Traceability has many driving factors. It is often discussed in relation to sustainability – helping organisations understand the impact of their initiatives and meet consumer demand for ethically-sourced products. But traceability can also be an operational advantage to your organisation. It allows you to see your supply chain in more detail, better understand your counterparties and identify bottlenecks, manage risk, and satisfy the requirements of external stakeholders including legislators and trade finance partners.
Many commodity organisations are therefore investing in a variety of projects to improve traceability within their supply chains. In part one, we discussed why any commodity traceability project should start with an internal data audit. Today, we take the next step in this process, asking how you will manage the nuances of getting new data into your system.
Validity and verification
Checking data validity is the first step in any new data import and an important consideration when looking at new data sources. This includes ensuring the data is in a format that can be read, shared, and analysed, as well as confirming that the data itself is correct. Most systems include some automated validity checks – we have probably all seen an error when copying percentages, dates, and times into Excel, or been frustrated when a webform wouldn’t accept special characters or values that are out of range. But there is good reason for these data formats, and for why you need to keep each piece of information in its own field – so that you can accurately process and report on it. User annoyance is often because the system has not been set up with intuitive data formats – an important point to consider when defining them. The validity check is therefore an important stage in ensuring the data is in the format your system is expecting so that it can be processed without creating errors. Different commodity management systems will have different ways for you to bring external data into the system, and some provide better validation than others. For example, if your people are copying and pasting, or typing information between documents and systems or spreadsheets, validation is lost. The only way to then ensure that the data logged matches the data you received is to have somebody else manually check it, which still does not remove the risk of errors, to say nothing of being tedious.Uploading commodity data
Another option is to upload Excel files directly into the system. This ensures that the data you are using exactly matches the data you received, and can allow you to process much more data than would be practical otherwise. These uploads can take different forms in different commodity management systems – if they are available at all. At Gen10 we map the client’s file uploads to a template so that when they receive data from a counterparty, they simply select the correct template, and the data is uploaded to the correct fields for the relevant contract and virtual lot. Data validation is automatic – CommOS will stop the upload and notify the user if the file does not match the template’s data fields.Discover how Gen10 use this data upload approach to manage complex HVI requirements in cotton trading.