In this episode, we have our guest Susan Walsh, who discusses how to normalize your product, customer, and vendor master data to avoid planning and forecasting issues with your inventory. She also shares several stories where she has been personally involved in cleaning companies’ financial and operational data and the impact such activities have had on operational efficiency and growth. Finally, she shares the differences between good vs. bad data and how to stop piling up on bad habits of dirty data syndrome.
- [0:18] Intro
- [3:23] Personal journey and current focus
- [4:08] Perspective on growth
- [8:26] Return issues due to the incorrect data
- [10:13] Impact of classification on forecasting
- [12:28] The process of taxonomy or SKU normalization
- [17:15] The data governance process for the ongoing maintenance
- [20:15] The data training process
- [26:43] The implications of having multiple vendors
- [30:42] Closing thoughts
- [31:12] Outro
- Returns of one product could have a knock-on effect on forecasting, because suddenly if people are buying the next size down, you need to be able to forecast to account for all that change in volume. So are you going to lose customers because you don’t have enough stock.
- So instantly, you can save some money by selling to customers. Are you giving them the best customer service because you don’t realize that actually, they’re buying double work from you, they should be getting extra benefits, extra discounts. And if they did have those things, they might buy even more from you.
- You can’t underestimate the power of just purely normalizing your suppliers. And I’m not talking like Parent-Child relationships here like you would in financial. I’m talking just standardizing the names. And that can be so powerful.
- Within procurement, you want to minimize the number of suppliers you have. So you certainly don’t want to be letting everybody set up new vendors, which might end up being all over the place.
Subscribe and Review
Apple | Spotify | Stitcher | Google Podcasts | Deezer | Podcast Addict | Player FM | Castbox
With nearly a decade of experience fixing your dirty data, Susan Walsh is The Classification Guru. She brings clarity and accuracy to data and procurement; helps teams work more effectively and efficiently; and cuts through the jargon to address the issues of dirty data and its consequences in an entertaining and engaging way.
Susan is a specialist in data classification, supplier normalization, taxonomy customization, and data cleansing. She can help your business find cost savings through spend and time management – supporting better, more informed business decisions and has developed a methodology to accurately and efficiently classify, cleanse and check data for errors, which will help prevent costly mistakes and could save days, if not weeks, of laborious cleansing and classifying.
She is passionate about helping you find the value in cleaning your ‘dirty data’ and raises awareness of the consequences of ignoring issues through her blogs, vlogs, webinars, and speaking engagements.
Susan Walsh 0:00
It will have a knock-on effect on other parts of the business where you can save time; you can save costs; you can increase your margins without really having to do very much just by tidying up your data. I mean, that’s the most amazing part of this that cleaning data is highly underrated.
Growing a business requires a holistic approach that extends beyond sales and marketing. This approach needs alignment among people, processes, and technologies. So if you’re a business owner, operations, or finance leader looking to learn growth strategies from your peers and competitors, you’re tuned into the right podcast. Welcome to the WBS podcast, where scalable growth using business systems is our number one priority. Now, here is your host, Sam Gupta.
Sam Gupta 0:54
Hey everyone, welcome back to another episode of the WBS podcast. I’m Sam Gupta, your host, and principal consultant at a digital transformation consulting firm, ElevatIQ.
Remember going to a well-organized library or a store where you didn’t need the endless merry-go-round to find the product or book you want it. That’s the power of clean master data and organized systems for manufacturing and distribution companies, with the never-ending need for balancing control and agility. The task is easier said than done. You might have further challenges if you don’t have systems that minimize the master data entry or experience significant churn with your team.
In today’s episode, we have our guest, Susan Walsh, who discusses how to normalize your product, customer, and vendor master data to avoid planning and forecasting issues with your inventory. She also shares several stories where she has been personally involved in cleaning a company’s financial and operational master data and the impact such activities have had on operational efficiency and growth. Finally, she shares the differences between good versus bad data and how to stop piling up on bad habits of dirty master data syndrome. Let me introduce Susan to you.
Sam Gupta 2:09
With nearly a decade of experience fixing your dirty data, Susan Walsh is the classification guru. She brings clarity and accuracy to data, and procurement helps teamwork more effectively and efficiently and cuts through the jargon to address the issues of dirty master data and its consequences in an entertaining and engaging way. Susan is a specialist in master data classification, supplier normalization, taxonomy, customization, and master data cleansing and can help your business find cost savings through spend and time management supporting better, more informed business decisions.
