Six pitfalls to avoid when tackling your logistics data strategy

Logistics 4.0 is a data-driven approach to logistics, where organisations collect and manipulate data via a smart technology system that offers complete visibility and control. In taking an approach like this, organisations can better utilize their assets and enhance services, driving both efficiency and maximum customer-value.

The trend has been largely prompted by the rise of the Internet of Things and catalyzed by customer demand for more service-driven experiences. Because of this, plus a whole host of other factors, there’s now even more demand for data across all industries; but no more so than across the logistics industry.

Yet, despite increasing importance, Carousel’s latest white paper Big Data for the Big Picture found that when it comes to Logistics 4.0, we’re still not quite there. Their research reveals that data silos are still apparent within many high-performance organisations and this is a problem preventing many strategies from reaching a much higher level of maturity. Less than one-half (39%) say that current technologies meet customer expectations and in turn, this is impacting on things like customer satisfaction and retention levels.

The good news is, the logistics industry is responding; and already looking at ways to capture and utilize data more effectively. The research also indicates for instance, that service-focused industries are already implementing or planning a Big Data project in the next 12 month, which is also encouraging.

Successful implementation from the outset however is imperative. So, with that in mind, what are the common pitfalls that organisations – looking to embark on a Big Data journey – need to avoid to ensure they’re keeping up with customer demand? Here, Carousel’s newly appointed COO Gerry McDonnell shares his thoughts on the top six things to look out for:

1. Avoid putting Big Data to the bottom of the pile because of a lack of time
A lack of time was revealed as a major barrier in our white paper; in fact, ‘time’ is generally a common response to why most new projects don’t get started. But it’s an answer that I don’t really buy into!

Lack of time is often down to perception, because of course, everyone has the same amount of time. It’s about how you use your time.

One thing a response like ‘lack of time’ could indicate is a lack of strategic buy-in to a data management strategy. If senior leaders aren’t carving out the required time and resource to deliver a change, then it’s obvious that something’s not quite right at a strategic level. It’s clear therefore that for any Big Data project, there needs to be that senior management buy in. It should be a key strategic priority for an organization and therefore managed as such, to ensure important projects like these don’t get delayed. If departments are working in silos or without the support of senior team members it’s a sure-fire way for them to fail, or at least be executed badly.

2. Failing to capture data without a strategy about how to use it
Perhaps an obviously thing to say but a logistics data and a Big Data strategy shouldn’t be separate – they should work to enhance each other, create actionable insights and importantly enhance your overall organization. It isn’t, I’m sure, for one team to manage and analyse, it’s something that should sit at the heart of your whole logistics solution.

We mentioned above that management buy-in is critical. So, if that’s step one, then the second step is defining and agreeing the aim with all stakeholders: What are you trying to achieve? What does success look like?

Some key questions you could ask yourself at this stage are – What are our customers demanding? What questions do we want answering? And what data do we need to answer them? Your strategy can then work backwards from there.

Asking ‘why’ is also a great way to approach this stage. If you can’t answer ‘why’ you’re doing something, that’s a good sign that the data objective is probably pointless!

3. Not investing in the architecture you need to implement your strategy
Seamless integration of your logistics technologies is an absolute must. For many organizations however, our research reveals work needs to be done to update legacy systems before a Big Data strategy can be implemented. Many re using are using legacy, on-premise or multiple systems which whilst a barrier in themselves to getting data into your data solution, they will provide unwelcome barriers to getting the visibility and control that Big Data demands.

It’s commonly understood that moving your technology stack to a cloud-based solution will give you the scalability, security and reliability which will allow you to be more agile as your business needs change.

4. Not focusing on talent recruitment or retention
There is one challenge that appears consistently appear within Carousel’s white paper, and this challenge relates to the availability of talent. A lack of skill was noted in our last paper as not just a barrier for change, but also a ‘ticking time bomb’ for organisations in the future.

27% of respondents attribute poor data management to a skills gap and the risk here is that organizations will miss the valuable insight that can be garnered from their data by not investing in the right talent. Recruitment needs to be a critical part of the overall data management strategy to ensure the maximum value can be brought from such an investment. Without it, the data captured will be largely worthless. You need to be working closely with your people management team to ensure your offering is competitive within the tech and data marketplace

5. Ignoring the insights the data is telling you
This seems obvious, but you’ll be surprised how many organisations don’t take action from the insights their data is telling them.

When you have the right resource in place to understand your data, it’s important to ensure you’re set up to respond to action those insights. That may be through local initiatives or large transformation programs. If you’ve got your ‘aim’ right in step two, then everything you’re capture should drive real value to you and your end customer.

Don’t be overwhelmed by the data, use it.

6. Stop innovating after you’ve launched your strategy
A plan of continuous improvement is so important for organizations, when it comes to both their logistics and data management strategy, because it’s the only way they can safeguard their future. Standing still isn’t an option.

If there was a battle of the business scenarios, agility beats volatility every time. While time poor managers and a lack of in-house skills remain as a constant, the world continues to change – and at an expeditious rate. That means that an upgrade in data tools and skills alone, will not be sufficient. Instead, it’s about being ready and adaptable for the next challenge – whether that’s enhanced customer demands, improved traffic management or the need to run a leaner stock inventory holding – it’s clear that organization change is also required to support with the ‘new normal’. The deployment of both a smart and agile solution is a critical starting point, but by all means, it’s not the end.

If you’d like to know more about Big Data in logistics – specifically, how to accelerate your logistics with a data-driven strategy, why not request a copy of Carousel’s latest white paper Big Data for the Big Picture by emailing findoutmore@carousel.eu.