For Original Equipment Manufacturers, inventory management is a matter of great importance. It is their master lever to unlock streamlined operations, cost savings and demand in the national and international markets. But what are the relevant elements that help in actionable strategies? Here are a few mentioned with global examples on execution.
Predicting the Unpredictable: Smarter Demand Forecasting
Gone are the days of relying on gut feelings or outdated spreadsheets. Today’s OEMs use predictive analytics to anticipate market shifts with startling accuracy. A great example would be what Dell Technologies does. They do their analysis in real time on customer data and macroeconomic trends. By doing so, they managed to reduce excess component inventory by 30% while maintaining near-perfect delivery rates. What they used was ML models that adjust procurement plans hourly, not monthly.
Siemens takes this further with digital twins, virtual replicas of production lines that simulate demand scenarios. When the company noticed a surge in orders for industrial automation parts, these simulations allowed them to recalibrate inventory without overstocking. The result? A 20% drop in obsolete stock.
JIT Isn’t Dead—It’s Evolving
Toyota introduced the Just-In-Time or JIT system back in the 1970s and its modern version is still saving the company billions of dollars every year. However, the 2011 Thailand floods exposed its supply vulnerability when the company’s suppliers were underwater, halting production for weeks. Bosch follows a hybrid model where it keeps a safety stock in place, this ensured their European assembly lines stayed active even when their suppliers in Malaysia faced lockdowns due to the pandemic.
Safety Stock: The two-ended sword of dilemma
Too much safety stock strangles cash flow; too little risks shutdowns. Ford’s response to the 2020 chip shortage shows how to strike a balance. Using AI tools that recalculate safety stock needs daily, the automaker reduced capital tied up in buffer inventory by 15%—even as rivals idled plants.
Contrast this with Samsung’s 2018 misstep. Underestimating demand for OLED screens left the company scrambling to fulfill iPhone orders, costing $300 million in lost sales. The takeaway? Static safety stock formulas fail. Dynamic, data-driven models win.
Suppliers are Partners, Not Vendors
Apple’s iPhone 5 production delays in 2012 led Apple to learn a lesson on over-reliance. With its collaboration now with Pegatron and Luxshare, Apple’s suppliers let them monitor Apple’s inventory in real time. This ensured that lead time was cut by 25% and Aplle’s suppliers became collaborators rather than vendors.
Walmart and P&G went a level beyond by doing something called Vendor-Managed Inventory (VMI).
By letting P&G track Walmart’s shelf-level stock data, the retailer reduced out-of-stock incidents by 10%—a win-win that boosted sales for both companies.
Lean Isn’t Just for Factories
Caterpillar’s lean journey began with a simple question: Why store months’ worth of hydraulic components when customers order weekly? By mapping every step from raw material to delivery, the company identified $2 billion in waste over a decade. Their factories now run with 40% less buffer stock, proving lean principles apply as much to warehouses as assembly lines.
John Deere’s story echoes this. By redesigning workflows at its Iowa plant, the company reduced raw material waste by 22%, a figure highlighted in its latest sustainability report.
Tech’s Double-Edged Sword
While AI integrations in large-scale businesses have definately made life easier, it is not without complications and errors. Amazon warehouses have over 500,000 robots that sort, fetch & pack items, ensuring seamless transfers of products to customers. However, a software glitch in 2023 led to a series of malfunctions and robots collided into shelves and each other. This led to the need to ensure smarter fail-safe and rollback options.
EOQ in the Real World
Procter & Gamble’s diaper division shows Economic Order Quantity (EOQ) done right. By integrating data on global demands, shipping costs & the possible need for backup stocks, P&G optimized P&G optimized batch sizes, saving $500 million annually.
But EOQ has pitfalls. Tesla’s 2018 battery component overorder—a $150 million mistake—reveals what happens when models ignore demand reality.
Location, Location, Location
Fashion major Zara has has cracked the code in supply chain management. With 85% of all production close to its Spanish HQ, it is able to get products from sketch to store in a matter of just 15 days. This ensures Zara has a 30% less inventory while still staying relevant in comparison to competition.
On the other hand, H&M used a decentralized network which left the company with $4.3 billion in unsold clothes in 2018.
Inventory Forecasting- How Sankey is helping in the Revolution
Effective supply chain management in commercial vehicle (CV) manufacturing plants hinges on accurate demand forecasting. However, disparate data sources and the lack of integrated forecasting models often lead to spare-part overstocking, shortages, and inventory imbalances.
