‘Garbage in-garbage out’ is a term often used in computer modelling to express the idea that poor-quality inputs typically lead to poor-quality outputs. Responsible sourcing teams should view the data that they use to evaluate risk in exactly the same way.
Successfully measuring and understanding risk is a nirvana for responsible sourcing managers, which is why many have willingly jumped onto the data bandwagon. However, given the plethora of open and closed sources of data available, determining which data sources are the best or most appropriate can be a serious challenge. You can have the best and brightest responsible sourcing team, but if it is working off suboptimal or inappropriate data the business will fail to derive the full benefit. Equally, top-notch analytics in the hands of teams without data expertise is at best wasted or at worst potentially damaging to the business.
Building a culture of data
The effective evaluation of economic, social, environmental and natural hazard risks throughout the supply chain is critical for business, and requires data that are transparent and up-to-date, and that capture inherent risk with sufficient depth to inform strategic decision-making. It serves all companies well to bring the best global risk analytics into the process early to drive the responsible sourcing journey and mitigate risk through every part of the supply chain to avoid potential reputational and legal pitfalls.
Whatever a business uses risk data for – whether for simple compliance purposes, to assess risks for key commodities, or to provide the backbone for a best-in-class responsible sourcing programme – the aims must align with the trajectory and values of the corporate agenda. In our experience, responsible sourcing teams failing to make clear to the wider business the important influence that data can have will fail to unlock the value that a data-integrated approach is designed to create. This can lead to a range of problems, including the loss of executive support. In other words, they need to effectively instil a culture of data upwards in their organisation.
But to fully embed a culture of data, responsible sourcing teams must go beyond simply making sure that everyone understands how data is being used. Data should be viewed as an integral part of responsible sourcing and a key enabler in helping drive value creation as opposed to simply a ‘nice-to-have’. Data requirements evolve, and responsible sourcing managers need to secure the best resources possible to ensure their programme can effectively keep the business clear of the multitude of risks that supply chains are exposed to. This is one of the major hurdles of trying to implement a beyond-compliance responsible sourcing programme, particularly if the manager in question is looking to gain additional internal buy-in and budget approval. Money can make all the difference when it comes to accessing data analytics.
Identifying the good stuff
All too often, we see responsible sourcing programmes that reveal a lack of true understanding of the underlying drivers of risk. The problem may lie with the analytics, because a lot of risk data available – particularly free, open-source data – tend to be derived only from issues that are easily quantifiable, such as reported human trafficking incidents. In this case, the limitations to the data could cloud the ability of a responsible sourcing team to see the real human rights picture in a country.
Figure 1 below shows all the key indices that make up our Human Rights Index, and demonstrates clearly how general human rights risk is driven by a whole set of sub-issues. As well as recording the number of human trafficking violations for example, our data also measures the laws in place to address human trafficking and the efficacy of the enforcement of those laws. This provides businesses with a much truer – and more actionable – view of the human trafficking risk posed in a country. As the figure demonstrates, the fact that a country is rated medium risk overall when it comes to human rights does not convey the fact that there are areas where that country is categorised as extreme risk (or indeed low risk).
Responsible sourcing teams that ensure the data they use cover the full spectrum of risk, and then study the many and various indicators, will be able to achieve a whole new level of insight, allowing a company to better understand and appropriately design or adjust its responsible sourcing strategy. By being clever when it comes to choosing data sources, and understanding differing methodologies, teams can also ‘future proof’ their responsible sourcing programmes. Sourced correctly, risk-related data can drive and shape strategy. So the key takeaway here is: buy the data you need for tomorrow, not today. As you build insight over time, that’s valuable. Changing the data that you use will ultimately change your view.
Looking into the future
Using predictive analytics to make accurate forecasts about future unknown events represents an inflection point for the responsible sourcing community and will fundamentally enhance a company’s ability to understand and get ahead of the emerging risks within a supply chain.
The potential for predictive analytics to revolutionise auditing, supplier selection and identify likely supply chain disruptions is vast. However, with predictive analytics being touted as the next big thing in the responsible sourcing toolkit, managers must remember that data are only as good as the individuals used to construct them, or indeed the colleagues charged with applying the data to the business.
In search of a data expert
Every responsible sourcing programme needs people with the expertise to source, manage and even generate data. These individuals need to understand the capabilities of the data they are using, fully comprehend what the outputs mean, and ultimately differentiate between good data and bad data – in other words, the ‘garbage’. They will also need to communicate the purpose the data serves to the wider business and be able to address, and at times defend, unavoidable questions from within the team as well as all corners of the business (including legal, compliance and procurement) as to the validity and applicability of that data.
These internal experts must also be able to work with both internal teams and external partners to design new approaches, and look at implementing advanced capabilities such as data mashing – combining, for example, SAQ and audit results with inherent country risk data and predictive analysis. However, as a responsible sourcing team advances its data capabilities and works to combine internal and external data sources, it must assess the construction, robustness and applicability of the external data very carefully upfront. Combining data is a powerful but also complex business.
At the end of the day, data analysis cannot and should not replace human judgement and expertise; instead it should work in tandem with responsible sourcing teams as a pivotal tool in making more targeted and better-informed decisions.