You are probably eager to get started with BigMile. But there might be parts where you were a little stuck and unsure what to do. That is why we share these frequently asked questions (and their answers) about the use of the BigMile platform with you. For when you don’t quite know what to do, but still want to find out on your own.
You are certainly able to use BigMile when shipping different units. If your data is related to transport, you will automatically be asked which units you want to specify to indicate the amount of cargo when filling in your logistics profile.
Here, you can also select multiple units. In the Excel input file you enter the quantity and the unit on which you wish to calculate each shipment to.
Naturally, we have taken into account the need for some users to express the CO2 footprint per customer. The Excel input file contains a separate field ‘customer’, where you can indicate which shipment you want to see being linked to which customer. This field is very useful in situations where a truck makes a trip with combined shipments for several customers.
There is a possibility the profiles of customers differ from each other. One uses pallets, the other uses roll containers, where the modality is also different. If the logistics profile differs from each other to such an extent that multiple options per question often have to be ticked (such as cargo in pallet, tons, roll containers, etc.), we highly recommend creating a separate logistics profile for each customer. In that case you can simply upload and validate multiple input files. This joint analysis can be done by selecting all these files in the analysis step.
Unfortunately that is not possible, due to the layout of the Excel file that is generated after creating a logistics profile. This file contains a good number of hidden columns that contain useful information about your profile. When an attempt is made to validate an Excel file that does not conform to the correct layout, the BigMile application will give an error message and the process will be aborted.
Good to know: a new Excel file does not always have to be created if you want to analyze new data. Simply go through the logistics profile once and then save the generated file. This way you can always add new information to your file. As long as your logistics profile does not change, you can keep using it.
The ‘assignment number’ is a unique (lane) number for the assignment (or order) of one or more shipments.
A ‘trip number’ is a number given to a particular route which the associated modality must bridge. Several shipments can sometimes be combined in one trip, e.g. if several smaller shipments are grouped into 2 full trucks.
It is therefore quite possible that the same trip number occurs within different assignment numbers.
But if you have trip information and do not necessarily use trip numbers (which means you do not reuse trip numbers either), you simply only use the shipment’s assignment number as input.
An example:
Assignment A for customer AA, assignment B for customer BB, and assignment C for customer CC. All three assignment are 10 pallets. To organize the transport as efficiently as possible, you combine it on 2-1-2020 in 1 truck that will drive with these 30 pallets. The trip number for this route is RR123. In this case there are 3 different assignment numbers, A, B and C, which all three contain the same trip number RR123.
Even when indicated that the data cannot be traced back to day or month, the ‘Date’ field will still be loaded. This is because it is the most neutral name to use. The other ‘period fields’ specifically say something about the fuel consumption of a modality or a location.
Even when an exact date might not be available, it is still important to fill in the date field. In that is the case, simply indicate 01-01 or 31-12 of that year. Please note that the data cannot be correctly analyzed if only the year is added.
An example:
Data is available for 2015, but the shipments cannot be traced back to a specific day or month. In this case, it pays to enter the same date for all shipments (for example: 01-01-2015). That way you will simply get an analysis with information about that specific year, while the dimensions month and year cannot be further analyzed.
There might be several warehouse locations and data available for the whole year. In other words; the available meter readings at the end of the year minus the meter readings at the beginning of the year.
You have also indicated your locations have used green electricity, gray electricity and gas, which energy types can differ per location.
It is possible you cannot allocate your energy consumption to one or more locations. If you indicate this in the logistics profile, the ‘Location energy consumption’ field will no longer be visible in the input file and you only have to enter the total over the period of energy consumption.
If your data allows it, you can enter several periods per location for a comparison of the emissions over time. In this case you fill in the lines per location with the energy consumption per period (e.g. 1 line for the energy consumption of location A in 2018, 1 line for the energy consumption of location A in 2019). Keep in mind that it is necessary for you to be able to split up your shipment information over the same type of periods.
‘Data completeness’ is the percentage of the number of lines of the input file you entered in which the BigMile app has been able to process. You will find your percentage of data completeness in the Validation Report that is created after validating your data.
A data completeness of 50% means that half of the lines from your Excel input file have not been processed in the BigMile app. This can result in a distorted analysis, because certain shipments and / or customers are not included.
Before you review your logistics profile, it is important to know where the incorrectly entered fields can be located. You can use the Excel file called ‘Validation Errors’ to find out, which you can download after validation of your data. The fields marked in red have an incorrect format. Lines with one or more of these red colored fields will not be loaded. The orange marked fields cannot be linked between the different sheets.
To ensure that your data completeness is at the highest possible percentage, we have created a checklist to avoid the most common mistakes:
Checklist before completing the logistics profile:
Are there no blank lines or white cells?
Do the specified postal codes exist?
Are the given place names correct?
Can a shipment be found with data on consumption or kilometers driven?
Checklist during the completion of the logistics profile:
Inconsistent scenario format: In the Shipments tab, column ‘Fuel period’ is filled in with: ‘January’. In the Fuel Consumption tab, column “Fuel Period” is filled in with: “John”. This cannot be linked to each other.
An overview of the most common causes for low data completeness:
When you use 1 modality (for example only trucks) you have chosen “Per lane”. This should actually be “Per leg”. The “Per lane” answer only applies when using multiple modalities per assignment.
The values entered in the coupling columns (these are marked yellow in the logistics profile) are not the same in the different tabs. As a result, the app cannot link this information.
BigMile converts your locations to geo-coordinates. The smaller the area, the more accurate the location data is. The order of most detailed location data is as follows:
In short: If you have both IATA codes and city names available, you will be able to display more detailed information with IATA codes than with the city names. If multiple location types are available in your data, use them to analyze your data as accurately as possible.
