<p>The transformative shift in production systems, known as Industry 4.0, has redefined the division of work between humans and machines. This study advocates for a human resource-focused approach to future production and manufacturing, highlighting the importance of human learning in adopting new technologies to enhance sustainability, social responsibility, and operational efficiency. A two-echelon (manufacturer–retailer) supply chain model is developed under a two-stage trade credit policy in an inflationary environment. In this model, the manufacturer offers two credit periods to the retailer and incorporates human learning into the production process to improve environmental performance. Retailer demand depends on both the product’s selling price and its green value. The model is examined under three practical scenarios: (1) carbon emission costs are considered to control the supply chain’s carbon footprint, (2) all cost parameters are treated as fuzzy variables to reflect uncertainty, and (3) human learning is integrated within the fuzzy supply chain conditions. A solution algorithm is proposed to determine the optimal profit and decision variables, including production time, cycle length, product green value, and retailer order quantity under varying trade credit conditions. The study’s novelty lies in highlighting the critical role of human behaviour—particularly learning—in ensuring effective and profitable supply chain operations under trade credit and inflation. The concavity of the objective function is established both analytically and graphically. Comparative and sensitivity analyses are conducted to examine the effects of trade credit policies, inflation, and other key supply chain parameters. Numerical results and managerial insights confirm the positive influence of a learning-oriented approach on supply chain performance in an inflationary environment within a coordinated system.</p>