Production networks are revolutionizing apparel manufacturing by enabling network effects between brands & retailers, factories, vendors & suppliers, and inspection agencies leading to responsible sourcing, increased product quality, & greater accountability enabled by prescriptive insights gained using AI.
The Era of Intelligent Production Networks Is Here
Creating network effects across every supplier, vendor, factory and inspection agency they rely on to produce the highest quality apparel products possible is what’s motivating brands & retailers to create production networks today. By definition, network effects are the positive contributions created by adding another contributor to a broader network or ecosystem developed to deliver products or services.
AI and machine learning are delivering valuable new prescriptive insights into how production networks can improve inspections, excel at responsible sourcing, and keep attaining product quality levels.
Characteristics Of Intelligent Production Networks
The era of intelligent production networks is here, enabled and defined by the following characteristics:
- Cloud platforms designed to scale and support globally-based production networks comprised of multiple nested networks, each of which contributes to and benefits from shared insights and analytics.
- Support for real-time multilateral relationships and customizable workflows enable greater collaboration and knowledge sharing across a production network and its many nested networks, further contributing to greater network effects for everyone.
- Every brand retailer, factory, vendor, supplier, and inspection agency has access to inspection and product quality data, further increasing the accuracy, efficiency, and transparency of every garments’ sourced materials, production and inspection history.
- AI and machine learning are providing prescriptive insights across production networks, making rapid advances in responsible sourcing, increased product quality, & greater accountability attainable for every member of the network.
10 Ways To Improve Production Network Performance
Product quality is the most powerful marketing any brand and retailer has; it’s the lifeblood that enables production networks to grow. Insights gained from AI and machine learning are accentuating that point, providing insights into how compliance, quality management, and responsible sourcing can be turned into core strengths of supply chains and the brands and retailers who rely on them.
Today’s production network decisions define tomorrow’s potential for growth. The more accurate, efficient and transparent the many multilateral relationships are in a production network, the higher the product quality and customer satisfaction with the garments produced. Here are the 10 ways brands and retailers are improving production network performance:
- By capturing and communicating the costs, compliance and quality levels by factory and garment, intelligent production networks provide real-time feedback on performance. With every brand and retailer striving to attain responsible sourcing and higher quality levels at the same time, real-time feedback is fueling a revolution in accountability and transparency.
- Enabling production networks to scale as systems of record by automating data capture in factories is improving lot, garment and material traceability. Responsible sourcing’s foundation is a valid system of record that provides traceability multiple layers deep into the supply chain. The highest performing brands and retailers today are upgrading production networks to improve traceability, making responsible sourcing and quality goals achievable.
- Replacing paper-based inspections with an automated system that relies on mobile technologies reduce the time it takes to complete one by 40% or more, increasing accuracy and shared data across the production network. Automated inspections are one of the most powerful inflection points driving brands and retailers to adopt intelligent production networks today. The data captured from questionnaires and inspections creates Corrective Action/Preventative Action (CAPA) requests every member of a production network can contribute to resolving faster thanks to the increased visibility.
- They’re defining a process of continual improvement and constantly measuring their progress towards customer-centric metrics. Brands and retailers initiate intelligent production networks, enabling them with AI and machine learning to gain insights into how, where and what they can improve. They’re relying on customer-centric metrics to measure their progress from their customers’ perspective. The most successful brands and retailers are obsessed with how they can gain prescriptive insights into all aspects of their operations.
- Brands and retailers are quickly adding AI, machine learning and predictive analytics to their production networks with the goal of creating new, richer data models that scale across the entire ecosystem. One of the roadblocks to accomplishing more with today’s production networks is how many different data models there are storing compliance, quality, and sourcing data. By automating inspections and adding AI, machine learning and predictive analytics, brands and retailers can create new data models that deliver the insights they need to grow.
- Reducing risk by openly providing inspection, site visit, and questionnaire data across the production network to accelerate the network effect further. The highest performing production networks are focused on enabling a network effect that scales quickly to provide every new member valuable data and insight they need to contribute. With every member of a production network sharing information and having it organized in a single, accessible data model, accuracy, accountability, and quality improvement.
- Designing in Voice of the Customer (VoC) metrics and key performance indicators (KPIs) that reflect what end customers think of their quality. The truest test of any intelligent production network is the impact it makes on quality – from the customer’s perspective. Brands and retailers are far removed from the customers they produce clothing and garments for. By enriching production networks with greater insight including VoC metrics, brands and retailers can know if they are delivering what customers need.
- Taking a platform-based approach that can flex and scale to support integration with a wide variety of systems is a must-have in any production network. The greater the integration across legacy manufacturing, supply chain, quality and service systems, the more intelligent a production network is. Add to that the ability to quickly aggregate, analyze and classify data from diverse data sources using AI, machine learning and prescriptive analytics, and it’s clear production networks are at an inflection point where contextual intelligence of every order is possible.
- The highest-performing production networks support every role can speak the lexicon of every key stakeholder they interact with, from brands and retailers to third party inspection companies. The quickest way to know if a production network will be able to scale and compete is to look at how well it supports each of the roles that rely on it. These include brands, retailers, vendors, suppliers, factories and manufacturers, and third party inspection companies. Being able to scale and speak the lexicon of each of these roles is crucial for a production network to attain network effects and capitalize on the massive amount of data captured daily.
- Brands and retailers are relying on intelligent production networks as innovation incubators, new venture hubs and a source of new patents. Combining AI, machine learning and predictive analytics to provide prescriptive insight into how compliance and quality can be improved while incorporating Voice of the Customer insights to track customer satisfaction is the future of apparel manufacturing. Orchestrating all of these technologies together creates valuable new prescriptive insights, enabling brands and retailers to reach new levels of quality while staying in sync with rapidly changing customer preferences and needs.