Developing Data Tools for Logistics and Supply Chain

Mathematical optimisation has been used to solve decision problems in logistics and supply chain for many years. As data have become abundant in many industries, so have the opportunities for optimising and automating operations. Meanwhile, harvesting the benefit from the large amounts of data requires advanced mathematical tools. Without the right tools, operators may well end up drowning in numbers instead.

A hierarchy of optimisation

In the Logistics and Supply Chain department, we use in-house expertise in the fields of optimisation, statistics and computing to develop data tools for logistics and supply chain. To drive the development of our optimisation services, we classify data tools according to their level of decision automation. Our ambition is to develop tools enabling customers to transition from data-based decision support to full decision automation. To that end, we distinguish between three types of decision tools: modelling, planning and control.

Modelling

We use the term modelling to refer to descriptive mathematical methods, which can give insights into the workings of a given system such as a production system. From an operational perspective, models can be used to test scenarios and conduct what-if analyses in order to identify bottlenecks or estimate capacity. Such models can provide decision support, but they cannot guarantee to find the best – or optimal – solutions.

Planning

Planning, on the other hand, refers to mathematical methods that guarantee to find optimal solutions with regard to some target or operational objective. These methods are prescriptive in nature since they specify which decisions an operator should make to optimise operations. In logistics and supply chain, planning tools can be used, for instance, to find the optimal location for a number of production facilities that minimises transportation costs. Meanwhile, planning methods are typically static and do not adapt to changes in the operational environment.

Control

In contrast, control refers to optimisation tools, that continually register changes in the environment and adapt operations based on available data. Such tools make optimal decisions in real-time, based on the current situation and predictions about the future. Using the proper optimisation tools, the decision process can be fully automated. Inventory management is a field that can benefit from automated control. The right data tools can for instance automatically determine the best order quantity at the right time based on incoming data and minimise long-term inventory costs while reducing the risk of stockout.

Data tools to enable business change

In NIRAS, we believe that the development of digital tools should be driven by customer benefits. This is why we focus on the development of optimisation tools for decision automation, which we see as the major enabler for business changes in logistics and supply chain. With the development of advanced optimisation tools, we can offer customers clear and quantifiable benefits in terms of lower operating costs, reduced emissions, increased reliability and controlled risks. Furthermore, decision automation plays well with many developing technologies in supply chain and logistics including IoT, big data, digital twin technologies etc.

Automation is an interdisciplinary endeavour

As an engineering consultancy firm, NIRAS has a competitive advantage in terms of developing optimisation tools for automation. NIRAS has domain experts from every conceivable engineering discipline in house, which makes it possible to adopt an interdisciplinary approach. To benefit from this, we are tightening bonds with other specialists across departments through knowledge-sharing and project collaboration. Developing data tools for automation is a priority in NIRAS, and we believe that advanced automation is key to staying ahead in the logistics and supply chain industry and to realising our customers’ sustainable potential.