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This document summarizes a case where Accenture achieved gross profit improvement for a major manufacturing client through the optimization of manufacturing planning. While it was recognized on the ground that "production capacity is 100% utilized," a detailed analysis of manufacturing plans and actual performance revealed that significant waiting times occurred to avoid process conflicts, resulting in one lot being carried over to the following month. Therefore, a feasible manufacturing plan was optimally devised, taking various constraints into account, and applied in a supportive and practical manner. Compared to peak production last year, the operating rates of key equipment improved from 88% to 99%, 81% to 98%, and 80% to 93%. The created production capacity has led to the additional production and expansion of high-margin products, achieving gross profit improvement.

This document summarizes a case where Accenture has entered the development site to provide practical support and improve productivity for clients whose development operations have become strained due to rapid business expansion. With the surge in demand outpacing hiring, and internal systems still burdened by individual dependencies, existing employees faced increased workloads and heightened turnover risks. Accenture teamed up with a partner (Company A) that specializes in hardware structural design, directly handling design tasks such as 2D/3D data creation, analysis, and drawing preparation, as well as project management, while streamlining routine tasks using generative AI and automation tools. They organized the tacit knowledge hidden in parameter design during analysis and automated the setting and checking processes with generative AI, implementing solutions from problem identification to effect creation in a short period. In the future, they aim for Agentic AI to autonomously execute the entire process from design requirement definition to test result analysis.

This document introduces Accenture's indirect material cost reduction program, "Zero Based Spending(ZBS)." While many companies have made progress in managing direct material costs, indirect materials often have purchasing authority dispersed across departments, and the expertise in sourcing (supplier selection and price negotiation) is often individualized, leaving significant room for cost reduction. ZBS is implemented in three stages: initial diagnosis (2-3 weeks), detailed investigation (2-3 months), and execution (3 months or more). In the initial diagnosis, accounting data is fed into ZBS-AI, which classifies millions of lines of transaction data into Accenture's standard categories, visualizing "who is spending, on what, and how much," and identifying cost reduction opportunities using category-specific benchmark data. Subsequently, cost reduction measures are formulated and executed using a set of initiatives. The compensation structure can be chosen between a fixed fee model and a fixed fee plus performance-based compensation model. For categories with low sourcing difficulty, a complete performance-based compensation model can also be implemented through a partner company (Company A).

This document summarizes a case where Accenture advanced the optimization of maintenance costs through the enhancement of equipment management and the review of construction plans at a manufacturing client's factory. While maintenance costs increased due to equipment aging, the construction plans were subjective, leading to issues such as over-maintenance and stagnation in the transfer of technology and knowledge. Accenture verified the validity of the construction plans from a third-party perspective through a second opinion called "Cold Eye Review" conducted by in-house experts. During the pre-review, potential reductions were identified, and the necessity and feasibility of the construction were discussed in review meetings, pushing even carry-over projects to a conclusion. As a result of reviewing all planned repairs and regular inspections, a reduction of approximately 10-15% in the overall construction plan costs is anticipated. Moving forward, we will incorporate primary screening by AI agents to further enhance the accuracy and efficiency of decision-making.

This document summarizes cases where Accenture has utilized AI to improve yield for major manufacturing clients. In manufacturing lines, the variability in raw materials and processing in earlier stages led to unstable outcomes even under the same processing conditions, placing a heavy burden on skilled workers for setting processing conditions. To address this, we developed a system that trains AI on inspection indicators and variety information of the target process, outputting optimal processing condition parameters for each lot using a model that also considers physical equations. This system accommodates varieties with limited data and is being utilized in mass production environments. For advanced varieties that were on the verge of shipment suspension, yield improved from 53% to 100%. After initial implementation in key factories, we are expanding this across all factories while continuously enhancing the model's accuracy by monitoring its effectiveness.

This document introduces Accenture's proposed approach and methodology for "smart manufacturing transformation." The conditions surrounding the manufacturing industry, such as demand, supply chains, geopolitics, workforce, and investment constraints, continue to change significantly. Accenture aims not just to digitize individual factories but to transform management, supply chains, factories, equipment, people, and data as an integrated whole, redesigning the very way of winning. The approach consists of five steps: "Understanding the current situation," which connects the realities of business, supply chains, and factories; "Envisioning the future," which designs where to win in markets and supply chains; "Designing the factory," which translates winning strategies into smart factory requirements; "Making it a reality," which implements concepts through Digital Twin, Physical AI, and Capital Projects; and "Continuously evolving," which adapts to changes through data and operations. We provide seamless support from supply chains to factories, from concept to execution.

This document summarizes a case where Accenture worked with a major materials manufacturing client to shorten the time to market by utilizing digital twins. The client was conducting trial runs on actual machines before mass production, which led to a reliance on specific expertise for verification know-how, dependence on actual machines and trial runs, and verification data confined to specific locations, resulting in prolonged startup times. Accenture provided support focusing on three key areas: "building digital twins," "establishing decision-making processes," and "calculating return on investment (planning and conceptualization)." By conducting pre-mass production operations through simulation and designing verification items and acceptance criteria for pre-mass production trial runs, the process was transformed to allow decision-making to be replicated without experts. As a result, the mass production startup period was shortened from 9 months to 3 months, a reduction of approximately 65%. The expected benefits include a 5.5% increase in total sales per product lifecycle and a 50% cost reduction through operation without actual machines during trial runs.