Clearing the route to logistics network optimisation
If modern logistics networks were to be characterised by one word, it would be ‘complexity’. Even the smallest operations can be affected by hundreds of variables — from unexpected shifts in supply and demand, to changing weather conditions, traffic patterns and global pandemics. This is especially true for industries such as food and consumer goods, which typically consist of interconnected production and distribution networks.
These respective networks then get exponentially more complex as they grow, such as when a business gains customers in a new region so larger quantities of products have to be transported to more customers over greater distances. Logistics networks therefore have to evolve to guarantee that as much customer demand as possible is met in the most efficient way.
Designing such networks consists of many different decisions, with the mathematical complexity involved making it too difficult to identify optimal solutions in a timely manner. This can lead to planning inefficiencies that adversely affect the cost, timeliness and environmental impact of deliveries, all having a significant impact on customer satisfaction and long-term business success.
Simply put, logistics networks are just too complex to be managed manually and many businesses lack the tools to uncover optimisations that can have a tangible financial and operational impact.
Efficiency at the speed of business
To increase the effectiveness and efficiency of their logistics networks, modern businesses need access to a solution that can dynamically optimise supply chain operations. It must also be able to adapt to different business objectives, such as meeting increasing customer demand, cutting costs or reducing carbon emissions. As these objectives can change and may exist simultaneously, businesses must look at the network holistically to quickly find the most optimal solution — with speed still being one of the biggest issues facing businesses.
Let’s use the example of a company that operates two manufacturing plants and ten distribution centres, with 200 customers across the UK. In this case, the most important metric is likely to be total mileage (distance) covered by its drivers, as this has a high correlation with other metrics such as cost, duration and environmental impact.
This is where dynamic route planning is essential. By analysing real-time data related to the number of trucks available, inventory levels, traffic conditions and delivery SLAs, businesses can quickly optimise the routes of individual trucks and the network as a whole in order to meet their strategic goals.
From a short-term planning and execution perspective, having access to this level of real-time decision-making is invaluable. Businesses can quickly react to on-the-ground issues by dynamically adjusting routing solutions — all customised to fulfil user-specified objectives.
However, while the use of AI and analytics to enhance tactical activities such as route planning is well established in the supply chain industry, it’s not where the true value lies. Despite recent technological advances, businesses are still taking too long to identify solutions to logistics challenges. As such, the real impact comes when businesses can make decisions at speed, optimise multiple layers and shape long-term network design.
The big picture
While optimising day-to-day activities is important, focusing on higher-level strategic planning is the key to long-term growth and maintaining a competitive advantage. To optimise at a large scale, businesses must build a full picture of their networks to understand where the most significant bottlenecks are and identify the optimum areas for expansion.
They have to take things a step further and consider every aspect of the logistics network — from transport routes and delivery points, to the location and capacities of factories and warehouses — in order to model potential network changes. AI-powered tools can then equip businesses with the data-driven insights needed to drive informed infrastructure, manufacturing and pricing decisions as they grow.
For example, if a company wanted to expand into a new area, it could analyse the impact of adding extra warehouses and distribution centres in select locations. Being able to accurately determine the optimal number and location of facilities to meet customer demand offers a level of strategic planning that many businesses simply don’t currently have access to (except vertically-integrated companies such as Amazon, Tesco and Ocado).
Alternatively, a business might want to find a way to remove a warehouse and still meet its SLAs, go from a single layer of warehouses to multiple layers of warehouses, or replace its land and ocean transport with air freight and actually cut CO2. Once businesses have a full picture of their logistics networks, they can understand the impact of certain decisions and take long-term strategic actions accordingly — providing an invaluable competitive advantage.
Ultimately, when it comes to logistics network optimisation, modern businesses must be prepared to look at the big picture. They have to take things a step beyond tactical efficiencies and leverage advanced technologies to guide strategic network design.
In today’s complex world of supply chains, a whole host of factors can influence the effectiveness, productivity and cost-efficiency of logistics networks. Understanding the impact of those factors and being able to make changes quickly will be essential to business survival over the years to come.
