Dieser Artikel beschreibt die Herausforderungen, die bei der Planung von Zementlieferungen vom Werk ins Depot und zum Kunden, zu meistern sind.
Dieser Artikel ist ausschließlich in englischer Sprache erhältlich.
In the daily race for competitiveness, cement producers can learn a lot from the data driven decision making in today's motorsport. It is not just about how fast your car or how good your team is. The right planning strategy and real-time data insights can helpyou to cross the finish line first.
Planning the supply of cement from plants to storage depots and customer sites can be quite challenging. Planners and Dispatchers face a high variability of demand,heavy onsite traffic during peak hours, limited availability of trucks, driving time regulation limits and many other restrictions. At the same time, they must try to find the best balance between customer satisfaction and transport costs. If just one item in the mix fails, the entire plan may collapse like a house of cards – with shipments running late and costs way out of line. And to make things worse, deliveries from plants are often scheduled independently on a plant by plant basis to serve the demand within a plant’s region only.
Conventional scheduling software
Conventional scheduling software packages can be a practical tool for the daily routine. They automatically copy order data from the ERP system (Enterprise Resource Planning), provide user-friendly graphical interfaces to help assign trucks to loading points, calculate schedules and turnaround times, or may even visualise routes on maps. But with a larger truck fleet on the road, numerous plants and depots, and delivery sites spread across a wider region, huge data volumes have to be handled in order to find a set of good schedules. Business rules (e.g. preferred service) and site constraints like product availability at a specific loading point add even more variables to the calculation. The human brain is not up to this sort of challenge. Even experienced schedulers and dispatchers are stretched to their limits if they only work with software that does not have its own intelligence.
Figure 1: Intelligent transport planning with real-time optimisation.
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