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Redefining Lean Product Development Principle in the world of AI/ML

By Prashant Chouhan posted 08-25-2021 17:43

  
PDMA Body of Knowledge: Strategy Insights #3
Read time: 5 minutes

Lean principle in Manufacturing is a well-known management philosophy over last many decades that primarily focuses on eliminating “Waste” in the system and achieving “process efficiency”. Lean principle is also well accepted across different business functions and processes beyond manufacturing production systems. Lean principle in product development is calibrated with flow of ‘design’ v/s flow of ‘material’ with “Waste” being contextually synonymous to multiple hand offs, re-work, excess adoption of features, late change, or approval etc.

With the advent of Industry 4.0, Value Chain and business processes are significantly influenced by digital technologies. Automation and AI/ML have become the key capabilities in the organization to get intelligence and agility in the processes beyond efficiency and economy.

There is difference in opinion in the industry on whether lean and automation can go together.(One good article on this subject is Lean vs Automation) Few opine lean as the first step before embarking on automation, while others suspect that AI & Automation and lean are naturally at odds  because it may allow organizations to embrace digital automation before making them lean.

During the current pandemic, we have witnessed significant volatility in the business environment. This has also taught us that the Value Chain should not be just lean & efficient but also be agile & flexible to adapt to fast changes in the market. TCS defines this future enterprise capability set as ‘Neural Manufacturing’, where the Value Chains will be responsive, adaptive, and resilient, and Automation, intelligence, and Connectedness are the key traits that bring forth such capabilities to both the enterprise and their ecosystem partners.

In light of the ongoing business challenges and trends in Industry 4.0, it is important to relook at the ‘Lean principle’ through the lens of Neural Manufacturing, rather than superimposing a fixed structure of Lean methodology into digital savvy industries.

Lean philosophy is based on 5 Key principles. Let us review them in the context of Product Development with possibilities of AI/ML and Automation.


  1. Define Value: This is core of any enterprise and definition of value has not changed but the form might have evolved. Specially in Product development, customer is now looking for early visibility in value, greater collaboration, and new mode of business. Disruptive digital technologies have enabled all such possibilities.
  2. Map Value Stream: This is a foundational exercise for establishing a Lean Enterprise. In Lean Product Development, Value Stream is defined from the Product development value chain to the process steps with focus of information and knowledge flow with capturing critical variables in terms of cycle time, delays, non-value-added activities, decision points etc. Since most of the process elements are nowadays handled in enterprise systems, Process mining tools are gaining importance in diagnosis of critical variables of Value stream. But overarching view of Value Stream map still holds relevance to capture the entire ecosystem view which is not possible through process or task mining tools as post facto effect. Fundamental construct of Value Stream also provides opportunities to identify automation & AI/ML opportunity as a critical variable to measure against each task or sub task.
  3. Create Flow: Continuous and smooth flow of information after removing waste defines the target flow. It is also important to Create Scenarios for given flow, simulating these scenarios while identifying optimal path is the critical need for today’s volatile business environment. Target flow should account for ecosystem play and should be designed to bring responsiveness and adaptability with network intelligence and automation.
  4. Establish Pull: Customer should be able to pull the work from the system as per need. To present it in the current context, pull is not always reactive but should also be predictive with the intelligence established in the enterprise.
  5. Pursue Perfection: This is the final step to achieve perfection with continuous improvements, and it is not only about achieving perfection on repetitive tasks but also about achieving insight & intelligence across the value delivery process.
Redefinition-of-Lean-Principle.jpg
                 Figure 1: Redefinition of Lean Principle

With above discussions, if I attempt to summarize the 5 steps of Lean Product Development Principles in a digital world, these would be

  1. Define the Value Chain: Defining the value chain with the complete play of eco system partners.
  2. Assess the Value Chain: Analyzing the Value stream map with redefined critical variables, conducting process mining & process simulation and, analyzing the data & system
  3. Identify the interventions: Identify not only the non-value-added activities, defects, waste etc., but also identify the machine intervention possibilities for intelligence and Automation in each value delivery step.
  4. Establish Intelligent Enterprise: Establish pull with Re-engineer and Automation of the process, also establish intelligence that can provide insights and prediction.
  5. Pursue Excellence: And finally, not only “Pursue Perfection” but also “Pursue Intelligence” with “Continuous Improvements “and “Continuous Learning in AI/ Automation Model” respectively.

In today’s Digital World where products are smarter, hyper personalized and having shorter life span, contextualizing Lean Principle with Intelligent Automation will invigorate Agility, Speed and Economy in Product Development.


About the Author


Prashant Chouhan

Prashant Chouhan heads the New Product Innovation and Lifecycle Process business practice for the Europe Manufacturing Innovation & Transformation Group (ITG) at Tata Consultancy Services. He has 19 years of Industry experience in Product Design & Development and has worked on business consulting and advisory services in Digital Transformation for Product Development and Product Life Cycle Management (PLM) for global manufacturing customers. He holds a Master’s degree in Manufacturing Management from IIM Calcutta and Bachelor’s degree in Mechanical Engineering.


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