Have you ever looked at the possibility of reducing the costs of your logistics network? Did you ever ask yourself if it was optimal? Where the nodes of your network at the right location?
As they are not usually part of the company’s core business, logistics and distribution aspects are often put in the background and happen to be a major cost factor.
Reducing logistics costs is an important lever to improve a company’s competitiveness. Several options can be explored, such as inventory management, warehouse automation or, and this is what we will be focusing on in this article, network design optimization.
Companies can have dozens of warehouses, hundreds of clients and thousands of products. Which network is the best? How can we find the one that minimizes logistic costs? The human brain alone is not up to the task. Such problem requires the use of dedicated software, along with a clear understanding of what we are trying to achieve. In this article, Conseil 2.0 is demonstrating its methodology to tackle these questions.
Supply Chain Guru (SCG) is a tool that allows you to model your logistics network and to optimize it in order to find the best one for your company. The main features of SCG in regard to this are two-fold:
- Modelling of the actual network, or baseline
- Network optimization
Modelling the baseline: a crucial step for every network design project
The more project is close to completion, the more expensive modifications are. This also applies to our case. The baseline must be properly modelled before going any further. If not, future optimization results will not be useful.
The baseline: why and when to build it?
There are three main reasons why you must build a baseline prior to any network optimization project. The goals of the baseline are
- Validate that the model reflects the company’s data
- Are the flows properly modelled?
- Are the computed costs in accordance with company’s financial statements?
- Act as starting point for future optimizations
- Act as a reference point to compare with scenarios
- How are my transport costs evolving? What about my warehouse costs?
- What is the impact on my service level?
A baseline is a way to make sure we are heading in the right direction. It must be built:
- After data collection and validation
- Before building any optimization scenario
Data collection and validation is a step of paramount importance, though often neglected. We usually tend to think that our data is automatically available and ready to be used. However, as we will see further in the article, exporting ERP data, combining Excel files and uploading them in the software is usually not enough.
Depending on the network complexity, we estimate about 3 to 5 weeks to collect and analyze the data, then another 3 to 5 weeks to build the model. If anyone tries to shorten this time, it will most certainly have a negative impact during the optimization phase.
The data to collect can be divided into different categories (the quantity of data sets varies depending on the project complexity), but in most cases we need at least data on:
- Products (type, weight, volume, unit cost, etc.)
- Sites (customer locations, distribution centre locations, production site locations, site sizes, etc.)
- Financial aspects (distribution centre fixes and operating costs, transport costs, customer demand, etc.)
- Inventories (ABC classification, inventory level, safety stock, etc.)
Depending on the project complexity, we could add more data such as the transport modes, the availability of these modes per distribution centres, production capacities, etc.
For every project, C2.0 follows 4 fundamental steps to collect data:
1. Verify that all required data has been gathered
There is usually a massive amount of data to collect, and it is easy to forget parts of it. We track closely the data collection status.
2. Ensure Data Integrity
Before modelling the baseline, it is imperative to analyze the data just to check its quality level. For various reasons (different IT systems, specific needs in some locations, etc.), data coming from different sites will use different naming conventions and formats, show a lack of coherence, contain erroneous information and data could also be missing.
3. Data Cleaning
Depending on the data integrity analysis, we could decide to add data, remove some of it (be careful to anticipate the impact of doing so), or make assumptions to overcome missing bits (that must be documented).
Here are frequent issues we face when cleaning the data:
- Duplication of data
- Inconsistent naming
- Different granularity level between several sets of data
- Multiple sources of information and no single source of truth
- Missing links between internal data, transactional data and invoices
- Confusions between interwarehouse shipments and customer orders
4. Data validation with stakeholders
Stakeholders that will validate the project must be involved right from the start to avoid any surprises. Does the data make sense to them? Are they familiar with the figures? Do they agree with the assumptions that were made?
We cannot stress this enough. Data collection is as long as it is vital for the success of the project. Neglecting this phase would put the entire project in jeopardy.
Fill in the Constraints
SCG will find the optimal model. Therefore, we need to model constraints to reflect reality such as business decisions, specific suppliers flows, fleet limits, the number of hours a driver can drive per day, etc. Constraints can be based on (among others):
- Weight or volume capacities
- Time periods
SCG uses constraint tables so that the user can fill them in.
Build the baseline
You need to start by creating a new database that gathers all data previously collected (from the REP, WMS, reports, invoices, etc.). This historical data (mainly customer orders, shipments, production, sourcing, product characteristics) allows us to create the 6 fundamental elements to model the baseline:
- Policies on
Sourcing and inventory policies can be left out if the goal is to model only outbound shipments from distribution centres to customers. Also, if production is key in your network, production policies can be modelled as well.
It is important to select the right KPIs, that must be done at the same time as data collection. You must verify that the modelled baseline complies with the reality of the company. For example, we know that warehouse XYZ ships 97,000 lb of merchandise for the entire period of the exercise. The model indicates that it ships 98,000.
- Is this gap acceptable?
- What is our tolerance threshold?
- How much does this gap represent versus the throughput of other warehouses? Can it be neglected?
- What is the required effort to improve the baseline?
The model will never be 100% accurate. The question we need to answer is when can we consider the model good enough? This question is specific to each project and must answer case by case.
During the model validation phase, C2.0 works closely with the client to ensure that both parties are satisfied with the results so we can safely validate the first big milestone of the project.
The road to optimization
Now that we built the baseline and that it has been validated by the stakeholders, we look at optimizing the baseline and run scenarios such as adding or removing a warehouse, set up a cross-dock facility, consolidate flows, etc.
If the baseline is properly modelled, creating the scenarios is not the hardest part. The teams can therefore spend more time analyzing the results and maximize their time spend on tasks with added value.
C2.0 is an official partner of Coupa. Do not hesitate to reach out to us if you wish to learn more about Supply Chain Guru or for any project about network optimization, we will gladly accompany you in your upcoming challenges.