I see many packaging companies struggle with orders coming in at the last minute. They find it difficult to make realistic service promises and than live up to those promises throughout the planning and production process. The need to produce ever smaller batches of highly customized boxes while maintaining reasonable levels of stock and work in process makes it hard to schedule and optimize their corrugator and converting lines.

The continuing pressure for shorter lead times results in a growing number of items being managed in a make-to-stock arrangement, which brings the additional challenge of combining make-to-stock with make-to-order competing for time on the same machines. And don’t forget that companies need to further reduce waste and increase efficiencies in this era of extremely fast fulfillment.

There’s room for improvement on the supply side too. For example, data science solutions can thoroughly analyze the product portfolio, shop floor production data, and sales data, in combination with dozens of external indicators. Such analysis can be used to improve the segmentation and prioritization of products and customers, or to decide which products could be better produced in a make-to-stock arrangement.

The kind of analysis offered by forecasting and data science also allows companies to get their long- and medium-term planning off the ground. Indeed, experience in other industries indicates there are a lot of opportunities for paperboard packaging companies to further improve their sales and operations planning (S&OP).

Tactical planning backed by AI can help these companies maximize synergies across the supply chain. Capacities can be planned to consider seasonal effects brought to the surface by data analysis. Predicted shifts in demand can trigger reallocation of production volumes to other sites, or a decision to open a night shift in one location, avoiding costly last-minute decisions on outsourcing or weekend overtime.