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Home Insights Omnichannel Commerce Smart manufacturing from the factory floor to
the customer's door


SMART MANUFACTURING FROM THE FACTORY FLOOR TO THE CUSTOMER'S DOOR

Kate Tsurkan

Sep 22, 2022 • 6 min read


AI ACROSS THE MANUFACTURING VALUE CHAIN

Manufacturers are embracing AI technology to gain a competitive advantage over
the market. Gartner predicts that by 2026, 75% of large enterprises will have
adopted some form of robotics in their warehouse operations. Additionally, 25%
of manufacturers will have transitioned by then to a microservices architecture
to enable differentiated AI and analytics capabilities. From a purely strategic
standpoint, smart manufacturing makes sense: AI and predictive analytics allow
you to detect data outliers using IoT data, perform root cause analysis in
seconds, prevent accidents and downtime before they happen, optimize supply
chain processes, and augment customer experience for increased conversions and
brand loyalty.

So how are the benefits of AI and predictive analytics achieved across the
manufacturing value chain? By modernizing legacy technology with industrial
internet of things (IIoT) systems and a robust Analytics Platform, manufacturers
can gain real-time insight on factory floor, supply chain and customer
experience processes.


THE CASE FOR AI IN SMART MANUFACTURING

In today’s globalized world, it is imperative that manufacturing value chains
continue to function regardless of extenuating circumstances such as equipment
failure, extreme weather conditions, geopolitical events, pandemics, or any
other cause of potential disruption. A disruption at the manufacturing site in
one country runs the risk of creating a ripple effect and having a negative
impact on a global scale. This is where machine learning (ML) and artificial
intelligence (AI) come into play.

Predictive and real-time analytics ensure maximum production output, eliminate
workplace inefficiencies and hazards, and empower manufacturers to prepare for
and respond efficiently to changing market demands.

For example:

 * Predictive maintenance algorithms monitor equipment/machinery data to help
   predict timely maintenance schedules to avoid any unplanned production
   downtime.
 * Anomaly detection models are trained to identify outliers in data that
   indicate machinery faults, identify where the faults are, and even provide
   relevant fixes or alert machine operators to attend to the issue before major
   damage occurs.
 * Visual inspection tools in the form of IoT sensors and cameras can identify
   product faults and assembly line inefficiencies to optimize quality control
   and speed of production.
 * Furthermore, on the supply chain and commercial side of manufacturing
   operations, AI analytics can help optimize product demand planning, prevent
   product stock-outs, enable real-time inventory visibility, and manage product
   delivery and logistics more efficiently.  

As such, manufacturers can focus on their core business strategy and begin to
invest in further business innovation.  

In this blog, we’ll break down how AI and analytics optimize each part of the
manufacturing value chain.


AI-POWERED FACTORY FLOOR

Stalled or broken down equipment is a disaster in the making for any
manufacturer. It’s not just about losing money: sometimes, these setbacks can
also pose a serious risk to the workers operating the shop floor. Add the cost
of product quality defects and assembly line disruptions to that scenario and
you quickly realize the overwhelming value that automated quality control tools
can bring to the factory floor. Through the use of industry 4.0 tools for
anomaly detection, predictive maintenance and visual inspection, manufacturers
are better able to:

 * Monitor equipment health;
 * Establish timely equipment maintenance schedules;
 * Detect machinery/product anomalies and automate fixes or alert staff;
 * Conduct root cause analysis to identify where faults are located; and
 * Identify product quality defects and assembly line inefficiencies.


ANOMALY DETECTION

Anomaly detection tools use AI algorithms to process IoT sensor data from your
equipment, monitor system health, detect anomalies in early stages, perform root
cause analysis, and trigger preventive or reactive actions that help to reduce
monitoring costs and time-to-resolution, and prevent failure propagation.


PREDICTIVE MAINTENANCE

Predictive maintenance tools use advanced models that analyze both single-metric
and cross-metric patterns, taking sensor and component topologies into account,
to enable reliable, real-time risk scoring. Based on these risk predictions,
time-to-failure can be estimated, and maintenance schedules can be optimized to
prevent failure propagation and to extend the remaining useful life of assets.


