TOP-5 specifics of Fresh category Inventory Management in grocery retail

Hello everyone! Nice to see you at our webinar! We will just wait a couple of minutes for those who are joining or joining, and we will start the webinar. Today we are going to talk about a specific of a, very interesting category for every grocery retailer, which is fresh. We know that it's a very painful category for everyone, and the topic is really hard. So I hope that today's webinar will be really insightful and interesting for you. So please join! Maybe you can introduce yourself; what retail chain do you represent. Maybe some questions, like some hard questions which you would like to receive the answers for. You can type them in chat, and in just a couple of minutes and we will start.

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And actually, according to registrations, I saw that today we are going to have a very international team of listeners, so we have a lot of countries here. We have European countries, and we have Middle East countries. So we are really happy to see everyone to see such an international team here. Ok, I think that we can start. Maybe somebody will join and join in a while. So today, the topic of our webinar is TOP 5 specifics of FRESH category inventory management in

grocery retail. But actually, when we were preparing for this topic, we understood that it's almost impossible to name just like five, and we added a plus one. Because this one was also very important, it will gonna be five plus one specific for managing this very sensitive, this interesting fresh category. Before we go to the topic of the webinar, let me introduce the speakers who will be conducting the webinar. So today it's Helen Schepanik, she's Product Director. She has very extensive experience in working with retailers in different verticals and specifically with grocery retailers as well. So she's an expert, and she has an experience of 10+ years in this industry, and she definitely has what to share. And it's gonna be me :-)

Sorry and you made me, ok that was my purpose. Ok, and the second presenter it's me, my name is Ana, and I'm Head of business development. I work with clients also rather closely. I know their pains. So I hope that today we will answer your questions regarding this fresh category and that this webinar will be useful and insightful for you. Before we go to the topic, let me tell just a couple of words about our company. Maybe some participants know us, already visited our meetings, but some are not, So I just a brief overview - of what we do. At LEAFIO, we optimize and automate solutions for retail. And we have different solutions, and we try to approach that from different angles. The first is like it's all a single platform that contains several modules.

The first one is inventory optimization. That's the solution that helps to automate the replenishment process for the retailer, starting from the stores and ending with distribution centers and warehouses. And also, this tool is empowered by a BI module, so that's actually a tool that helps to deal with the issues of the fresh products as well. So we will be sharing our experience today from the angle of this inventory optimization tool. The second tool is promotion management, so if we consider inventory optimization as some basement, so the promotion management is something like the next step here. So it's the tool that uses AI and machine learning algorithms to provide very efficient promotion management. This tool helps, first of all, to manage everything in one place,

like all the promotion campaigns. It helps to make the order and make the forecasting for all promo campaign, and additionally, if the data is very mature, it helps to recommend some promo mechanics and discount for the typical promo campaign. And the last tool module and part here is the planogram optimization solution that helps to manage and build the right end-to-end merchandising process starting from macro spacing creation and ending with the planogram creation, planogram execution, and control of the planogram at the level of stores. So at the company, we have a very good experience. We know what we are talking about, and we built our solution based on the experience of our customers and based on the demand from the market. Currently, we have projects in 16 countries. We implemented more than 160 projects,

and at the moment, we have around 100 employees on our team. We are actually working with different types of retailers with different verticals. Grocery retail is our like sweet spot that's we have a very good experience here, but also we work with the toy houses. We work with pharmacies.. We work with gas stations and other retail verticals, but today, of course, like we are going to focus on the fresh category management. So it's more relevant for the grocery retail business. Sometimes a fresh category is just specific to some retailers, but in most cases, it's the part of the assortment for like supermarkets, convenience stores, and so on. So let's start, and before we go to the description of these fresh categories, I would like to say that we have very extensive experience in automating this inventory management,

and we help retailers to manage all product categories at all levels of the supply chain. So at the level of stores, at the level of regional warehouses and distribution centers, but today we would like to talk about the specific of this fresh category. In terms of inventory management as it's one of the most sensitive categories and always requires additional attention from the site of inventory management. Since we have made a dozen of projects with inventory management automation in this particular category as well, we have identified some common rules and patterns that are specific to fresh and implemented them in our algorithms in our solution so that our customers can achieve very good results in managing this like painful category. The fresh category itself it's rather a broad one, and it consists of different subcategories