She has developed a methodology to accurately and efficiently classify, cleanse and check data for errors, which will help prevent costly mistakes and could save days, if not weeks, of laborious cleansing and classifying. Susan is passionate about helping you find the value in cleansing your dirty master data and raises awareness of the consequences of ignoring issues through her blogs, vlogs, webinars, and speaking engagements. With that, let’s get to the conversation. Hey, Susan, welcome to the show.
Susan Walsh 3:21
Thank you for having me.
Sam Gupta 3:23
Of course, it’s my pleasure. Just to kick things off, do you want to start with your personal story and your current focus?
Susan Walsh 3:29
Yeah, sure. So I set my business up almost four years ago when I saw an opportunity to help organizations and save money through the classification of their master data. I had worked for a spend analytics company before and had noticed that while companies were happy to pay for analytics and software, and the real problem was in the quality of the master data. And so, I have spent the last three years just building my business, helping clients save money, save time, save stress, and get projects completed quicker through my normalization and classification services.
Sam Gupta 4:08
Yeah, so I’m definitely going to dig deeper into all of that, that you mentioned, that quality of master data is absolutely critical for any system and the organization. That’s where the problem is, in my opinion. And the second could be people. Those are the two biggest issues in my experience for any system implementation. But before we get there, I’m going to have a question for you. And that is intended for every single guest that we get on this podcast that is going to be your perspective on business growth. What does growth mean to us?
Susan Walsh 4:37
Personally, and it’s not necessarily financial, it could be personal growth, personal development, and for my business, in particular, growth is the growth of my team members, which is a result of financial but I want to spread the growth of my awareness of my business of me and the services that I can provide. And but for larger organizations, they have a bottom line to hit, and they have to be profitable and grow the business and get more customers and be efficient with their members of staff. So it really depends on who you are and what your definition of growth is. I’m sure that if you’ve asked that question, you get lots of different answers.
Sam Gupta 5:20
Always. That’s why this is my favorite question. And I don’t give them any clue. And you know, you can take it the way you want. Sometimes I get from the perspective of sales and marketing. Sometimes I get from the operations and finance. But growth in our experiences is far more than that. It’s a comprehensive experience because your growth can be impacted in a lot of different ways.
So let’s go back to your comment about the quality of master data, right? And I want to capture some of these stories here. So obviously, you do a lot of work from the master data perspective. And I can feel how messy this data could be, just because if you don’t have control, then your master data is going to be messy. If you have too much control, obviously, your employees are not going to be happy. So, there is the balance, right? So tell me some stories where you had these quality problems, and you had to go there and fix them. And then you had some sort of impact. So walk me through that story.
Susan Walsh 6:14
Okay, so the first thing I would say is, I think I said it before, but then the main thing that I help anybody with is solving a problem first and foremost, that sometimes others can’t, then it’s financial and time-saving. Yep. And a client that I worked with, and they sold sportswear all over Europe online, they shipped a lot of product to the UK in Europe, and they were having an issue with a lot of clothing being returned, which costs a lot of money to ship back and then also send out an exchange product or give a refund. So what I did was, first of all, I tried to group their product into clothing areas such as, trousers, tops, belts, that kind of information, then I looked at all the reasons, it was a free text box. So it was very hard to quantify.
Susan Walsh 7:09
So I tried to categorize the reasons that products were coming back. So it tended to be it was too big, it was too small, it didn’t suit them. Or maybe it arrived too late. To categorize all that information. And what we can see from that was the main product, which was significantly higher than all the other products that were being returned, had one main reason that it was coming back. And that was because the people buying it found that it was too big. Yeah. So what I suggested to the client was that they could then put some kind of note on their website, when for people when they were buying this product to see that actually, it’s quite large sized.
So hopefully in that would then start to minimize the number of returns for that product. And it was the same for other products, but there was one that stood out more than any other. And you know, if they could minimize the number of returns on that product, then they would save a lot of money and a lot of time because it’s not just the cost of shipping something back and shipping something else out. You’ve got your hidden costs there. The extra packaging, the warehousing costs, the staff costs to package it and send it out. So you know, all those things add up that you don’t always account for.
Sam Gupta 8:26
Yeah, so interesting perspective there with respect to the size. And obviously, you put the note that is great. But why are people buying this in the first place? So let’s say if I’m buying a shirt or a piece of cloth from a website, I’m going to look at my size that it is probably medium or large or whatever. And I probably know my size when I’m buying as a customer. So why were people buying the size? And why was size so inaccurate that they had to return?
