Grid Dynamics
Canvas Category Consultancy : Company : Information Technology
Grid Dynamics (NASDAQ: GDYN), a global digital engineering company, co-innovates with the most respected brands in the world to solve complex problems, optimize business operations and better serve customers. Driven by business impact and agility, we create innovative, end-to-end solutions in digital commerce, AI, data and cloud to help clients grow.
Assembly Line
⛓️ A guide to supply chain control tower use cases
At a high level, we assume that the control tower receives a broad range of data from 1st and 3rd parties, and integrates with a number of operational systems, such as the system for warehouse management, and provides four categories of capabilities:
- Strategic planning. Decision support tools that are focused on long-term, often multi-year, time horizons.
- Inventory visibility. Near real-time insights and alerts that support ongoing operations.
- Inventory flow control. Decision automation tools for ongoing operations such as replenishment.
- Impact analysis and resolution. Tools for reacting to disruptions and deviations from planned scenarios.
The revenue-at-risk assessment is followed by the development of mitigation strategies. In particular, the company can change suppliers of certain parts or modify product designs to reduce the risks. Such decisions are supported by risk evaluation tools that allow one to assess the current risks and perform what-if analysis for alternative scenarios.
🖨️ Visual quality control in additive manufacturing: Building a complete pipeline
In this article, we share a reference implementation of a VQC pipeline for additive manufacturing that detects defects and anomalies on the surface of printed objects using depth-sensing cameras. We show how we developed an innovative solution to synthetically generate point clouds representing variations on 3D objects, and propose multiple machine learning models for detecting defects of different sizes. We also provide a comprehensive comparison of different architectures and experimental setups. The complete reference implementation is available in our git repository.
The main objective of this solution is to develop an architecture that can effectively learn from a sparse dataset, and is able to detect defects on a printed object by controlling the surface of the printed object each time a new layer is added. To address the challenge of acquiring a sufficient quantity of defect anomalies data for accurate ML model training, the proposed approach leverages a synthetic data generation approach. The controlled nature of the additive manufacturing process reduces the presence of unaccounted exogenous variables, making synthetic data a valuable resource for initial model training. In addition to this, we hypothesize that by deliberately inducing overfitting of the model on good examples, the model will become more accurate in predicting the presence of anomalies/defects. To achieve this, we generate a number of normal examples with introduced noise in a ratio that balances the defects occurrence expected during the manufacturing process. For instance, if the fault ratio is 10 to 1, we generate 10 similar normal examples for every 1 defect example. Hence, the pipeline for initial training consists of two modules: the synthetic generation module and the module for training anomaly detection models.
Inventory allocation optimization: A pre-built solution for Dataiku
In this blog post, we present the Inventory Allocation Optimization Starter Kit for complex environments that was jointly developed by Grid Dynamics and Dataiku. This solution is implemented on top of the Dataiku platform and is available in the Dataiku marketplace. It is geared toward retailers, brands, and direct-to-consumer manufacturers that seek to improve supply chain operational efficiency, increase customer satisfaction, and reduce shipping costs and order splits.
We show how you can make more informed inventory level decisions to meet customer demands and cut costs by carefully considering a variety of factors, including demand forecasting, procurement, order splitting, and shipping costs with a single solution: The Inventory Allocation Optimization Starter Kit. The solution can be extended with various features such as procurement shipping capacity constraints, region-level constraints, and more. The created pipeline can also be transformed into a replenishment control solution that optimizes not only the static inventory allocation levels, but also replenishment and shipping times.
Building a Visual Quality Control solution in Google Cloud using Vertex AI
In this blog post, we consider the problem of defect detection in packages on assembly and sorting lines. More specifically, we present a real-time visual quality control solution that is capable of tracking multiple objects (packages) on a line, analyzing each object, and evaluating the probability of a defect or damaged parcel. The solution was implemented using Google Cloud Platform (GCP) Vertex AI platforms and GCP AutoML services, and we have made the reference implementation available in our git repository. This implementation can be used as a starting point for developing custom visual quality control pipelines.
Grid Dynamics Acquires Mutual Mobile to Deepen Expertise in Experience Design, Mixed Reality, and Cloud Edge for Global Brands
Grid Dynamics Holdings, Inc. (NASDAQ:GDYN) (Grid Dynamics), a leader in enterprise-level digital transformation services and solutions, today announced the acquisition of Mutual Mobile, an innovation leader that brings digital experiences to life through an integrated approach to design and technology. With around 200 employees across India and North America, Mutual Mobile will contribute significantly to supporting Grid Dynamics’ objective of diversifying its global client base and enabling quality engineering talent to join the company.