to manage fresh products well. It's very important to understand not only their general features of fresh but also the differences between these subcategories. So here is you can see the list of like typical fresh category, typical fresh products. So the first one is dairy products. So this subcategory is one of the most competitive. Dairy products are everyday goods, so it's one of the traffic generating products and there is, of course, a large number of vendors who produce milk, yogurt, and cheese, but local vendors do that as well, so this diversity leads to increasing competition between brands, because and vendors they keep their prices at relatively the same way which of course like leads to reducing the margin. So besides that, such effects as cannibalization

takes place a lot in this subcategory during promotional activities, and also the research specific at that dairy product they have a rather short shelf life of something from 3 to 10 days. The next one here is fruits and vegetables. It's the most complex subcategory in terms of inventory management among all existing and fresh because like seasonality plays a huge role here, and as a result, vendors have different pricing policies, and for purchasing managers, it's critically important to compare the price for the same SKU and create the order based on the best prices and conditions. So it can be the situation when two batches of goods or fresh goods or fruits and vegetables they might be ordered from two different vendors or representatives. We saw the same week even and another negative phenomenon that takes place directly at stores.

Imagine this category is an inconsistency that happens during the inventory count, so sometimes, while weighing some fruits or vegetables, people just mix their own SKU code, and as a result, there are wrong balances in the European system. But the biggest pain here in managing this subcategory is the appearance of the product on the shelf, and its expiration date, because two products from the same batch may differ. One item may look like the one to be written off, but the other one from the same page it looks still looks good. The next subcategory is bread and bakery. In some ways, this subcategory is similar to dairy products in terms of inventory management. Bread is also a traffic generation product, but the difference is in pricing because bakery products can be divided into two types. For some retailers,

it's their own production and social products, so the maximum margin for the social group is something between like five-seven percent, and at the same time for the own production, it's much more interesting because it can reach up to fifty percent and even more. One of the main features of this category is the frequency of orders because the bakery should be fresh. It should be crispy, and deliveries are done every day or even sometimes even twice a day. So and the short shelf life for this subcategory is a very short one. So it's up to like three days on average. The last two categories will merge because they have relatively common characteristics that might have cannibalization during the promotional activities. For example, if a regular meatball will be in promotion, no one will go and buy the meatball with the cheese,

and the same logic works for the meat and fish because if the dorado, for example, is sold at a discount, demand for sea bass or trout will decrease heavily. And also, sales depend a lot on how accurately and nicely this kind of product they are like the layout for this kind of product is made on the shelf. So if we kind of makes some helicopter view on the fresh category, we can divide and highlight this following specific. So, first of all, as they differ from other categories because of their short shelf life. Often it's not more than 30 days. Because of this feature, fresh can be given the title of the most risky category in grocery. Short shelf life requires a very precise balance between the availability and writes off because striving for high availability can significantly increase

writes off, which will immediately have a very negative impact on the company's financial result. And the second one is that a fresh category usually occupies a very real large share in the company turnover. For some retailers, this number can reach up to 60 percent, and at the same time, the number of SKUs can be smaller in comparison with the other categories. So having not the highest margin, such products generate a significant sales volume bringing the consumer back to the store regularly. And the last, but not the least specific, is that this category is distinguished by the peculiarities of orders and delivery. For Fresh goods orders are sent at least 1-2 times per week. Mostly it’s direct deliveries from suppliers to the stores. And the most important thing is the

peculiarities of logistics because during the transportation special temperature conditions must be kept. Like, for example, if we deliver the fish or meat, we should do that in a freezer in the refrigerator, so it should be delivered under the cold temperature to the store. So with this being said, I'm giving the word to Helen. She will speak about the specific of fresh category. We will give some hints on how to deal with specifications for each hint. Usually, we solve that during our implementation process, during our working with the customers, with the help of our solution, of course. So, Helen, the stage is yours. We cannot hear you.

