Zara recently announced that it would soon allow customers to embroider their names onto denim clothing; it will launch this service in three Zara stores in Amsterdam, Barcelona, and Milan (read this article for more details). For a fast-fashion brand like Zara, the challenge is to fulfill customers’ requests quickly and at minimum cost. In this PoV, we outline the structure of an integrated and intelligent automation system for manufacturers to cater to the mass personalization requirements of digital businesses. The manufacturing industry was an early adopter of automation as it welcomed robotics long ago. Now is the time for the industry to embrace the next frontier of industrial automation by integrating various adjacent technology stacks to enable mass personalization.
Apparel and automotive organizations show the way to mass personalization
Over the years, apparel companies such as Nike, Levi’s, and Adidas and carmakers such as BMW, Ford, and Mercedes-Benz have been offering mass personalization-based differentiated-value propositions to specific customer segments.
For example, Mercedes-Benz has been offering personalized car design (interior, exterior, etc.) for some models. In 2016, it faced challenges manufacturing the increased customization of S-Class cars as the robots were not able to handle so many design variants. At present, it is adopting smart manufacturing techniques across the entire automotive value chain from design through production to sales and service. It also recently announced that AI is a key area for its vehicle development and production. Mercedes-Benz is leveraging machine learning, robotics, cloud, 3D-printing, virtual reality, and other emerging technologies to meet the customers’ increasing demands. In Mercedes-Benz factories, humans and machines are manufacturing customized automobiles hand in hand.
As the number of personalization options such design and color increase, existing siloed IT, operational technology (OT), and artificial intelligence (AI) systems become unsuitable for autonomous decisions and actions across the enterprise and extended ecosystem. Manufacturers can best leverage emerging technologies such as robotic process automation (RPA), AI, simulation and digital twins with AR-VR, and 3D-printing, only when they are integrated and synchronized for digital operation. Beyond just piecemeal technology adoption, digital operations require extensive and end-to-end process re-engineering, accelerating the design of an integrated and intelligent system that automates manufacturing processes with minimum or no human intervention while enabling mass personalization.
Seamless integration of manufacturing processes is the key for intelligent automation systems
HFS defines intelligent automation through our Triple-A-Trifecta, bringing together elements of automation, analytics, and artificial intelligence. These are each powerful in their own right, but together they offer greater functionality and benefit. In the context of the manufacturing sector, enterprises are leveraging these technologies in powerful combinations with other emerging technologies to help buttress and extend their capabilities.
Manufacturers are applying intelligent automation as a decision and action system with minimum human intervention. It can seamlessly integrate the manufacturing value chain from a customer’s request through procurement, shop floor manufacturing, and the delivery of the goods, as shown in Exhibit 1. The system can interact with customers to select a customized design from the available range and automatically check the design feasibility from the engineering and manufacturing points of view. It has other functions, too:
- It further checks inventory stocks and can autonomously create invoices and select suppliers for procurement, constrained by the specification, quality, cost, and timeline of the order.
- The system also interacts with shop-floor manufacturing systems modules for initializing robots for specific instructions.
- Emerging technologies such as machine learning and AI enable the automation system for tasks such as predictive planning, autonomous decision making in uncertain scenarios, and RPA for rules-based systems.
Exhibit 1: Representative intelligent automation workflow for mass personalization in manufacturing
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MBOM: Manufacturing bill of materials |
Source: HFS Research, 2019
Five imperatives for successful implementation of an intelligent automation system for mass personalization
Manufacturing of mass personalized products is not a one-horse race; it involves multiple technologies and multiple vendors across the Triple-A Trifecta spectrum. And, as these technologies do not exist in any off-the-shelf, readily consumable format, manufacturers will need to stitch these technology toolkits together to realize their business objectives. Many are relying on service provider partners to tackle this challenge. Here are several imperatives for ensuring successful implementation of an intelligent automation system for mass personalization:
- Integrate product lifecycle management (PLM), manufacturing execution system (MES), and enterprise resource planning (ERP) systems. Seamless integration among design, operations, and manufacturing is the key to the success of intelligent automation system. Typically PLM, ERP, and MES are associated with engineering, enterprise, and shop floor manufacturing decisions. Integrating these three can accelerate the new product development process.
- Digitize manufacturing operations. Paper-based processes are prevalent in manufacturing operations, particularly in inventory, transportation, and factory floor activities. Enterprises should focus on digitizing the operations for greater visibility, control, cost-of-ownership, and better customer experience.
- Overcome the integration challenge. Since there are different IT and OT technology stacks involved in the intelligent systems, integration and interoperability are big challenges for manufacturers. This is in line with the findings of the State of Intelligent Automation, 2019 study, in which we observed that while over 60% of enterprises leverage multiple intelligent automation technologies, only 11% leverage an integrated approach.
- Leverage big data for decision making. Data-based decisions fuel the efficiency of intelligent systems for supplier selection, planning, and other activities. Since many of the executives still rely on their own assumptions for decision making, manufacturing enterprises must start encouraging data-driven decision making and create a data culture across the organization.
- Deploy human-centric automation. Limited quantity production in a highly automated manufacturing setup, which has almost zero human intervention, creates low production efficiency because of lengthy production initialization and changeover time. Human intervention can mitigate this challenge with semi-automated production systems and co-working with robots in a modular manufacturing setup.
The Bottom Line: Manufacturers must revamp their operating models to pave the way for mass personalization through intelligent automation systems.
Manufacturing enterprises need to revamp their existing operating models to accommodate wide-ranging of customer preferences. Mass personalization demands end-to-end intelligent automation manufacturing systems that provide greater flexibility, speed, and accuracy. Thus intelligent automation systems should look into the process excellence, human-system interaction, and the underlying data-driven logic systems to fulfill the two most critical factors of personalization—time-to-market and cost.