1. Demand is shifting to smaller quantities, more frequent deliveries, and faster response times

The shift in the society towards e-commerce, fast response and fast delivery is everywhere. This presents numerous challenges in optimizing logistics, not only for the last-mile towards retailers and end-consumer, but they also need to focus on internal replenishment and inventory strategies. Logistics is becoming a very substantial factor in maintaining efficiency and service levels, for example when considering direct delivery to consumers via own network or via partners/platforms. This requires advanced technology to optimize all these complex logistic challenges.

2. Sustainability requirements drive manufacturers to rethink about their logistics

Consumers are increasingly demanding sustainable practices from manufacturers. This has various consequences, like the usage of electric vehicles and related techniques, to reduce their CO2-footprint. But also, a focus on reducing plastic and other packaging material to reduce waste, while supporting the returns of used components. This leads to increased complexity, and as a consequence, requires advanced decision-support tooling to meet the sustainability goals of the company.

3. There are huge opportunities to optimize routing and loading simultaneously

The logistics of finished goods for manufacturers is characterized by inter-company replenishment orders (primary transport) and the last-mile delivery to retailers, outlets, and consumers (secondary transport). In both primary and secondary transport, loading and routing are key factors. If you have some flexibility for the moment of delivery, then this brings important benefits to create a ‘perfectly loaded shipment’. Also, an optimizer can compute directly the size of each order, and calculate the optimal routes, while optimizing the loading of each truck, taking a huge number of details into account.

4. You can learn a lot from historical data

Storing and analyzing data from the logistics operations provides new methods to analyze the data. For example, you can compare actuals with planned volumes, to improve the forecasting on product level, and on logistic level: how much capacity and workforce is required to manage the coming period. Additionally, applying machine learning allows for cost assignment to individual tasks, and by bundling commercial and logistics information, you can analyze the P&L per customer, per region, per sales channel, etc. This is very helpful to finetune your proposition and contract negotiations with your customers and logistic service providers.

5. Case BSH: combining routing and loading optimization can easily improve truck utilization by 15%

Suzan Gűreli, head of local distribution operation at BSH Turkey clearly explained how they obtained 15% improvement in truck utilization in their primary transport, while maintaining the same service level agreement, by bundling routing and loading in their logistics operation. As a result of this success, they plan to implement the solution to support their secondary transportation to end consumers. They also want to use the data to improve their capacity planning. This example clearly shows the benefits of implementing an integrated proposition for finished goods logistics.

With these 5 takeaways in mind, you can begin to transform your finished goods logistics and stay ahead of the competition. By leveraging advanced technology and strategic thinking, you can optimize your routing, loading, and more for greater efficiency, sustainability, and profitability.

So why not take the first step today? Contact your local ORTEC representative to learn more about how our advanced solutions can help you optimize your finished goods logistics operations.