Scale your transformation with industrial IoT solutions

What good looks like:

• Embedded process optimization: Processes are designed to be continuously efficient despite varying operational conditions.
• Continuous manufacturing innovation: Cutting-edge manufacturing technologies and systems are integrated in production and process designs.
• Streamlined workflows: Work systems are streamlined and collaborative to increase productivity.
• Customer-centric manufacturing: Production operations are designed to continually meet customer needs.

What good looks like:

• Scalable manufacturing: Automated physical and process systems enable rapid scaling and downsizing of operations based on market demand.
• Collaborative robotics: Robotic systems are seamlessly embedded in manufacturing processes and work systems.
• Overall process optimization: Automated tasks drive end-to-end operational and cost efficiency.
• Consistency and reliability: Automated systems consistently deliver high-quality outputs in all operating conditions.

What good looks like:

• Seamless communication: Advanced collaboration tools enable constant communication and alignment across internal departments and suppliers.
• Integrated operations: All operations from raw materials receiving to production and shipping flow continuously.
• Automated scheduling: Production tasks are optimally assigned based on real-time external (demand/supply) and internal (production) data.
• Synchronized value chain: Materials flow in a continuously optimized manner from extraction to usage and end-of-life management.

What good looks like:

• Workforce modernization: Reimagine manufacturing work, workforce, and workplace with optimized human: technology collaboration.
• Organizational strategy and design: Structure the right governance and blend of resources to deliver value.
• Workforce and talent: Define talent strategies and skills using human-centered design.
• Change and learning: Embed change management and training to sustain transformation adoption.
• Responsible supply chain: Advance climate equity and human rights to improve worker safety.

What good looks like:

• Expansive operational scope: Execution excellence is sustained across different product types and operating conditions.
• Data-driven decision-making: Strategic and operational decisions are driven by widely available data analytics.
• Bottom-up optimization: Operators are empowered with tools to help them identify optimized methods to carry out daily tasks.
• Sustained engagement: Employees exhibit a culture of execution excellence in their daily work.

What good looks like:

• Instant intelligence: On-demand access to real-time asset condition and performance across entire network.
• Proactive maintenance: Maintenance processes and procedures are always conducted before performance issues arise.
• Skilled workforce: Maintenance and production staff are always capable of properly handling and maintaining their assets.
• Continuous compliance: Asset performance conditions and maintenance procedures continuously adhere with regulatory and safety regulations.

What good looks like:

• Close-loop detection and containment: Quality is continuously monitored with closed-loop method for containment.
• Upstream prevention: Quality failures are prevented through product and process design for quality; ownership of quality is upstream.
• In-process sensing and prediction: Analytics and in-process technology is used to predict cause-and-failure-mode occurrence before the effect occurs.
• Customer-driven quality: Quality requirements and processes are designed to meet customer needs.

What good looks like:

• Safety by design: Production processes and procedures seamlessly integrate safety into day-to-day work.
• Carbon footprint intelligence: Analytics on energy consumption, carbon emissions, and environmental interaction are readily available.
• Operations-viable sustainability: Sustainable practices are efficiently embedded in operations and aligned with business objectives.
• Zero-incident manufacturing: Safety incidents are anticipated and prevented using digital technology and advanced analytics.

Impact

• Lower operating costs and less technology debt
• Clear strategy to scale IT/OT convergence
• Agility to respond to changing manufacturing needs and business objectives
• Greater access to top talent

Impact

• Rapidly activate new digital ventures with access to top talent
• Immediately access technology resources without having to recruit
• Adopt industry-leading processes
• Harness data to drive valuable insights
  

Impact

• Integrate technology into daily operations
• Quantify business outcomes with actionable insights, KPIs, dashboards
• Optimize workforce, continuous improvement capabilities
  

Case study

A metal manufacturer struggling with low productivity needed to increase output to keep up with demand. See how AI, machine learning, and automation formed an innovative trifecta while driving workforce adoption.

Client background

A multinational metal manufacturer needed to increase output within its current footprint but struggled with capacity constraints exacerbated by increased demand. Vibration spikes affected product quality and machine integrity, causing operators to run the line below the target speed, ultimately reducing productivity.

Solution

Deloitte partnered with the client to use AI, machine learning, and automation to predict when vibration would occur and automatically adjust the speed, decreasing downtime, reducing scrap, and minimizing potential damage. Our innovative approach was deployed within the client's existing system and quickly achieved workforce adoption. Our Smart Manufacturing team:

Harnessed AI: Deployed an AI-driven predictive model into the machine to anticipate vibration spikes and adjust speed without manual intervention. Using real-time inputs from sensors, the control system, and product data, the model delivers speed changes and alerts almost immediately.

Created interchangeable model: Designed the solution to address the vibration issue using a modular architecture capable of sending and receiving data to an edge model in near real time to address other factory challenges. Providing such flexibility in the model enables other factory solutions as well as the ability to scale to other plants.

Integrated with plant systems: Provided analytics and reporting capabilities that allow operators to view key drivers of predicted vibration, perform root cause analysis and corrective action, track overall solution impact, and monitor performance.