Susan Walsh 8:51
Well, I guess I would assume, and this is an assumption that people were buying the size that they normally bought for other clothing products. Yeah. But this was a sports product. It was jujitsu, or it was a commercial type thing. So yeah, I would assume that they are possibly made slightly larger because you have to move freely in them. I see. So it could have been something like that. And that could have been the reason. But I mean, that’s very speculative.
Sam Gupta 9:16
Interesting. And sometimes it’s very hard to forecast in terms of what people are going to like, and this is the problem in the e-commerce and the distribution space, especially when you’re selling something that is going to be apparel or shoe it just sometimes you know, it’s good to be the same size, but it doesn’t fit the same way that you would like I mean, I have personally experienced this. So I can definitely see this problem in this particular space,
Susan Walsh 9:38
Actually, the problem is discovered that so many returns of that one product could have a knock-on effect on forecasting, because suddenly if people are buying the next size down, you need to be able to forecast to account for all that change in volume. So are you going to lose customers because you don’t have enough stock? Yeah. Are you going to sit on inventory that you don’t need because suddenly you’ve realized that people aren’t buying that size anymore? So you know, there’s a whole range of areas that that one little change could affect.
Sam Gupta 10:13
Yeah, it’s interesting the way you bring this point because your skill classification could have impacted a lot of places that a lot of manufacturers, distributors, and retail companies don’t think about. So the way you categorize your product, the way you classify your product could have an impact on your forecasting. It could have an impact on your inventory. So tell me some stories where you have had the forecasting problem because of incorrect classification of the product.
Susan Walsh 10:44
Well, that’s a huge problem. For a lot of clients, I deal a lot with procurement, so they don’t have visibility on their data, then they’re working blind or going on what people suggest without actually looking at the facts. And but another project that I worked on was a food service company in Germany, and they had a master skill list at head office. But they also had about 500 hubs around Germany. Yeah. And those hubs were creating their own descriptions, their own SKU codes for the same product.
So, for example, you have one can of Coca-Cola or a pack of Coca-Cola. And it could be called different things. And it could have different chords, but it’s actually the same thing. So they had 34,000 SKUs on their system. I managed to rationalize that down to 24,000. And that was all in German as well. I don’t speak German. So I think I did pretty well with that.
Susan Walsh 11:50
That’d be a fun project, I guess
Susan Walsh 11:52
You know what, it was really interesting, actually just learning some new words in German. And more than that, they could then see that actually, like this, this hub is buying their Coca Cola from their friends down the road, but they should be buying it from our preferred supplier. And they’re paying twice as much, yeah, or to buy from their friend as they are from our preferred supplier. So suddenly, they have visibility on what’s actually going on within the business, and they can start to control the purchasing, monitor it, and ultimately save money, make more profit.
Sam Gupta 12:28
Yeah, amazing. And so, how would you recommend anybody who’s starting on this journey? So let’s say if I am the manufacturer or the retail organization, and obviously, I don’t want my employees to be buying from their friends. So I need to start some sort of initiative that I may have three different locations, and I want to give them enough autonomy to be able to buy a product if they are short on that product. Because if they cannot do that, then they are probably going to lose on a lot of different opportunities and the customers.
So let’s say if you were to start on this journey, where you have three different locations, and you want to create a plan where you can have the centralized skill planning so that I’m not selling three different products when I’m actually selling the same product across three locations? How would you recommend an executive who’s going through this journey in terms of creating whether you want to call this taxonomy or the SKU normalization? You tell me what is going to be involved in creating this for me.
Susan Walsh 13:33
So first of all, it’s going to be a really manual process. There is no magic software or a button you can press that will magically fix this problem. You have to really work with the master data, get some humans involved, you start to read the data as you work with it. And you will pick up trends and areas where maybe the information isn’t being input correctly. Yeah. And sometimes, the data can be in separate systems. So I have the ability to pull all those systems together and look at it as a whole. And so, if you’ve got siloed information, you won’t know that you have duplicates. So by pooling all that information together, you get the full picture.
Susan Walsh 14:29
And again, it’s manual, you tidy it up, and then suddenly, you realize that you are purchasing or selling as much of something as you thought because they had different codes, or they have slightly different descriptions. So suddenly, oh, are we going to make more money? Or can we purchase the raw materials in bulk and bigger bulk so that we save more money? And do we need five suppliers for this item? Or could we do it with one or negotiate a better rate.