Lines with completed, but not found locations are not included in the analysis. This may have a number of causes:
The location information has been entered incorrectly.
An incorrect combination of location data was used.
In some cases the location information has been entered correctly, but cannot (yet) be found in the BigMile location database.
Use the Excel file called ‘Validation Errors’ to find any problems with unrecognized locations. Fields marked in red in this overview have an incorrect format or are not recognized. Lines with one or more of these red colored fields will not be loaded. We therefore recommend adapting this data if possible in the original input file and validate again.
If you are absolutely sure the location information that has been entered is correct, but still not shown in the BigMile analysis, please let us know. Send an email to support@bigmile.eu with the request to add this location(s). The BigMile team will then ensure that the requested location will be recognized as soon as possible.
In most cases, while filling in the Excel input file, a choice must be made from a drop down list in order to guarantee the correct format. If this is not the case, we suggest to have a closer look at the columns ‘Validation’ and ‘Example’ to check if the words are correct.
Any errors in the location values can be traced with the ‘Validation Errors’ report. In this report, the fields with an incorrect format will be indicated in red. Lines with one or more of these red colored fields will not be loaded. We recommend that you adjust this data in your original input file and validate it again.
This KPI provides you the sum of the numbers entered in the ‘Quantity’ column in the Shipments tab of the input file.
Does the KPI deviate from the numbers entered in the input file? Then there are two possible causes:
If only the Shipments tab has to be entered, only the quantities are included that have been entered correctly of the location information of that shipment. More information about this can be found in the FAQ section ‘Locations’.
If multiple tabs have to be entered, only those quantities are included where the location information of the shipment has been entered correctly. The shipments must also be able to be linked with the other tabs. More information about this can be found in the FAQ section ‘Data completeness’.
An example for clarification. Suppose you have this scenario:
You only have to fill in the Shipments tab of the input file.
You have ‘Ton’ as a unit.
You have 5000 tons as total quantity in the input file, but 4000 tons as KPI “total shipped (tons)”.
This means that shipments with a total quantity of 1000 tons cannot be processed in the analysis, possibly due to incorrect entry of location information. To find out which shipments cannot be processed, you can use the Excel file ‘Validation Errors’ that can be downloaded after validation.
This KPI indicates the total amount of CO2 that your company emits and is influenced by, among other things:
The modality you use for your shipments. For example, the emissions from air freight are higher than the emissions from a truck.
The type of fuel that belongs to the modality or warehouse. A liter of diesel has a higher emission than a liter of petrol.
The quantity of the cargo shipped. The higher the amount shipped, the higher the emissions will be.
The kilometers driven. More kilometers traveled leads to higher emissions.
The fuel consumption. The higher the consumption, the higher the emissions will be.
CO2 is the total amount of kilograms of CO2 that has been emitted.
With the number of CO2 per Ton, the CO2 emission is divided by the total shipped cargo in the specified unit (in this case: Ton).
For CO2 per Ton.km (GCD), the CO2 is divided by the number of tonne-kilometers (Unit * as the crow flies).
So you may have an apparently high CO2 per Ton, but a relatively low CO2 per Ton.km (GCD). This is the case if you can allocate these emissions to a large number of kilometers driven.
In the ‘Benchmark in relation to the market’, only companies are shown that achieve a CO2 per Ton between 0 and 100. If your CO2 per Ton falls in this range, it is seen as ‘usual’.
In ‘Benchmark your transport performance’, only the companies are shown that have a CO2 per Ton.km (GCD) between 0 and 0.4.
A number of possible causes:
The logistics profile has not been created correctly. The units (for example cargo) could have also been entered in an inconsistent manner.
Quantities are formulated incorrectly. Example: ‘Ton’ is specified as a unit. Subsequently, the number of ‘Kilos’ is entered in the column ‘Quantity’ on the shipments tab and not Ton. The result is a CO₂ per ton that is 1000 times lower than expected (1 ton = 1000 kg).
The industry in which the user’s company operates differs from the industries used in the benchmark. The results between companies from different industries can vary greatly. This has to do with the cargo unit and the quantities that are shipped. The chosen modality also plays a major role.
The data quality actually tells you how accurately your source data is entered for the analysis. The data quality is determined by the choices you make while answering the questions in the logistics profile and mainly relates to the detailed data of your shipments and fuel consumption.
There are 4 different levels for classifying the quality of your data:
Bronze: The shipment contains data based on an estimate, for example the consumption (km / l) is not known and must therefore be estimated with key figures.
Silver: The shipment contains data based on a sample, for example the consumption (km / l) is an average over a measurement of the number of trucks.
Gold: The shipment contains data based on what actually happened, for example the consumption (km / l) is known per truck per month.
Gold +: The shipment contains data based on what actually happened, for example the consumption (km / l) is known per shipment per truck per trip.
The level of data quality is determined by a percentage. A dataset has the data quality silver, gold or gold + when at least 75% of a certain level occurs. If this 75% is not achieved, the dataset has the data quality bronze. Some situations:
Situation 1: 50% bronze, 50% gold = bronze
Situation 2: 20% bronze, 80% silver = silver
Situation 3: 25% bronze, 50% silver, 25% gold = bronze
In the validation report, to be downloaded after the validation of your data has been completed, you can see how your data has been processed by the BigMile tool.
The better the data quality, the more accurately the BigMile tool can calculate the actual CO2 emissions for you.
The data quality is determined by the level of details in your information. To take your data quality to the next level, you could make sure that more actual details are known, instead of any estimates or samples used.
For example, use the invoices from your fuel supplier to enter the consumption per license plate instead of the total number of liters for the entire fleet. Or request the fuel information from your charters instead of entering it as unknown. You can also try to get more detailed information about your orders from the TMS system, so that each shipment can be included in the analysis instead of an estimate.
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