To address this, Sankey Solutions has developed a data-driven demand forecasting system. The solution begins by integrating historical consumption and production data from APIs, SAP systems, and BOM records. This data undergoes cleaning, transformation, and exploratory analysis before being stored on AWS S3 for secure access.
Machine learning models are developed using MLflow, allowing rigorous training, evaluation, and reproducibility. Once trained, models are containerized via Docker and deployed as API-based services, enabling seamless integration with enterprise applications.
The system outputs granular, day-wise part demand forecasts. These predictions empower planners to make data-informed decisions, optimizing inventory levels and ensuring uninterrupted production flow across multiple plant locations.
The Path Forward
From Toyota’s JIT evolution to Amazon’s robot armies, successful OEMs treat inventory as a living system, not a static asset.
AI is now easily accessible and can use calable tools to cut costs, spur innovation, and streamline operations without having to make significant infrastructure investments. Sankey Solutions provides customised AI solutions that meet the demands of contemporary automakers, ranging from digital manufacturing cockpits to predictive maintenance. Our data-driven, real-time insights assist companies in making better decisions, maximising efficiency, and maintaining a competitive edge in a rapidly evolving mobility landscape.
We believe in embracing tools like predictive analytics but temper them with human judgment. We must work on building supplier ecosystems, not transactional relationships. Recognizing that today’s perfect formula becomes tomorrow’s liability in our era of constant disruption.’
Introduction
With the rise in market demand for Software Defined Vehicles, the automobile industry is becoming more and more reliant on complex code-based systems. OTA as a mechanism for updation of these software systems, has enabled software enhancement remotely. By fixing bugs and enhancing the user experience without having to call the vehicle into the service centre, the overall customer servicing has become seamless. However, the complexities and mechanical implications are much deeper.
What is an OTA Update?
OTA updates are software updates for a vehicle, delivered wirelessly. They directly connect to the car’s systems once updated onto the cloud and activate the updates without the vehicle having to visit the service centre.
The mandate earlier was very different. Apart from some high-end infotainment systems, vehicle software would only be updated when the vehicle would visit the service centre. This was often a once-a-year affair and all bugs that were part of that version would stay as an issue till the customer either downgraded or went for servicing a year later.
With the help of cloud-based platforms, the entire industry today is focused on streamlining operations by enhancing the customer experience without the customer having to participate in the service. Some examples of services that are being updated remotely include vehicle diagnostics, energy management and safety features. This is how OTA is spearheading the future of automotive innovation.
OTA can further be divided into 2 versions, SOTA and FOTA.
OTA is further divided into 2 types, Software Over-The-Air (SOTA) and Firmware Over-The-Air (FOTA). They both enable remote updates to software and firmware updates to a vehicle without physical intervention of any kind.
SOTA- Creates and tests update packages via a cloud server. It notifies the vehicle of it’s new update availability, downloads onto the system, installs and finally reboots and verifies to ensure success in implementation.
FOTA- Considered a subset of SOTA it’s updates are relevant to hardware functionalities. The workflow mirrors the SOTA workflow, but is more critical and hence requires additional precautions. FOTA updates enable manufacturers to deploy firmware enhancements, security patches, and bug fixes efficiently, ensuring devices operate optimally and securely.
Benefits of OTA Updates in vehicles today
- Enhanced Vehicle Management- Updates can be deployed regardless of where the vehicle is. This ensures safety measures and bug fixes are done on the fly.
- Better User Experience- When a vehicle is getting updated without the user having to move a muscle, it adds to the streamlined customer experience and trust-building factors. Creating a better vehicle experience with every update ensures the customer stays happy for years to come.
- Reduced Recalls- A lot of software-related issues forced vehicle companies to recall their vehicles or it was too dangerous to drive. With OTA, these updates are done remotely, ensuring customers do not need to send their vehicles back and can continue using them after the remote update.
Technical Aspects of OTA Update Processes
- Firmwares: When it comes to baseline vehicular updates, each vehicle has multiple ECUs. Each ECU has a firmware which is responsible for the functioning of the cars movement and safety features. These features require periodic updates to enhance safety and updated bugs.
- Central Gateway: This is the nucleus of all software related communications. The central gateway facilitates the exchange of data between the cloud and the vehicle software network.
- Telematics Unit: This is the in-built network unit of the vehicle. It houses the cellular chip that helps in managing the internet module of the vehicle.