So there’s a number of different ways, and it really depends on the circumstance. So you might have the master data all in one format and one file, or you might have it separately, and then it’s going through and working out what you need as a business. You’re kind of agree on some standard terminology. So liter is a great example. Is it liter spelled the European way? The US way? Common standard use of all those different weights and measurements? And have them all the same? Because again, that can really and through the number of products that you think you have? Yeah, and that’s how I managed to rationalize so many of those SKUs because they had liters, all kinds of crazy spellings.
Susan Walsh 15:53
Even Coca-Cola, and you know, you think that one, I’ve seen that misspell as well. So I’ve got a check for things like typos and spelling mistakes and cut and paste errors. Yeah. And you’ve just got to methodically go through it and rationalize those down and name the product in a standard way. And then, if you want to categorize them as well, you have to look at them. Do you want one level? Or two levels? Or three levels of categorization? Do you want it to just be food and drink? Do you want it to be food and drink? And then beverages? And then fizzy soft drinks and water and tea and coffee? Do you want to separate it out to that level of detail? Yeah. Or do you just want to know that it’s a beverage?
So it depends on the needs of your business. And but by having that information to hand, you know, like all the other examples, it will have a knock-on effect on other parts of the business where you can save time, you can save costs, you can increase your margins without really having to do very much just by tidying up your master data. I mean, that’s the most amazing part of this. Yeah, and I think that cleaning data is highly underrated as a cost-saving and time-saving tool. And I always say to people. Data is an investment, not a cost. And if you invest in your data, you’ll reap the rewards.
Sam Gupta 17:15
You bring up a very interesting perspective there. Because so when we look from the process perspective, and master data cleaning is not going to be just a one-time exercise, because you are going to be requiring these reports all the time, you are going to be requiring additional units of measures all the time, maybe payment terms, right? Maybe new vendors.
So let’s say if I am going back to my same example, manufacturing, or the retail example, and I have my three locations. And from the process perspective, let’s see if I have the ongoing need for this data. So what will be the process? What would you recommend, from the process perspective, let’s say if my retail location is trying to add one specific skill, or the part or maybe payment term, and they are stuck in adding this. So the process is going to be that you have a centralized authority that controls the whole master data governance, or they can add on their own. And then you can clean this after the fact. So what is the process for the ongoing master data cleaning? Because it’s not the one-time process that you clean it and say and do, it doesn’t work that way.
Susan Walsh 18:17
Because that’s what I’m always saying that you know, it’s you can get me into fixture detail. But if you don’t maintain it, once I’m gone, then I’ll see you again in 12 months because it doesn’t stay the same for very long. And I think what we’re a lot of organizations miss a trick is that we have to target the people who are inputting the master data. They’re the most important people. They’re the people who make or break the data quality. Yeah, and most of the time, they are not at or data people. So we need to talk to them in their language and get them on board in ways that they can relate to they understand. So really intensive training at the start and a manual with us specifically how to this is how we set up products.
Susan Walsh 19:03
This is what you must fill in this is the way you must see liters, or grams or kilometers or meters. If you’re having any problems, when you set up a product like this, you know, there’s a helpline that you can call. Yeah, but if you allow the data input or have too much control over the data, then then you’re going to have real data problems. But if you can put roadblocks in place so for example, as they type in liters the wrong way. Yeah, and it won’t let them or a drop-down menu. However, drop-down menus can have a lot of problems within themselves.
Yeah. And if you minimize the amount of information that they have to put in and make sure they know how they have to put it in and make sure and explain to them the consequences of what happens when they don’t get it right and how that can hold up a supply chain or you know a massive sales order. Some are: we need to order a new product, but the SKUs are not on the system yet. So it holds up so many of their colleagues’ work and their colleagues’ time.
If they understand that, then they might be more thoughtful about what they’re doing when they’re inputting that information. Because I think a lot of the time people think, Oh, well, nobody notices. Nobody’s going to mind if I don’t fill in this box. Yeah. But actually, if you fill in that box, it makes a huge difference.
Sam Gupta 20:25
Yeah. So that’s a very interesting perspective. And again, I want to touch back on the training aspect, right. So you mentioned that everything is dependent upon the people and their training. And let’s say, if we try to train them on everything that they need to do, sometimes it could be 1000, page manual, which might derail people’s interest.