And how do you hear me? Yeah, how we can… Yeah, yeah, thank you. Thank you, Ana. So nevertheless, there is a couple of specific issues that are connected with managing the inventories of the fresh category. At the same time, with the proper management of this category, a retailer can improve the turnover in general, earn more and attract customers to the store since the fresh category is a traffic generating one. So let's talk deeply about how to handle this sensitive, fresh category and earn more without additional costs for demand stimulation. So the first point here is about the data. The clean and correct data on balances sales and writes off it is a very, very crucial thing for demand forecasting and demand planning as a whole, but for the fresh category, it is very, very important. It has a higher age of

importance, and I guess the data correctness determines the final quality of the order because, with incorrect data in the input, good quality shouldn't be expected at the output. So in terms of fresh goods, there are often problems with the correctness of balances due to the late fixation of deliveries in the ERP system. Late write-offs and returns to the suppliers, of course. I mean the fixation of returns of this to the suppliers, of course, if they are possible according to the agreements with the suppliers and according to the company's policy. Also, especially in the category of fresh fruits and vegetables, as Ana already has said, there is often an inconsistency during the inventory count, so the final balance for the whole category might even be correct but for some particular use.

The information could be incorrect, so here the main recommendations would be. They would be in terms of the operational specifics and in terms of improving, let's say, the operational excellence more than the auto ordering system. So the first thing here, just a moment. The first thing here is to establish time limitations for fixations on goods delivery in the ERP system, and in this case, it is possible to minimize the risk of negative balances. The second point is to determine the schedule. Sorry for interrupting. What limitation, what time limitation would you recommend for this fixation of the data in the European system? So the best variant, of course, would be the day-to-day fixation. Day by day fixation, I mean, and I know a list of companies who

are on the rather high level of operational excellence, and they do it day-to-day, and it it would be perfect for the inventory management. But, of course, in not all cases, it is possible due to some operational reasons, I guess so. The best variant is day-to-day, but it could also be okay if all these fixations will be done no later than tomorrow. Thank you. Okay, so the second point was about determining the schedule for the inventory count according to which the count of the fresh category needs to be done more frequently than for dry goods. And the third one is to establish a time limitation for backdating documents. If

your company has such practice because you know even due to the presence of the strict rules, it can stimulate a more responsible attitude to the data for the past periods, of course. And it is very important because when you change something in the past, it is possible that you will not be able to analyze why certain decisions were made in the past. As we had a lot of cases in our projects when we were just investigating the reasons why the system calculated some kind of a strange order, and we were investigating it, and only in logs, we can see that the balance was different from the correct one on the order date. So we understand that the balance was changed in the past, and as a result,

we got the incorrect order and a lot of mess in the ordering process and reliability of orders. Yeah, actually, the data is a very tricky, tricky, how to say tricky point for every retailer. And it's one of the most common questions from our customers about data maturity and what data maturity should be. During like for the beginning of the project for the replenishment automation and during the first stage of implementation, we usually like to analyze the what's current situation, what the data, what's the level of maturity of the data, and we are working on improving this level. Helen, can you maybe say what are the perks for the retailers who are rather mature in data in terms of the data, who have like really high data quality what can be the perks for such kind of retailers? You know, as Ana said, we are working with retailers on any stage of the mature data

maturity, and sometimes we face some big issues with their data maturity, and it really doesn't stop us from implementing our solutions. During the implementation process, we are just working hard to make the data as good as it is possible. But of course, there is a variety of customers who already have the initial very high level of data maturity, and in this case, of course, it is a very great opportunity for us to implement all advanced methods of forecasting that our solutions consist. And if talking about then, let's say the highest level of maturity is, of course, the forecasting of the promotions. Of course, we will talk about

it a little bit later in the webinar, but still, their retailer has a high level of data maturity and has the data to forecast be based on in. In this case, we can use advanced methods such as machine learning and using the AI algorithm to predict promotional sales and demand. Okay, I think that we can move on. So the second point is to consider the remaining shelf life and predict the shelf life of the current stock balance at the time of delivery of the next batch of goods. You know it is not a secret that creating and maintaining bench accounting in food retail is impossible since, in one shipment, maybe goods from different batches with different expiration dates. So it is very hard, and I think that it is