Key Technologies Behind Secure OTA Updates
- Secure Communication: To prevent unauthorised accesses by hackers, an encrypted channel is paramount to enabling OTA updates.
- Authentication: To prevent tampering, digital signatures must be integrated to ensure updates only from an authorised entity is accepted by the vehicle.
- Delta Updates: To save time and space, only the modified portions of the software is sent as an update.
- Cloud Backend
- A secure cloud setup is able to update software in vehicles from around the world.
Challenges in OTA Implementation
- Connectivity- Seamless access to the network is mission-critical in OTA updates, especially in the more remote areas.
- Compatibility- Software updates in service centres are easier to execute for older vehicles whereas in case of OTAs, compatibility often becomes an issue.
Future Trends & How We Are Leveraging OTA Updates
At Sankey Solutions, our team is providing top players in the automotive sector with secure OTA updates for their fleets. Our priority in addition to efficiency is to ensure these updates are secure.
We combine a trio of blockchain frameworks, zero-trust architecture and AI-powered threat detection. We ensure a tamper-proof ecosystem by using an encrypted delivery channel.
Softwares today controls all critical and leisure functions of vehicles. As updates become more and more critical, waiting for them to visit a servicing station to update isn’t at all safe or convenient. Hence, to ensure life goes on and the vehicle performance and safety can be enhanced on the fly, OTA’s are critical to mobility today.
Popular use cases would be Tesla & Mercedes-Benz. While the former uses OTA to enhance battery management and autopilot capabilities, the latter updates its driver assistance systems, giving the customers better experience without asking them to visit the service centre.
Introduction: The Changing Landscape of Automotive Manufacturing
The automotive sector is experiencing a seismic shift with the adoption of Industry 4.0 technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing. At the heart of this transformation lies the Digital Manufacturing Cockpit (DMC)—a centralized, data-driven control hub that aggregates information from a network of Cyber-Physical Systems (CPS) and Industrial IoT (IIoT) devices. These systems enable manufacturers to optimize Overall Equipment Effectiveness (OEE), reduce Mean Time to Repair (MTTR), and minimize Mean Time Between Failures (MTBF).
Through advanced analytics, real-time monitoring, and predictive modeling, DMCs streamline production processes, enhance resource utilization, and significantly lower operational costs—all while achieving scalable efficiency.
Understanding Digital Manufacturing Cockpits
Imagine being the Plant Head at an OEM producing thousands of vehicles daily. Your assembly line operates with robotic arms, Automated Guided Vehicles (AGVs), and Programmable Logic Controllers (PLCs). A minor fault—if undetected—could cascade into a full-scale disruption, halting operations and incurring millions in losses.
A Digital Manufacturing Cockpit resolves this by functioning as an Advanced Supervisory Control and Data Acquisition (SCADA) system, monitoring every node and operation. It integrates data from sensors, Machine Vision Systems, and Edge Computing Devices to detect anomalies, predict system failures, and trigger proactive responses. For example, it might identify an impending actuator malfunction, enabling the system to schedule predictive maintenance before downtime occurs. This capability not only safeguards Key Performance Indicators (KPIs) like cycle time and yield rates but also ensures operational resilience.
Components and Technologies Driving Digital Cockpits
Human-Machine Interfaces (HMI): Interactive dashboards offer visualizations of KPIs, allowing operators to make rapid, data-backed decisions.
IoT and Smart Sensors: Provide telemetry data, including vibration analysis, thermal mapping, and energy consumption trends, for a comprehensive understanding of machinery performance.
Edge and Fog Computing: Enable localized data processing to reduce latency and ensure high-speed decision-making for mission-critical operations.
Cloud-Native Architecture: Supports seamless data exchange and scalability across multi-site manufacturing facilities.
AI/ML Algorithms: Deliver insights into anomaly detection, process optimization, and prescriptive actions, elevating operational intelligence to the next level.
Real-World Scenarios: How Sankey Solutions Created an Impact
- Scenario 1: Optimizing EV Battery Assembly Lines
Sankey Solutions partnered with an automotive OEM to implement a DMC within their electric vehicle (EV) battery manufacturing line. By integrating Battery Management System Software (BMS), the DMC monitored battery module assembly in real-time. It tracked telemetry data such as voltage fluctuations and thermal anomalies, enabling predictive maintenance and ensuring compliance with quality standards before final assembly.Results:
- 20% reduction in assembly defects.