But once they come to the real scenario, in their real life, that’s when they are going to realize that I’m not sure what to do, and they are probably going to make a mistake. And by the way, from the training perspective, it’s not that the people I have today, they could leave, and then I could have somebody else working in the organization. And then they have to go through the same manual again. So how do you minimize this? What can we do to make sure that the process is streamlined, it is not overwhelming for people? What are some of the best practices that you might recommend to a manufacturing executive like me?
Susan Walsh 21:21
Okay, so the first thing I would say is if you have a really thorough, detailed, but easy to follow manual, it doesn’t matter if your staff is replaced, because if it’s a lead, a relatable, usable makes-sense manual, then somebody, anybody should be able to pick it up and follow that process properly. So I think maybe that’s where we kind of the process starts to fall down. And there’s a whole other piece around staff welfare and well-being, and there’s that I’ll never forget this phrase that I learned at university that contented cows produce more milk.
Susan Walsh 22:01
If your workers and your employees are happy, if they feel valued, if they feel that they are contributing, like to in a really important part of the process, then that can help. And you know, I’m not naive, it can, it’s not the most glamorous job in the world. Yeah, but it’s a really important job. And maybe if the organization viewed it as more important than the employees would see is a more important job as well.
Sam Gupta 22:26
Yeah, and this is great. Okay, and let’s talk about the scenario. So let’s say if I have an extremely contented employee that is actually working on the floor, and they might have a customer knocking down their door to be able to ship the product because the customer is mad. And now I’m actually having to go through this 1000 page manual, which might not be updated, let’s say, in the last two months, just because it’s very static information.
Then finally, there are things that are going to change, so somebody may have forgotten to update them. And because of that, now, I cannot perform my tasks. I cannot relate to it. Because of the information that I’m looking for, I have to go through 1000 pages. So this is a real problem. And I am simply trying to close my transaction so that the customer is pleased. And it’s not that I’m not committed or contended, but this is a real-life scenario. Okay. So how would you overcome this?
Susan Walsh 23:18
So I go back to the manual again, right? I do training for a lot of my clients, in-house teams so that they can take over the data process. So the first thing is, there are no bullet points and very little text in my manuals. It’s all pictures, like screenshots on where you need to click what you need to do and speech text bubbles on what to do. So let’s keep it to a minimum, make sure the sections are clearly described and named so that if someone needs to jump to Section x, they can find it quickly, then you’ve got colleagues.
So does someone else within the team know what’s happening here? Can they help you? Yeah, you have a well-trained team that should be knowledgeable to show you. In fact, I would recommend that you don’t have just one person doing that task. If you share the knowledge, then you shouldn’t have an interruption to your supply chain as much because you will always have a backup. And then, maybe at that point, you do have a centralized team that you can call for support as well.
Sam Gupta 24:17
Okay, so let’s say if I have the screenshots in the training manual, and especially if we are talking about the newer SAAS products, they are getting updated, let’s say on a monthly basis, or maybe on a quarterly basis, sometimes on a semi-annually basis. And when the product changes, there are going to be changes in features and functions, and the manual needs to be updated as well. Yeah, but typically what happens is if the information that this person is looking for if the manual is not updated all the time because you are not going to know when this change is going to happen in the product. So how would you make sure that whenever the changes pushed in the software are also reflected in the manual?
Susan Walsh 24:56
If you have a team, you might have a supervisor and or two supervisors. And it may be their collective responsibility to make sure that that manual is updated and maintained. And that’s what I would suggest.
Sam Gupta 25:15
So the next question is going to be to tell me a scenario where you have seen super messy data, and what were the problems that you found. So help me visualize the problems that you have seen, let’s say in the recent project or in the recent five projects that you have done?
Susan Walsh 25:43
Well, I can tell you every single project that I work on is messy data, and but it varies from vendors being misspelled. So there, you have multiple versions of the same vendor. And I have seen terrible and description-free text descriptions with non-descript information. And I have seen at least six ways that you can spell the word screw and its description.
Susan Walsh 26:18
So we’ve got screw screws, SCR WSW, Sc, Ws, STS, and that’s just the screws. There are bearings. And then you know, I could go on about this.
Sam Gupta 26:33
You might be selling 20 products, but in the system, you probably have 1000 products that you’re selling.
Susan Walsh 26:38
Yeah. Yeah. What do you think? Yeah, it’s, it’s more common than you think.
Sam Gupta 26:43
Yeah, I agree. I see that all the time in the ERP world as well. I mean, this is definitely a problem. So tell me, what are the implications of having multiple vendors in business?
