Bain & Company released new research showing how paper and packaging companies are responding to a challenging operating environment marked by structural overcapacity, volatile input costs and soft demand across multiple sectors. Drawing on its 2026 Paper & Packaging Report, Bain identifies three imperatives shaping industrial performance: laser focus around capacity decisions, accelerated adoption of AI-enabled efficiency and effectiveness improvements, and stronger commercial execution to protect margins.

Overcapacity remains a chronic challenge for industrial producers. Executives routinely overinvest, assuming future growth that fails to materialize, and many underestimate the long-term cost of persistent oversupply. Bain's analysis shows most companies aim to grow their profits at four times the rate of the market, but only 7% of industrial companies achieve this goal, underscoring how significantly excess capacity can erode margins. The research notes that overcapacity is structural, not temporary, and that companies must understand true relative cash cost per ton vs. competition – grade-by-grade and mill-by-mill – to make effective decisions.

"The industry is operating in a structurally oversupplied environment, and companies that rely on past demand assumptions will continue to face pressure," said Ilkka Leppävuori, leader of Bain & Company's global Packaging subsector. "Leaders are taking a more rigorous view of cost position, portfolio choices, as well as capacity decisions to stay competitive in a market that is unlikely to rebalance quickly."

To overcome overcapacity, leading companies allocate more volume to the most profitable customers, products, and geographies. They consider M&A as an option to expand network coverage and improve the efficiency of their asset base. Additionally, they employ a laser-focused approach to mapping options for each production site, accounting for the system-wide implications of possible closures and conversions.

As companies search for efficiency in a capital-constrained environment, AI-enabled maintenance is emerging as a powerful operational lever. Bain finds that AI makes maintenance predictive and prescriptive, reducing failures, downtime and labor costs. Increasing tool-in-hand time by 15 percentage points and reducing maintenance cost per ton by 17–23% are among the most significant opportunities cited.

The research outlines three pillars of smart maintenance – asset strategy, work productivity and spare parts optimization – along with a four-stage transformation journey: diagnostic, development, ramp-up and scaling. Spare-parts optimization alone can reduce inventory requirements by 20–40%, freeing up working capital. In capital-intensive manufacturing environments, these gains also influence cost per ton.