollars and this is an indicative number that shows that still if you have the good information you can still have a very good program because you will focus more on the risky regions of the ingredients and on the suppliers and you can reduce the sampling for the low risk regions so it's really if it does work to make to follow such a strategy in the case of laboratory testing Alessandro do you believe that following such a strategy could help to make a better investment of each dollar for the laboratory testing and to have a very good prevention approach? Yanis definitely yes okay again we are discussing about tentative numbers and one example but it is I think a good explanation of what a new strategy or new approach would be so yes I personally find your presentation very interesting because this is a different approach to an old problem that we all have and the most and I will close my part and will be very happy to hear the questions and to continue the discussion with you with the importance of having an effective laboratory testing plan that can help us to prevent the next recall so if you have such a such a good plan or if you don't have such a plan we hear from the companies that there are several incidents that happen as you mentioned also Alessandro for the case of ETO but also as mentioned by by Neil for other cases that if we don't have such an effective plan there is a very high probability of having internal recalls internal incidents that are not affecting all the supply chain of a company but also having an external public recalls that can have a very good very very bad impact on the company on the on the financial part but also on the brand part and we have just assumed here and used some numbers from our discussion with the companies but also from previous studies like the one that was performed for the average call of a major food recall in the industry that was published by the Grocery Manufacturer Association and was saying that the the average cost and the starting number is one million dollars and this can be up to 10 million and it depends on the size of the recall so if we if you we assume that in the last three years in the next slide we have 10 internal issues that were identified and that they were not finally they didn't affect the whole supply chain and they didn't get up to the consumer and one major food report during the last three years for a company this creates a very high cost of this incidence for the food company if you cannot have an effective laboratory testing plan and with that I will just ask you that in the next slide please that if you are considering to adopt the digital food to make a small step towards the risk prevention tool we can help you first of all to calculate the Roy we can sit together and we can make this kind of calculation and see which could be the return of investment so if you go to the specific link either scanning the QR code or just typing agronome.com slash Roy you can book a slot so we can sit together and discuss which could be the return of investment of such a digital solution thank you so much and really interested to hear your thoughts about Neil getting back to you. Okay thank you Janice thanks for a detailed explanation as always and a very good overview of the demo I think one thing maybe we've got a few minutes left hopefully we can get some questions but just just for me just to summarize now from what I think we've heard and just just to close out the webinar I think you know it's very interesting for me and hopefully for everybody else to hear from Alessandro about Barilla and their approach and how they're using their different solutions to assess their risks I think Janice's explanation around how you can use technology to modify your existing lab test plans is a good insight hopefully people can contact and follow up now and I think the whole demo always never fails to please me let's say when you're showing the live demo Janice and I know Alessandro has the same thoughts the fact that you can go in drill down drill down again and get to the specific ingredient type all the risks is very impressive in the food archive tool so I think that's really really insightful but the other two things I just also wanted to reflect in the call you know maybe Alessandro and you can also talk about it is we introduced two new things the digital maturity tool an assessment process you've come up with today and also on screen here now the ROI tool that people can use in contact so I don't know if you want to reflect any more on that and if there's any other questions from anybody I don't see anything in the chat unless anyone's got anything to share thoughts Alessandro from the yeah for about digital maturity I mean at least let's be aware about where we are and what we have to do to improve so these are very simple and infographic Janice that you have developed is quite clear and then we can easily position ourselves and our company on the chat and you mentioned the cost of a product recall but we didn't mention the reputational cost that we have to add on top and recall means crisis and when you are managing crisis internally in your companies having the rate the date the precise data enough data and the data you need it's crucial so the fact that we have we are able to be more granular to better focus our resources when there is a crisis ongoing I think it could happen yeah I think Janice I didn't mention it but I thought your cost estimates were very conservative there as well I'm sure you did that on purpose but I would think that in many companies from my experience it would be much greater the cost so much higher than that for a recall both internal and external yeah as I mentioned this is a starting point that this is the minimum but it's still very it's very important cost and not counting the reputation on the brand diamonds yeah that's huge it is very important and now we're close to the time but there is one question in there I think from Nico Ackermann maybe it's for you Janice about is the system also suitable for labs and customers I mean I could answer that I'll let you answer no no please please do yeah the system is is designed for the food companies but if our opinion is is that if it can create value for other segments in the food industry like the laboratories which has a have a major role in the supply chain a major role of course it can be used that's all there there's no limitation in the in the way that the system can be used and we have modular a modular upload approach on the modules on the functionalities that someone can select and so it cannot adopt it can create a package that will really fit and will create value for the type of the companies the company that he has so there's no limitation and this is the the very simple answer there you go you got your sales hat on at the end everyone can use the solution it's open for everyone that's what I would be seeing yeah but it needs to create a value yeah this is this is my point so if it creates a value everyone is welcome man sure I think the other piece we touched on in the conversation about if you're looking at a holistic cost of testing I don't think people have that data at hand and I don't mean just within the company you know the full supply chain I think it's much bigger than you would realize I mean it's probably duplicative because the supply is testing it and the manufacturer is testing it you know sometimes all sampling for the same same analysis but I guess that's a more complicated issue to resolve but maybe I think we're about nearly at time if there's no other comments from Alessandro thank you again both of you for participating in my webinar very informative thank you Yanis Alessandro maybe just an email thank you and to the next slide I think we just want to end on the last one if you need more information about agronaut and the company please please get in touch Anna at the contact information below we'll be happy as the head of customer success we'll be happy to talk to you and help answer any of your questions or queries it was a pleasure to to have a such an important discussion with both of you Neil and Alessandro thank you thank you so much a pleasure and an honor because you are very experienced and these are very important and complex and critical things so we are approaching them with respect and we are trying to go deeply and to provide value at the level of knowledge of sharing experiences of estimating together the return of investment so thank you so much all thank you very much thank you very much thank you so much guys thank you thank you bye bye