really impossible to keep track of from batch the goods were sold and with which expiration date. But still, it is very important to consider expiration dates because ignoring this information can cause write-offs, especially for the goods with bad inventory turnover shelf life should be taken into account at the time of the calculating their forecasted balance on the date of the goods delivery. So it is important to understand whether the balance will be valid or, let's say, appropriate for sale at the time when this current order will arrive, so our recommendations here will be the following. The first is to consider the residual shelf life. We call it the residual shelf life in the system. It is the number of days starting from when the product is delivered to the store till the end of its shelf life. So this

information can be considered to calculate the forecasted stock balance for the date the product will be delivered. And the second point here is to consider the sales by the LIFO method. LIFO is "last-in, first-out." So to assume that the customer will buy the freshest goods first, this approach allows considering the fact that not the initial, not the fact balance will be available for sales even if the goods were not written off in time. Unfortunately, we often face untimely write off, you know, during the implementations. And it really takes time to change the process to have all the documents in the European system up to date. So, therefore, to minimize the impact of the incorrect balances, we recommend doing a balance check using the LIFO method.

The third start-specific thing is to consider reliability across the week. It is not a secret as well that every product has its variability across a week, and a good example here can be some home appliances that are sold significantly better on Friday and during the weekends than during the big days. But this pattern, of course, characterizes the fresh category as well and since goods of this category are supplied frequently. The schedule, the supplier schedule could be on a daily basis or several times a week, and the shelf life is limited. We recommend using this week's variability coefficients to predict demand for the next delivery. This coefficient could be calculated by some statistical methods or by more advanced methods using AI technologies.

Can I ask here regarding the coefficients, so in the case of the replenishment automation solution? Should these coefficients be calculated and passed from the customer, from the retailer, from their account in an ERP system, or should they be calculated directly in the solution itself? Of course, yeah, thank you very much for the question. It's just rather a good question, Ana. So considering such coefficients, the main aim here is to make the forecasting more accurate in terms of demand, of course. But it should really be included in the out ordering algorithm because otherwise, it might be just labor-intensive and generate a lot of errors and mistakes. While people will be just calculating these coefficients and using them in the orders, every product in

every store will have its own specific weekly demands, which depend not only on the specific product but also on its location. I mean the shop location, the store location, and the format of the store. Also, it is worth mentioning here that it is important to consider the time during the day, the particular time during the day when the delivery is made. This allows making that delivery schedule more accurate to take into account the required number of days and even hours in the forecast, and we call these the delivery slots. And it really helps us to make the order even more accurate for our customers. Yeah, because if the orders are delivered in the morning, they still have the day to sell this product. But if the product is delivered during the daytime, it's just like the part of the day during which the sales can happen. Yeah, exactly and exactly.

So, the fourth point is considering the zero balances for the end of the day for the ultra-fresh category. Ultra-fresh category and goods have some interesting specifics, such as in a lot of cases, there is no stock balance by the end of the day. And in practice, it just looks like these goods are ordered, they are received at the location, and they are sold on the same day. But it is very hard to understand if there were enough quantity of products for each particular day to satisfy the customer's demand. First of all, for such goods we should consider the time of day when the last sales were made - according to the receipts. This allows you to understand whether there was enough stock balance until the end of the day and whether the demand for the day was covered. If not, the

order quantity should be increased to have enough stock balance to cover the demand. But this is only relevant if the availability of goods has not been reduced on purpose to minimize write-offs. And the second point is about out of stocks. OOS - is an important indicator for the category, but if the stock balance is Zero each day, it becomes more complicated to calculate it. Therefore, another assumption can be used for a correct calculation: if there was not enough balance at the end of the day to calculate lost sales, it is worth using the statistical ADU.

The fifth point is the increasing part of promotions among the fresh category. We have touched on the topic of the promotions, but still, the forecasting and promo management is already a very difficult process, and taking into account all the above specifics of fresh, it really turns into a very complex, risky, and expensive process with a lot of mistakes. So we recommend first of all use demand forecasting algorithms in promotions that have taken to account every specifical thing of the fresh category, and of course, taking into consideration the cannibalization of demand within the category for the most accurate demand forecasting. The cannibalization is a specific thing, of course, for all SKUs used in the product range, but for fresh SKUs, it is crucial to take into account. The cannibalization across the category.

