IoT Data Enables Mass Customization Through Tailoring Over Platform Strategies

Why It Matters

Understanding how IoT data can drive product variety is crucial for businesses aiming for mass customization. This insight guides strategic decisions in product development and supply chain management, impacting market competitiveness and customer satisfaction.

Key Finding

Businesses can achieve greater profitability by offering a range of customized product variations (tailoring) rather than a single flexible platform, especially when leveraging data from connected devices (IoT) to precisely meet consumer demands.

Key Findings

• A tailoring strategy, which involves producing multiple product varieties to meet specific consumer needs, can be more profitable than a platform strategy.
• The profitability of each strategy is dependent on conditions that maximize consumer value, influenced by the availability and use of IoT data.
• The customer can be viewed as the 'completer' of the product, especially in contexts where IoT data enables on-demand customization.

Research Evidence

Aim: Under what conditions is a tailoring strategy more profitable than a platform strategy for a provider in the context of mass customization driven by IoT data?

Method: Mathematical modelling and strategic analysis

Procedure: The study developed a model to compare the profitability of two mass customization strategies (tailoring vs. platform) based on consumer value maximization, considering the implications of Internet of Things (IoT) data.

Context: Supply chain management and mass customization strategies in the context of the Internet of Things (IoT).

Design Principle

Leverage real-time consumer data from connected devices to drive product variety and achieve profitability through tailored mass customization.

How to Apply

When designing a new product line or revamping an existing one, analyze the potential for collecting and utilizing IoT data to offer a spectrum of customized options. Evaluate whether a strategy focused on producing distinct variations or a flexible, adaptable platform best serves your target market and maximizes profit.

Limitations

The model's applicability may vary depending on specific industry characteristics, the maturity of IoT adoption, and the complexity of consumer preferences.

Student Guide (IB Design Technology)

Simple Explanation: Using data from smart devices (like your phone or smart home gadgets) can help companies make lots of different versions of a product that people really want, which is often more profitable than just making one basic product that can be changed a little.

Why This Matters: This research highlights how technology like IoT can fundamentally change how products are designed and manufactured, moving towards highly personalized offerings and impacting business strategy.

Critical Thinking: To what extent does the 'customer as completer' model hold true across different product categories, and what are the design implications for user interfaces and product modularity?

IA-Ready Paragraph: The research by Maull et al. (2020) suggests that leveraging Internet of Things (IoT) data can significantly enhance mass customization strategies. Their findings indicate that a 'tailoring' approach, focusing on producing diverse product varieties to meet specific consumer needs, can be more profitable than a 'platform' strategy that offers a standardized, adaptable base. This is particularly relevant for design projects aiming to capitalize on personalization trends, as it underscores the commercial advantage of designing for variety informed by real-time user data.

Project Tips

• Consider how data from connected products could inform the design of variations.
• Explore the trade-offs between offering a standard platform versus a range of specific product types.

How to Use in IA

• Use this research to justify a strategy that prioritizes product variety informed by user data, especially if your design project involves connected devices or customization.

Examiner Tips

• Demonstrate an understanding of how emerging technologies like IoT can influence strategic design decisions in commercial production.

Independent Variable: Strategy type (tailoring vs. platform), availability and use of IoT data.

Dependent Variable: Provider's profitability, consumer value.

Controlled Variables: Market conditions, production costs (implicitly).

Strengths

• Provides a strategic framework for mass customization in the context of IoT.
• Offers a clear comparison between two distinct business strategies.

Critical Questions

• What are the specific data analytics capabilities required to effectively implement a tailoring strategy?
• How do the costs associated with managing a wider variety of products impact the profitability analysis?

Extended Essay Application

• An Extended Essay could explore the practical implementation challenges of a tailoring strategy for a specific product, analyzing the required supply chain adaptations and design considerations for modularity.

Source

Contextual variety, Internet-of-things and the choice of tailoring over platform : mass customisation strategy in supply chain management · Warwick Manufacturing Group · 2020