Data in transport and logistics: potential waiting to be realised

20/10/2020 Working faster, making smarter decisions and predicting what’s ahead: data in the transport and logistics sector promises to give companies the competitive advantage but its use is still in relative infancy, according to a new report from Mazars.

According to the study, which includes interviews with C-suite leaders of 16 transport and logistic companies based in the Netherlands with a turnover of up to €500m, the potential of data analysis is yet to be fully realised. To shed light on the opportunities data analysis presents and to know how to use it, we’ve shared our key findings and practical steps for implementation below.

Six key findings

1. The board's vision and leadership are crucial

To be successful in applying data analysis, it is important to have a clear vision and for the management to actively share it, among themselves and the whole organisation. The companies that scored better in our study all had a clear vision and strategy for using data analysis to support internal business operations.

2. Data analysis starts and grows in finance and planning departments

We noticed that finance and planning departments are the ones that often need data analysis the most and are the ones starting projects and initiatives. Tasked with making financial decisions and planning for multiple possible outcomes makes these departments natural homes for data innovation.

3. Investing in data analysis contributes to concrete financial results

Our interviews reveal that investments in data analysis are recovered without it always being immediately apparent. The return on data analysis – much like other areas of soft technology – can take longer to be measured, compared to investments in new hardware or spaces, which can be calculated immediately. As a result, the financial case for investing in data analysis has to be clearly and consistently made.  

4. Investments often start with small, ’pilot’ projects

Many companies start with purchasing a business intelligence solution - in some cases out of necessity because working in Excel spreadsheets, for instance, no longer meets expectations. The deployment of these solutions often starts with small-scale, pilot initiatives.  

5. Mix enthusiastic, experienced team members with young talent

This, according to our findings, considerably increases the chance of support and commitment to new projects. Sometimes the more experienced members of the team struggle to keep up with change, but recently graduated employees tend to master it in a relatively short period of time. Mix their affinity with IT with experienced members to achieve solid buy-in for new ways of working.

6. Data analysis is a way to be attractive to young talent 

It is clear that the application of new technologies is seen as a means to attract young and talented employees. Transport and logistics is not always the most obvious sector for a university leaver, but the offer to work with cutting-edge technologies is a way to become, and remain, an attractive employer.

Practical steps for implementation

1. Understand the possibilities of data analysis and get started

There is a clear responsibility for management here as the importance of data analysis will only increase. The insight that data provides into performance, clients, competitors and (new) employees will reinforce this. Knowledge can be gathered in different ways, including speaking with internal experts, external experts and observing how IT suppliers and clients work. Importantly, don’t think you have to ‘start big’ - but instead begin with pilots and learn from them.

2. Formulate KPIs based on the business vision & strategy: what do you want and need to know?

When formulating and elaborating KPIs, stick to the business as a whole, not just certain departments and business units. Otherwise you risk creating an extensive list that fails to help realise the overall strategy.

3. Find the solution that suits you

Pay attention to the different providers that are out there and the distinguishing features they provide. Carefully consider the criteria that the new solution should meet (for instance functional requirements, maturity, user interface, the possibility of making adjustments, integration into the IT landscape and cost structure, among others.) The clearer the criteria, the easier it becomes to select a suitable solution. If there is no knowledge and experience in this area within the company, seek support from an external, independent IT consultant.

4. Create a flexible and scalable IT landscape

In line with having a clear vision on reporting and analytics, it is important to create an IT landscape in which it’s possible to easily implement a new application. If, for example, you want to switch to another solution, this should be relatively easy to do without major modifications in one or more applications. In these kinds of situations, having a system that can integrate applications with each other helps to make an IT landscape flexible and scalable.

5. Involve employees in data analysis

Introducing data analysis within a company is often accompanied by the introduction of new technology. In many cases, these new technologies also require a different way of working, for instance by working more 'fact-based'. As a result, it is important to involve employees early on in the development of the vision for the application of data analysis and the possible implementation and evaluation of a pilot.

It is important that management actively oversees the vision: there will always be ‘early adopters’ as well as ‘followers’, and management needs to build a coalition that underpins the implementation of technology across all levels of the company. In all cases, good communication is essential.

Interviews included questions on current use of data analysis and business leaders’ ambition for it to better used. The interviews took place in the period prior to Covid-19 and its consequences. We intend to publish an addendum to this report to clarify the consequences for the application of data analysis in the transport and logistics sector.

To find out more about our report, findings, and practical steps for implementation – go here.

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