Data-driven methodologies such as big data and artificial intelligence (AI) are driving faster and more accurate decision making in every step of the supply chain and logistics industries. Pricing, supply chain optimization, fleet management, and ESG efforts all stand to massively benefit from better data management and utilization.
AI in particular promises to help logistics companies better manage their supply chains: successful AI enabled supply chain management has empowered early adopters to improve logistics costs by 15%, inventory levels by 35%, and service levels by 65% when compared to their non-AI-enabled competitors.
Predicted value of the global AI market for supply chain in 2028, with a CAGR of 20.5% from 2022 to 2028
Percentage of surveyed executives want to invest in more technology to identify and track supply chain risk
Percentage of AI-driven supply chain transformation projects that were either delivered late or over budget
Embracing a data-first approach helps logistics providers 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 operating with logistics data mean that operationalizing a data initiative is notoriously difficult: only 17% of surveyed executives say that their company’s investments in supply chain technologies have fully delivered the expected results.
Given the industry ’s multi-billion dollar annual investment in data-driven strategies, this low satisfaction implies considerable financial and productivity losses.Despite the massive opportunity on hand, AI and machine learning penetration in logistics is relatively low, with only a third of companies leveraging AI to enhance their supply chain planning and even fewer for other stages
AI/ML Usage in
Supply Chain Stages
Functions of a data platform
A data platform enables logistics providers to quickly experiment with, productionize, and scale high-impact data initiatives like AI models. Data platforms have four principal functions:
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.
For the logistics industry, pricing has the highest and fastest impact on profitability of all available improvement levers, with the holy grail being highly efficient and automated dynamic freight pricing. Companies that invest in digitizing and modernizing their pricing strategies can see a revenue boost of 2 – 4%, corresponding to considerable EBIT margin improvement of 30 – 60%
Understanding historical trends is the first step to developing a real-time pricing model. Kaspian’s infinitely scalable compute backend allows data teams to crunch even the most granular and massive datasets with confidence. These capabilities are essential to leverage modern methodologies like hyperlocal geospatial analytics and can power highly accurate forecasts.
Spot rates that hit the spot
Determining the optimal spot rate to charge is a complex exercise that must factor in numerous variables. Because safety rates directly determine the profitability of logistics providers, it is paramount that these figures accurately reflect current market conditions.
Kaspian seamlessly integrates with the databases, datalakes, and data warehouses organizations use to track data, fuel prices, driver preferences, and more. After iterating on pricing algorithms in code notebooks, analysts can leverage Kaspian’s low-code data pipeline builder to deploy both a high-frequency rate adjustment workflow and a daily dashboard update.
Commercial vehicle fleets comprise the lifeblood of the physical economy: over 23 million are produced each year around the world, and more than 32 million are on the road in North America alone. These vehicles are parts of vast connected fleets and generate tremendous quantities of data, from ELD-associated metadata to OEM-specific telemetry to driver-generated reports. Harnessing these data assets at scale via AI will enable logistics companies to streamline their operations.
Companies are under increasing regulatory, consumer, and investor pressures to broaden and publish their environmental, social, and governance (ESG) strategies. Data fragmentation in supply chains greatly complicates ESG adoption, with approximately 50% of surveyed executives saying that identifying ESG supplier risks and prioritizing minority-owned, diverse suppliers pose challenges to their supply chain functions
Percentage of Surveyed Executives Citing These Gaps as Challenges to ESG Integration
Develop a single source
of truth
Consolidate data from disparate data silos such as emissions assessments, water and power consumption records, and employee accident reports to develop a unified and comprehensive understanding of ESG position. Analysts can schedule Kaspian pipelines to load data from these silos’ APIs into a centralized data warehouse
Discover insights at scale
IoT sensors are increasingly being utilized to quantify metrics such as carbon footprint, energy efficiency, water and air quality, and waste production. Kaspian’s support for big data technologies like Spark enables analysts to consume the large volumes of data produced by IoT devices and thereby direct ESG strategy
Accurately assess
ESG strategy impactKaspian’s native dashboarding solution makes it easy for data analysts to consolidate and communicate both quantitative and qualitative outcomes of ESG initiatives. These assessments can then directly power the consumer-facing and investor-facing reports companies must produce to demonstrate their ESG posture
Supply chains 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
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.