VISUAL QUALITY CONTROL

Visual quality control relies on a comprehensive toolkit of computer vision
algorithms for anomaly detection, using a wide range of signals and data
sources, including IoT sensor data, infrared and X-ray imagery, and video
streams, to detect product quality and production anomalies to prevent larger
failures, outages and the resulting costs.

Read more:

 * Anomaly detection for Industry 4.0
 * Anomaly Detection in Industrial Applications: Solution Design Methodology
 * Detecting anomalies in high-dimensional IoT data using hierarchical
   decomposition and one-class learning


AI-POWERED SUPPLY CHAIN

Smart manufacturing can drastically simplify inventory planning across your
supply chain networks. Managing numerous suppliers and distributors can quickly
become a cumbersome process if the right system isn’t in place. AI tools for
supply chain optimization can help to improve:

 * Supplier network and inventory management;
 * Warehouse management; and
 * Logistics and transportation.

Predictive analytics, combinatorial optimization, and simulation algorithms can
help determine changes in consumer demand and supplier network capacity,
allowing manufacturers to plan ahead concerning production schedules and
obtaining any necessary raw materials. It also optimizes inventory flow, safety
stock levels, and production costs. The data used to provide this insight is
gleaned from various factors such as the manufacturer’s historical sales data,
financial data, supplier data, weather/social/political events, market
forecasts, third-party platforms such as social media, and so on.

Read more:

 * Reimagining resilience: supply chain optimization for smart manufacturing
 * Why you need a cloud-native analytics platform for smart manufacturing


AI-POWERED DIRECT-TO-CONSUMER DISTRIBUTION

In today’s digitally connected world, smart manufacturing capabilities benefit
not only the manufacturer but can also work to enrich digital customer
experiences. As part of their smart manufacturing modernization efforts, many
manufacturing companies are shifting their go-to-market strategies from B2B only
to a blend of B2B and D2C sales to drive revenue, maximize profits and gain
greater control of their brand. To facilitate this new go-to-market model,
manufacturers must continually improve their operations and technology platforms
to support digital marketing and personalized customer experience.

How are they doing it? Industrial e-commerce sales optimized with AI-driven
customer experience capabilities.

Using AI to provide real-time inventory visibility, multiple fulfillment
options, accurate delivery timelines, and automated delivery notifications
enriches the customer experience and greatly minimizes the risk of churn.

Moreover, AI algorithms can detect real-time changes in business metrics, such
as unexpected drops or spikes in traffic, conversion rates, session durations,
and so on, enabling manufacturers to quickly react to issues and respond to
customer demands.


INVENTORY AVAILABILITY

An AI-powered inventory management system prevents customer demand from
exceeding manufacturer supply. AI algorithms monitor and update inventory levels
in real time so that customers always know exactly what is in stock on the
product details page of your e-commerce site. Furthermore, this real-time
monitoring helps manage safety stock levels and prevent stockouts.


FULFILLMENT OPTIMIZATION AND ACCURATE ESTIMATES

A sophisticated omnichannel fulfillment model enables customers to choose
between options such as “ship to store”, “ship from store”, “buy online pick up
in store” and so on, and enables manufacturers to optimize delivery costs.
Moreover, manufacturers can refine delivery times by relying on AI to choose
optimal routes based on factors such as the weather, geopolitical or social
events, and other factors that impact travel and logistics. This reduces costs
for both the manufacturer and the customer.


AUTOMATED NOTIFICATION FLOW

Customers count on manufacturers to provide them with the most important
notifications in real-time. Incorporating an omnichannel approach means that
customers can specify when they receive notifications and on their preferred
channels. AI-powered messaging systems can supply users with real-time updates
after products ship from warehouses to keep customers informed and excited to
receive their deliveries.


CONCLUSION

Manufacturers looking to stay on top of equipment and assembly line
productivity, supply chain capacity, and customer experience personalization are
turning to AI to take smart manufacturing to a whole new level - from the
factory floor right through to the customer’s door.

Grid Dynamics is a digital-native technology services provider that accelerates
growth and bolsters competitive advantage for Fortune 1000 companies. We have
more than 15 years of experience in digital transformation and software
innovation.

Interested in expanding your smart manufacturing strategy? Get in touch with us
to start learning more!


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