Data-driven methodologies such as big data and artificial intelligence (AI) are driving faster and more accurate decision making in every step of the manufacturing value chain. Procurement, logistics, production rates, and product quality all stand to massively benefit from better data management and utilizationAI is becoming increasingly important to the success of manufacturers, and companies are increasingly capitalizing on recent advancements in the field. 66% of manufacturers surveyed who use AI in their day-to-day operations report that their reliance on AI is increasing.
Potential value created by AI solutions for the manufacturing industry globally
Percentage of manufacturers that rely on AI for day-to-day operations
CAGR of AI in the manufacturing solutions market from 2021 to 2028
Embracing a data-first approach helps manufacturers be proactive rather than reactive,allowing them to stay one step ahead of supply chain disruptions, market volatility,and evolving customer preferences.
The complexities of managing manufacturing data mean that operationalizing data initiatives is notoriously difficult. Indeed, manufacturing produces more data per year (over 1800 PB) than any other industry
.However, only 9% of surveyed manufacturers say AI projects meet their expectations. Given the industry’s multibillion dollar annual investment in AI strategies, this low conversion efficiency results in considerable financial and productivity losses.
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Functions of a data platform
A data platform enables retailers to quickly experiment with, productionize, and scale high-impact datainitiatives like AI models.
They enable teams to easily analyze arbitrary data volumes and build performant AI systems that can meet the needs of any deployment.
They abstract away compute runtime setup andupgrades, software containerization, hardwareprovisioning, workflow orchestration, etc.
They empower teams to collaboratively iterate,validate, and communicate data strategies beforeproductionizing them.
They move data initiatives across the finish line, fromidea to full-scale, ultimately enabling an organizationto reap the benefits of its data strategy investment.
Choosing the right data platform enables your team to
focus on leveraging data, not managing it.
The Kaspian Data Platform™ is a powerful compute layer for the modern data cloud. Kaspian abstractsaway the complexities of managing compute infrastructure by exposing granular, UI-driven software andhardware control planes. The compute resources can then be consumed as standalone Jobs (ex. Sparkcluster jobs, multi-GPU deep learning training jobs), chained together in a visual workflow builder usingPipelines, and leveraged via Notebooks for iterative development and experimentation.
Jobs – standalone compute workloads with support for open source orchestrators(ex. Apache Airflow)
Pipelines – low-code,multi-stage workflow graphbuilder with a built-inorchestrator and scheduler
Notebooks – Jupyter Notebooks for prototyping data workflows and developing AI models
Kaspian’s software control plane (Environments) enables users to define the dependencies needed to run their data workflows. Environments can be defined with Docker images andDockerfiles, and Kaspian can also auto-build Environments from Pip/Conda requirements files
Kaspian’s hardware control plane (Clusters) enables users to define virtual compute groups for different workload sizes, types, and use cases. Clusters automatically autoscale within specified limits and spin down fully when not in use for maximal cost efficiency.
By facilitating the provisioning and management of multiple popular compute runtimes in a single console, Kaspian empowers scientists and analysts to frictionlessly experiment with and operationalize data technologies at scale without dealing with multiple vendors and point solutions.
Retailers can deploy Kaspian in under one hour and customize their instance to meet their specificdata workload, operations, and security requirements.
Kaspian deploys into your cloud environment as aKubernetes application. Compute and storage artifactsrelated to your deployment stay within your cloud formaximal data privacy and security
In addition to interfacing with your data lakes and warehouses, Kaspian connects to your version control system so you can maintain your GitOps and CI/CDpipelines. It also plugs into your notification system.
Leverage Kaspian’s managed hardware and software control planes to define the interoperable compute configurations upon which Kaspian Jobs, Pipelines, and Notebooks can run.
Kaspian’s core platform product empowers data scientists and analysts to prototype, productionize, and deploy data pipelines and AI solutions at any scale.
In addition to interfacing with your data lakes and warehouses, Kaspian connects to your version control system so you can maintain your GitOps and CI/CDpipelines. It also plugs into your notification system.