Impact

100%

adoption rate in first month of operation

$6.5M

in savings annually*

2.5%

improvement in productivity loss*

4%

cycle time reduction*

*Last three metrics projected over the first year.

Case study

Mid-modernization, a food processing company needed to shift gears and deploy a new enterprise resource planning system. See how our smart manufacturing team found synergies between the two projects to deliver value in months, rather than years.

Client background

A multinational food processing company began its smart manufacturing journey to modernize operations but needed to quickly convert a quarter of its business to a single enterprise resource planning system. By implementing SAP in conjunction with the multiyear smart manufacturing transformation, the client realized value faster than a typical SAP deployment—in months versus years.

Solution

The client was using multiple homegrown systems—some more than 30 years old—that created unnecessary financial, compliance, and operational complexities. Deloitte found synergies within the ongoing smart manufacturing journey to implement SAP S4/HANA simultaneously to address the client's inventory management challenges. Our Smart Manufacturing team:

Complemented SAP with advanced analytics: We integrated AI-enabled forecasting and production technology with the inventory management tool to optimize scheduling and decrease manual operations. This streamlined processes for the end user, improving inventory, scheduling, and adherence to key performance indicators.

Collaborated with the shop floor: We deployed options for the end users to test and provide real-time feedback, quickly adjusting the solutions based on the workers' experience. We produced rapid iterations for users who knew the systems best, enabling the solution to address the unique processes and technology of the factory and avoid limitations of the current system.

Customized solutions: We developed options for each plant based on specific requirements of the unique end users instead of implementing a “one size fits all” approach. Our plant floor experience and industry best practices enabled us to deploy rapid and adaptable solutions.

Impact

58

legacy systems replaced with one streamlined platform

35%

reduction in system-related manual processing

15

new analytics products with real-time data

Case study

With little visibility into production and disparate data management, a cabinet manufacturer had no way of seeing the big picture of its organization—until a smart manufacturing pod tapped the right people to create value.

Client background

A multinational cabinet manufacturer struggled with production metric visibility across its network. Multiple facilities missed operational targets, which negatively affected the end-to-end supply chain. Data was managed through a series of emails, spreadsheets, and other software. With limited visibility to plant-specific production metrics, senior leadership lacked understanding of the status of the business, faced operational blind spots, and required critical talent to close the gaps.

Smart manufacturing pod delivery model

Deloitte delivered a manufacturing value pod with manufacturing and technical specialists who partnered with the client to rapidly develop and deploy data visualization tools and improve the organization's metrics management. The pod collaborated with the client's team to quickly create high-ROI data visualizations, ensuring client leaders were aligned on the program and supported implementation of processes needed to derive value from new insights.

The manufacturing value pod model was on demand and scalable to support evolving client needs and provide the right skill sets at the right times. The pod's laser focus on value creation outpaced improvement costs, resulting in a self-funded model for improvements, proving the pod's value in under eight weeks.

Impact

Expected to scale and save $7 million by Q4 2024, $12 million by Q2 2025

Consolidated disparate forecasting and budgeting data from 16 sources to one

Common plant metric visibility from executive to plant- and operator-level leaders

Expanded reporting at the executive level from one site to the entire company and at the operational level from one to two sites

Case study

Underperforming machining processes put a leading aerospace company in a tough spot, losing over $20M annually in scrap and straining customer relationships. See how a scalable smart statistical process control (SPC) solution transformed machine data to reduce scrap and optimize processes.

Client background

A leading aerospace company faced significant challenges achieving through put targets and controlling cost of quality for precision machining and manufacturing processes. Underperforming machining processes led to parts falling outside of specification tolerances, thus failing inspections and requiring rework. The client was losing more than $20 million annually in scrap material and had a growing backlog after suffering several escape events, resulting in significant financial losses and strained customer relationships.

Solution

Deloitte partnered with the client to develop and implement a smart statistical process control (SPC) solution that collected data from different machine types, transformed the data into an extensible data model, and utilized advanced statistical models to monitor and evaluate the machining process. Smart SPC's cutting-edge technology could identify specific time intervals when the process and characteristics began to deviate and suggested programming improvements to reduce scrap and quality escape events. By creating the foundation for monitoring machine utilization consistently across lines with different production types, the client could scale across multiple sites.

People: Equipped the workforce with real-time data and actionable insights from Smart SPC to empower them to make informed decisions, increase utilization, and troubleshoot quality issues. Supported adoption and scaling by training more than 600 workers and providing the client with critical tools and processes to train hundreds more.

Process: Networked machines to constantly monitor and send real-time data to a cloud environment, enabling advanced statistical models to evaluate the machining process and identify necessary improvements.

Technology: Leveraged SPC and predictive models and technology including machine sensors, cloud computing, and the edge, to identify and rectify deviations in the manufacturing process in real time.

Impact

20,000

labor hours saved annually

$2.25

Mscrap avoidance saved per site annually

90%

reduction in annual root cause analysis

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