The second point here is to take into account the actual promotional sales in real-time. And the sooner you start considering reliable statistics for a particular product, in a particular location, in a particular promo, the better it is. And be very, very careful with additional promotional layout stores as additional tools are needed to automatically consider that it is additional equipment, to make sure that there won't be over stocks when the promo will end. And the last but not the least. I think that it is my favorite topic - the ongoing analysis of the bottlenecks in ordering the fresh category. So in this webinar, we will not discuss general KPIs that are relevant for all product categories, such as inventories, sales, lost sales, overstocks, availability, and inventory turnover. We have a separate webinar for

the analysis of those indicators. Today we would like to focus on the indicators that are specific to our topic in the fresh category. So taking into consideration all mentioned specifics, it is important to analyze writes off and their dynamics, as we were talking a lot about writes off. There are bottlenecks caused by a situation when a minimum order quantity is higher than sales for the term of the product's shelf life. So, in this case, each replenishment is bigger than the ability of the location to sell during the shelf life of the item. And the sorting of the bottlenecks when the number of days for delivery is higher than the residual shelf life. In LEAFIO solution, there is a separate block of reports

for analyzing the fresh category. So now we have switched to the system. And for example, this particular report shows specific SKUs used in specific locations. You see the information regarding the name of the SKU, the specific location, and the information regarding the supplier, for which the minimum order quantity. Just a second. For which the minimum order quantity is higher than the average number of sales per shelf life of the item. So you can see that the expected sales and quantity would be like this. The average daily usage is like this. The minimum order quantity is like this. So then, the system also calculates the expected percentage of sales, the supplier's packaging, and predicts writes off of the product based on this logic. So we can

easily see, sorry, we can easily see the expected written off for the future delivery period. The next report is regarding the written-off history that could be sorted, for instance, due to the decrease by SKU. Use at storage locations for the fact writes off in purchase prices or in quantities during the previous week. So going back, sorry, going back to the presentation as they say what gets measured gets managed such as important category, definitely requires precise analysis and control.

Ana, please, could you summarize and give us some key outcomes here. Yeah, sure. Just wanted to thank you for outlining and highlighting this specific and regarding the last one, the analytics. It's one of our favorite parts, but we definitely know that a lot of actually, it's not only about the retailers it's in every industry that people are so like deep dive into the routine work, that they don't have enough time to make the analytics. Like everyone knows that analytics, it's good to make analytics, it's good to analyze, and so on. But no one has time for that, so it's good that you highlighted that. So regarding their outcomes, let's summarize what's been said today. So fresh is one of the most difficult categories. In terms of inventory management like, while managing this category, you should pay separate attention and use the different approaches to each sub-category.

About the data - bad input bad output. So multiple that by ten, and you get the impact of data quality on the quality of inventory management of the fresh category, so that's why it's important to do paperwork and inventory count in time like Helen said, ideally each every day. For tomorrow, for yesterday, it's very important to limit the documents backdating. The next one is. A next specific is a special approach should be used for estimation of the balances at the projected order arrival date, and we recommend to do it by the residual shelf life using the LIFO model. Considering that fresh is a very risky group of products, we recommend applying coefficients of variability across the week because the demand in the week is different,

and it's very important to make the orders like based on this each specific of each daily demand for calculating the statistics and out of stock for an ultra-fresh category. It's important to understand the reason for these zero balances at the end of the day and when these zero balances. They happened because maybe the balance wasn't enough to cover the daily demand. Advanced tools should be used to forecast promotional demand and should be based on reliable, current statistics during the ongoing promotions. And the last, but not the least of course. It's about the ongoing analysis and improvements, and monitoring. They keep your eyes on, they is needed, especially such KPIs as : availability, write-offs, and analysis of various bottlenecks that are specific to this category of products.

Thank you for attending. We will be happy to discuss this topic, specifically for your company, to show how we solve that and how we approach that.