Kaspian offers responsive customer support for technical issues, implementation assistance, feature requests, and other account inquiries.
Kaspian engineers have extensive experience settingup and managing cloud infrastructure, data lakes and warehouses, and compute services.
Research suggests that the greatest value to manufacturing from AI is in predictive maintenance, which is projected to save $500-700B for companies globally. Indeed, 29% of manufacturers implementing AI are planning to focus on maintenance. Forecasting when machinery is likely to fail is a powerful way to reduce downtime.
Predict failures
before they happen
Schedule maintenance as machines are beginning to show signs of failure by training and deploying anomaly detection algorithms on Kaspian. These models can be connected to retraining pipelines so that they continuously improve their performance over time. Reduce disruptions to your assembly lines and improve worker safety
Develop bespoke models
for different machines
Different machines exhibit different failure modalities, which makes it difficult for off-the-shelf models to generalize. Successful solutions require customization, and because Kaspian manages all of the required compute infrastructure, data teams can readily iterate upon and deploy custom AI solutions
Ingest huge volumes
of sensor data
Kaspian’s infinitely-scaling compute backend gives data engineers the confidence to connect arbitrarily many data sources: the more sensors that are connected, the greater the ability for AI models to predict complex failure modalities. The rich metadata produced by these sensors can also be utilized for process mining.
Manufacturing assembly lines require a constant rebalancing of supplier inventory against customer demand. Improvements in demand planning can improve the top and bottom lines by minimizing excess inventory while ensuring sufficient stock for customers. Forecasting with machine learning is becoming increasingly more powerful as companies have been able to integrate multiple datasets and systems.
Higher profit margins enjoyed by companies who utilize demand forecasting
Reduction in cash-to-cash cycle times as a result of forecasting models
Less inventory required on hand while maintaining 17%better order fulfillment
Fewer out-of-stock events vs competitors
Off-the-shelf vendors for product forecasting struggle to understand the nuances of specific businesses and supply chains. Developing an in-house solution not only gives teams greater visibility over production needs, but also produces a treasure trove of metadata upon which analysts can develop dynamic production allocation algorithms with actionable and customized insights.
Kaspian seamlessly integrates with the databases, data lakes, and data warehouses manufacturers use to store inventory levels and external market signals. After iterating on allocation algorithms in code notebooks, analysts can leverage Kaspian’s low-code data pipeline builder to schedule and deploy recurring production rebalancing workflows and stakeholder-facing dashboard updates
Manufacturing businesses are built on a complex web of interconnected processes. Process mining extracts knowledge from event logs produced by networked systems to determine where bottlenecks lie, enabling manufacturers to quantitatively prioritize initiatives that tackle inefficiencies.
Catalog business processesDevelop an end-to-end centralized understanding of operations
Built-in dashboarding
for visibilityBundled dashboarding facilitates communication of key insights
Identify process
bottlenecksGenerate performance insights with native AI and big data capabilities
The cost of manufacturing defects includes not only the financial penalties of lost products, but also reputational damage and fines associated with regulatory non-compliance. With advancements in AI, it is becoming increasingly effective to perform in-line automatic product defect detection to catch mistakes before they propagate. This capability is so valuable that 27% of manufacturers implementing AI are planning to primarily focus on product quality solutions
See more, see better
By harnessing Kaspian’s deep learning capabilities, specifically its managed cloud GPU offering, data scientists can develop and deploy advanced computer vision models for assessing product quality from camera feeds or other IoT sensors located in assembly lines.
Always-on, always improving AI
Data scientists can utilize Kaspian’s low-code pipeline builder to continuously improve their defect detection models.Kaspian’s flexible and fully-managed compute layer enables model training pipelines to have full flexibility of scale and implementation. Work on data of any scale with Spark and take advantage of GPUaccelerated PyTorch deep learning models with zero additional setup or maintenance.When instrumented correctly, these training workflows form positive feedback loops: more labeled examples of previously-encountered defects improve the underlying AI models and consequently the quality of subsequent detections.