Susan Walsh 26:54
Well, first of all, you have no true visibility in your business of what you’re buying or selling to specific vendors. Yes. So you know, and within the procurement side, yeah. And quite often, a great example is Price Waterhouse Coopers, so you can have price space Waterhouse, Coopers PricewaterhouseCoopers. Without the “s” on end, you can have PwC PwC, Inc, PwC. Limited, p dot w dot c, there are so many different versions of that one supplier. So if you’re looking at a global data set, you might suddenly find that if you normalize those suppliers to one PwC Yeah, oh, my goodness, we’re paying 10% more and region eight and region B with PwC.
For the same services, why don’t we have a global contract in place? Yeah. So instantly, you can save some money, again, by selling to customers? Are you giving them the best customer service because you don’t realize that actually, they’re buying double work from you, they should be getting extra benefits, extra discounts? And if they did have those things, they might buy even more from you.
So you can’t underestimate the power of just purely normalizing your suppliers. And I’m not talking like Parent-Child relationships here like you would in financial. I’m talking just standardizing the names. And that can be so powerful. Yeah, for example, I had a client recently. They had 43,000 vendors. Her normalized vendors came down to 34,000. So they had around 10,000 vendor suppliers that were the same, just slightly differently.
Can you imagine the value in that and how much that would SKU the numbers that they were looking at? And, imagine if that’s on, on a manufacturing site, and you’re using Cost of Goods as a measure, and you’re buying an X amount of nuts and bolts to make a product? Yeah, but you hadn’t normalized your suppliers. So actually, you’re using twice as much, but you’re not getting the right discounted price, your cost of goods could come down. Yeah, if you’ve got that visibility to be able to buy smarter.
Sam Gupta 29:11
Yeah, interesting perspective. And I can definitely see how centralizing the customers and vendors are going to be beneficial. So now, let’s talk about one more interesting scenario. And I’m pretty sure you have come across this one as well. For example, let’s say if a customer is a vendor, and that could be probably employee walks if that is the scenario. And let’s see if your system does not allow you to have the customer as a vendor. If that is the case, then what would be the implications in the business process?
Susan Walsh 29:41
Well, that’s definitely going to hold things up. So okay, I mean, I would assume that you would have some kind of new vendor form, and the other thing I would actually check is that are they known by another name. We just talked about Parent-Child, so IBM has a lot of subsidiaries. Let’s check they’re not under the system or something else, and it could be a historical name.
So that company doesn’t even exist anymore. It’s been bought by someone else. But it’s still under the old name or, or it’s under the new name, but nobody knows it by the old name. Such things like that. And that’s something I see a lot, or you know, they just don’t check properly. If you don’t know the name, check by the address, etc.
And then I guess the process for setting up a new supplier because certainly within procurement, you want to minimize the number of suppliers you have. So you don’t want to be setting up, you don’t certainly don’t want to be let letting everybody set up new vendors all over the place. That’s how you end up having to come to me to fix it.
Sam Gupta 30:42
Do you have any last-minute closing thoughts for this conversation?
Susan Walsh 30:51
Yeah, check your data, maintain your data, and see it as an investment. Not a cost.
Sam Gupta 30:58
Yep. Amazing. So my takeaway from this conversation is going to be data is everything. But clean data is more important than that. Yeah. On that note, Susan, I thank you for your time. This has been an insightful and fun conversation. Great. Thanks so much for having me on.
Sam Gupta 31:12
I cannot get enough to come on the show for sharing the knowledge. I always pick up learnings from our guests, and hopefully, you learn something new today. If you want to learn more about Susan, head over to theclassificationguru.com. Links and more information will also be available in the show notes.
If anything in this podcast resonated with you and your business, you might want to check the related episodes, including the interview with Kevin Lawton from the New Warehouse Podcast, who discusses why standardization plays a key role in inventory planning. Also, the interview with Ian Pratt who discusses how to distinguish between the need for additional resources and operational bottlenecks that need to be optimized before investing further.
Also, don’t forget to subscribe and spread the word among folks with similar backgrounds. If you have any questions or comments about the show, please review and rate us on your favorite podcasting platform or DM me on any social channels. I’ll try my best to respond personally and make sure you get help. Thank you, and I hope to catch you on the next episode.
Thank you for listening to another episode of The WBS podcast. Be sure to subscribe on your favorite podcasting platform, so you never miss an episode. For more information on growth strategies for SMBs using ERP and digital transformation, check out our community at wbs.rocks. We’ll see you next time.