I interviewed Igor Queiroz who discussed Spare Parts Optimization.
It’s nice to speak with you today, Igor. Today I’m looking forward to hearing your views and experience regarding spare-parts optimization. Before we start, can you provide a brief background of yourself?
Thank you, Dustin, for having me here today. I’m working at Visagio, which is a consulting company, for more than four years as a project manager, leading projects mainly related to supply chain management, such as mining and retail. I’ve been also working on international projects in South America, Europe, Africa, and Russia.
Can you talk about your recent successful project when you working on spare-parts optimization and talk about the main goals you had for this project?
Sure I can. We recently finished three successful developments of spare-parts optimization in Africa. Two projects were in Burkina Faso, and one project was in Kenya. Normally, spare-parts stock-optimization projects, they try to help companies manage their spare parts.
Mainly, questions that companies are willing to have an answer are: Which items should I buy? How much of each item? When should I buy these items? These are questions that iron mind of procurement directors, and they normally have problems because of the high number of items they have to deal with.
An interesting point of such kind of project is the positive impact on companies since it affects both working capital and operational efficiency because spare parts are expensive and they’re also critical to operations. When we normally go to an operation, the common similarity we see is inconsistent database. Databases that companies deal, they’re full of duplications and also missing data.
Also, all the processes related to stock management, they’re not integrated, both with procurement department and end users. Also, there’s no proper methodology to calculate optimized min and max parameters so that they can manage the spare parts in the databases. As a direct consequence of these scenarios and issues I mentioned, companies end up having overstocked items, which is a working capital, and understocked items, causing stock-up defense, which impacts prediction efficiency.
To tackle these kinds of issues and to develop projects in order to solve most of the problems related to spare-parts stock optimization, we have a five-step approach.
1. The first one is: Clean the database. We collect data, we look for the duplications using mathematical models, and try to exclude duplications from the system. Then we go into other databases, trying to retrieve information that’s missing, such as unit prices, part numbers, and categories of items.
2. Once we have a clean database, then we’re ready to start the second part of the work, which is to collect inputs to optimize min and max parameters. Most important of these inputs is consumption and criticality of these items, because, normally, companies, they don’t have this information, or this information is very poor. With this work of assessing consumption criticality , we cannot simply use information that’s already in the system because the information is unreliable, as I told you. We have to map this information with the end users. It’s work that demands a lot of preparation with forms, training end users so they can really provide you with proper and reliable information of consumption criticality, and also monitoring this work. This is one of the most critical parts when you’re developing a spare-parts stock-optimization project.
3. Once you go through the second step, collecting this information from the end users, then comes the third step, which is the optimization process itself, which means that, in this part, we will put together all inputs we collected from end users of consumption and criticality and, together, we input information of lead time, unit cost, order cost, and stock-holding cost. We include this information in an optimization tool that we have developed. This optimization tool, it puts these inputs together and uses statistical models to properly calculate min and max parameters for both high-turnover items and low-turnover items, which are the most complicated ones that just simple mathematical models cannot deal with.
4. After you’ve finished the calculation and optimization processes, then we go through the implementation of new processes, because optimization process, it handles items that they already have in their system and they work with. When there’s a second part which the operation is still running and new items will be purchased, they’ll have to create these items and manage them properly. In order to avoid in the future that they will have such problems as they’re facing now, we have to guarantee that process of creating items, it’s in place in a way that they will be able to calculate proper minimized parameters with correct data so that they can manage them properly in the future.
We designed a new process of item creation and update in a way that we define roles and responsibility between the procurement department and end users. The procurement department is responsible for inputs that they have access to, such as lead time, such as unit cost and they ask, in the process, they receive information from end users criticality of items and expected consumption during the year. Then we apply these new processes—the optimization tool I told you we use to optimize items during the optimization process. We give this tool to the procurement department, to the client, so they can use the tool to help create minimized parameters for the new items that will be created.
The second process we also have to implement is to redefine the process that companies use to reorder items. It seems, many of them, they don’t even have minimized parameters; they have to develop a detailed process so they know exactly how to use minimized parameters once we percolate and give the parameters to them.
5. The fifth step, which I think is the most important, that is going to make sure that companies will be able to manage the spare parts after a project like this, is training. Normally, we conduct around 40 hours of training and a sit operation to guarantee all these new processes that I told you, they were absorbed by the procurement department and that they can run the process without the presence of the consulting team there. By the end of the project, after this training, the procurement team should be able to run processes without help from the consulting team; they will be independent.
As a typical result of a spare-parts optimization project—and these are even results that we had a chance to receive, because there are projects that we finished more than a year ago—there is an expected reduction of working capital of 20 percent in two years. We also see a very fast and short-term reduction of stock in transit. Companies stop buying things that are overstocked, so it’s an immediate impact; stock in transit goes down. We also see an increase of availability they won't run out of these parts. And cost avoidance around $2 million during the process.
Cost avoidance is one of maybe the shortest-term results that you can find in a project like this because once you have the preliminary parameters, you can already challenge purchases that are during the project to try to see if there is any overestimation or underestimation of these purchases. During the project, even without finishing, we are able to prevent the procurement department to purchase wrong.
I think those are the typical results that we find in our stock-optimization projects. That was a brief description of how a project of stock optimization is conducted.
Thank you, Igor, for sharing today. Do you have any final recommendations?
Recommendations. Companies should really try to assess how they are in terms of stock management, basically trying to see if there’s any feeling if they’re overstocked and, at the same time, they see end users complaining that they don’t see the items that they should find in the warehouse. If you are in this situation, if you see this scenario, your company or operation is probably one big candidate for a project like this.
Thanks for sharing today.
Thank you, Dustin.
About Igor Queiroz
Consultant at Visagio