Building a Predictive Maintenance Solution Using AWS AutoML and No-Code Tools
In this post, we describe how equipment operators can build a predictive maintenance solution using AutoML and no-code tools powered by Amazon Web Services (AWS). This type of solution delivers significant gains to large-scale industrial systems and mission-critical applications where costs associated with machine failure or unplanned downtime can be high.
To implement a prototype of the RUL model, we use a publicly available dataset known as NASA Turbofan Jet Engine Data Set. This dataset is often used for research and ML competitions. The dataset includes degradation trajectories of 100 turbofan engines obtained from a simulator. Here, we explore only one of the four sub-datasets included, namely the training part of the dataset: FD001.
Detecting anomalies in high-dimensional IoT data using hierarchical decomposition and one-class learning
Automated health monitoring, including anomaly/fault detection, is an absolutely necessary attribute of any modern industrial system. Problems of this sort are usually solved through algorithmic processing of data from a great number of physical sensors installed in various equipment. A broad range of ML-based and statistical techniques are used here. An important common parameter that defines the practical complexity and tractability of the problem is the dimensionality of the feature vector generated from the signals of the sensors.
While there is a great variety of methods and techniques described in ML and statistical literature, it is easy to go in the wrong direction when trying to solve problems for industrial systems with a large number of IoT sensors. The seemingly “obvious” and stereotypical solutions often lead to dead-ends or unnecessary complications when applied to such systems. Here we generalize our experience and delineate some potential pitfalls of the stereotypical approaches. We also outline quite a general methodology that helps to avoid such traps when dealing with IoT data of high dimension. The methodology rests on two major pillars: hierarchical decomposition and one-class learning. This means that we try to start health monitoring from the most elementary parts of the whole system, and we learn mainly from the healthy state of the system.
Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook
Modern manufacturing, transportation, and energy companies routinely operate thousands of machines and perform hundreds of quality checks at different stages of their production and distribution processes. Industrial sensors and IoT devices enable these companies to collect comprehensive real-time metrics across equipment, vehicles, and produced parts, but the analysis of such data streams is a challenging task.
We start with a discussion of how the health monitoring problem can be converted into standard machine learning tasks and what pitfalls one should be aware of, and then implement a reference Vertex AI pipeline for anomaly detection. This pipeline can be viewed as a starter kit for quick prototyping of IoT anomaly detection solutions that can be further customized and extended to create production-grade platforms.
Visual search: how to find manufacturing parts in a cinch
The process of engineering a robust mechanical product, whether it’s an escalator or a car engine, requires many small parts. We accept that these parts wear out over time and require replacement to avoid breakdowns and to keep the mechanics of the product running smoothly.
During our analysis of the data that the client shared with us, we found a mix of photos of the parts themselves, photos of packages or only product labels. Serial numbers or easily distinguishable characters were clearly visible in some photographs, but not in all of them. One of the primary challenges we faced, therefore, was dealing with the differences between the photos the engineers were submitting compared to the images in the search catalog. For example, there were examples of visually indistinguishable images where only the model number differentiated the part, photos of a sticker with a serial number instead of an object itself, rulers alongside objects in photos to indicate scale, and drawings of the part in the catalog instead of photos.
For this use case we implemented the CNN model based on ResNeXt architecture (ResNeXt-50 (32×4d)) pre-trained on an ImageNet dataset. However, the manufacturing parts we were dealing with were not adequately available in the pre-trained dataset, which meant we had to enhance the training dataset with about 10 000 independently sourced manufacturing part images along with the client-supplied labeled dataset.
Multi-agent deep reinforcement learning for multi-echelon supply chain optimization
In this article, we explore how the problem can be approached from the reinforcement learning (RL) perspective that generally allows for replacing a handcrafted optimization model with a generic learning algorithm paired with a stochastic supply network simulator. We start by building a simple simulation environment that includes suppliers, factories, warehouses, and retailers, as depicted in the animation below; we then develop a deep RL model that learns how to optimize inventory and pricing decisions.
Our first step is to develop an environment that can be used to train supply chain management policies using deep RL. We choose to create a relatively small-scale model with just a few products and facilities but implement a relatively rich set of features including transportation, pricing, and competition. This environment can be viewed as a foundational framework that can be extended and/or adapted in many ways to study various problem formulations. Henceforth, we refer to this environment as the World of Supply (WoS).