- Early detection of battery inconsistencies saved up to 15% in production costs.
- Enhanced visibility into the supply chain improved component traceability, ensuring seamless regulatory compliance.
- Scenario 2: Streamlining Vehicle Production Utilizing a DMC to align Schedules
An Indian automotive manufacturer utilized a DMC to align production schedules with real-time plant data. The Plan vs. Actual dashboard helped identify deviations from production goals and provided actionable insights for resolving bottlenecks. The Sequence Adherence Report ensured that vehicle assembly processes adhered to strict quality standards.Results:
- Improved Sequence Adherence to 98%, reducing rework.
- Enhanced Vendor On-Time Delivery (OTD) by analyzing supply chain inefficiencies, leading to a 30% reduction in delays.
- Real-time KPI tracking enabled the plant to achieve 15% higher throughput efficiency.
Benefits and Challenges of Digital Cockpit Adoption
Benefits:
1. Predictive Maintenance :
- Enables Condition-Based Monitoring (CBM) and reduces downtime through real-time diagnostics.
- Connectivity: Accessible through Web APIs, mobile applications, and Industrial Control Systems (ICS).
2. Dynamic Interfaces :
- Customizable dashboards tailored for discrete or continuous manufacturing setups.
3. Portability :
- Deployable across hybrid manufacturing ecosystems.
4. Enhanced Production Control :
- Real-time Digital Twin Simulations allow for accurate tracking and optimization of manufacturing processes.
5. Data-Driven Decision-Making :
- Cloud-augmented platforms provide access to Big Data Analytics, enabling rapid resource allocation, inventory planning, and process reengineering.
Challenges:
- System Integration: Incorporating DMCs into Legacy Automation Systems requires robust middleware solutions and APIs.
- Data Security Concerns: Protecting sensitive production data from cyber threats demands the implementation of Zero Trust Architecture (ZTA) and encryption protocols.
- Workforce Readiness: Implementing DMCs necessitates Technical Training Frameworks for operators and engineers to familiarize them with advanced technologies.
Enhancements to Automobile Manufacturing Transformation with DMCs:
- Battery Management System Optimization:
DMCs can integrate Battery Management System Software to monitor and analyze battery performance in real-time during vehicle production. This enables predictive maintenance of batteries used in electric vehicles (EVs) and ensures quality control before final assembly. - Custom Process Automation:
Utilizing Custom Software Development, DMCs can be tailored to handle specific production line requirements, such as automating workflows for battery module assembly or streamlining EV drivetrain manufacturing, ensuring alignment with evolving production goals. - Enhanced Component Traceability:
With integrated Battery Management Software, DMCs can track battery components through various stages of manufacturing. This capability reduces errors and accelerates the assembly process by ensuring correct sequencing and alignment of parts. - Energy Usage and Sustainability Metrics:
By incorporating data from IoT-enabled battery systems, DMCs can analyze energy consumption patterns during manufacturing. This supports the development of eco-friendly practices and enhances energy efficiency. - Battery Management Software could be presented as part of advanced monitoring systems that ensure the health and efficiency of battery systems during manufacturing.
- Custom Software Development fits naturally when discussing the adaptability and scalability of DMCs to address the specific needs of modern automotive production facilities.
Industry Benchmarking and Insights
- Predictive maintenance powered by IIoT reduces machine downtime by up to 50% while extending component lifespans by 30%.
- Factories utilizing edge-computing-based cockpits have observed a 25% increase in Throughput Efficiency.
- Cloud-integrated DMCs deliver a 20% reduction in operational costs by enhancing Just-In-Time (JIT) manufacturing capabilities and reducing overproduction.
These metrics underscore the transformative potential of digital cockpits in driving profitability and operational excellence.
Scaling Beyond Automotive
While Digital Manufacturing Cockpits have revolutionized the automotive industry, their modular and scalable architecture positions them as a pivotal tool for adjacent sectors such as aerospace, pharmaceuticals, and consumer electronics. For instance, in the pharmaceutical industry, DMCs can integrate with Good Automated Manufacturing Practice (GAMP) systems to ensure compliance and quality control.
By fostering predictive maintenance, enabling Remote Asset Management (RAM), and integrating Supply Chain Analytics, these platforms are driving industries toward smarter, more efficient, and sustainable production ecosystems. Adopting DMCs is not just a technological upgrade—it is a step toward redefining industrial excellence in the era of digital transformation.