🧠 SpartanNash Leveraging AI Technology to Predict Shopper Demand, Decrease Waste
Food solutions company SpartanNash is expanding its use of Upshop’s Magic™ inventory and replenishment optimization application, to consolidate its ordering systems, maintain planogram integrity and assist with merchandising reset planning in both the center store and the produce department. The system also provides store Associates with a real-time, comprehensive view of the store’s stock to maximize assortment for guests, while at the same time reducing waste. The Magic application is specifically designed for the grocery industry, reviewing seasonal trends, promotional activity, display allocation and real-time sales data to offer actionable insights that enhance inventory accuracy and guest satisfaction
Part Level Demand Forecasting at Scale
The challenges of demand forecasting include ensuring the right granularity, timeliness, and fidelity of forecasts. Due to limitations in computing capability and the lack of know-how, forecasting is often performed at an aggregated level, reducing fidelity.
In this blog, we demonstrate how our Solution Accelerator for Part Level Demand Forecasting helps your organization to forecast at the part level, rather than at the aggregate level using the Databricks Lakehouse Platform. Part-level demand forecasting is especially important in discrete manufacturing where manufacturers are at the mercy of their supply chain. This is due to the fact that constituent parts of a discrete manufactured product (e.g. cars) are dependent on components provided by third-party original equipment manufacturers (OEMs). The goal is to map the forecasted demand values for each SKU to quantities of the raw materials (the input of the production line) that are needed to produce the associated finished product (the output of the production line).
How Walmart Uses Apache Kafka for Real-Time Replenishment at Scale
Real-time inventory planning has become a must for Walmart in the face of rapidly changing buyer behaviors and expectations. But real-time inventory is only half of the equation. The other half is real-time replenishment, which at a high level, we define as the way we can fulfill the inventory demand at every physical node in the supply chain network. As soon as inventory gets below a certain threshold, and based on many other supply chain parameters like sales forecast, safety stock, current availability of the item at node and its parents, we need to automatically replenish that item in a way that optimizes resources and increases customer satisfaction.
On any given day, Walmart’s real-time replenishment system processes more than tens of billions of messages from close to 100 million SKUs in less than three hours. We leverage an array of processors to generate an order plan for the entire network of Walmart stores with great accuracy and at high throughputs of 85GB messages/min. While doing so, it also ensures there is no data loss through event tracking and necessary replays and retries.
Production Planning Template for Excel (Free Download)
A production plan is a guide that outlines every step involved in the production of a good. With production planning, the production process is more organized and efficient, and there is less of a risk of missing an important step that stops or slows down production. These plans help optimize the manufacturing process, which makes the design, production, and delivery of goods much easier to plan out.
An Excel template can work as a jumping-off point, allowing your business to build around your own goals and needs without starting from scratch. Keep in mind that a template should be flexible enough to allow for personalization while also being rigid enough that you don’t miss vital steps. The Anvyl production planning template was built with this mentality: to help manufacturers improve their processes and create a better production planning experience with a unique, customizable approach.
Price optimization notebook for apparel retail using Google Vertex AI
One of the key requirements of a price optimization system is an accurate forecasting model to quickly simulate demand response to price changes. Historically, developing a Machine Learning forecast model required a long timeline with heavy involvement from skilled specialists in data engineering, data science, and MLOps. The teams needed to perform a variety of tasks in feature engineering, model architecture selection, hyperparameter optimization, and then manage and monitor deployed models.
Vertex AI Forecast provides advanced AutoML workflow for time series forecasting which helps dramatically reduce the engineering and research effort required to develop accurate forecasting models. The service easily scales up to large datasets with over 100 million rows and 1000 columns, covering years of data for thousands of products with hundreds of possible demand drivers. Most importantly it produces highly accurate forecasts. The model scored in the top 2.5% of submissions in M5, the most recent global forecasting competition which used data from Walmart.
The evolution of Amazon’s inventory planning system
Forecasting models developed by Amazon’s Supply Chain Optimization Technologies organization predict the demand for every product. Buying systems determine the right level of product to purchase from different suppliers, while large-scale placement systems determine the optimal location for products across the hundreds of facilities belonging to Amazon’s global fulfillment network.
“In 2016, Amazon’s supply chain network was designed for scenarios where inventory from any fulfillment center could be shipped to any customer to meet a two-day promise,” said Salal Humair, senior principal research scientist at Amazon who has been with the company for seven years. This design was inadequate for the new world in which Amazon was operating; one shaped by what Humair calls the “globalization-localization imperative.”
A new multi-echelon inventory system developed by SCOT (a project whose roots stretch back to 2016) is a significant break from the past. The heart of the model is a multi-product, multi-fulfillment center, capacity-constrained model for optimizing inventory levels for multiple delivery speeds, under a dynamic fulfillment policy. The framework then uses a Lagrangian-type decomposition framework to control and optimize inventory levels across Amazon’s network in near real-time.
Broadly speaking, decomposition is a mathematical technique that breaks a large, complex problem up into smaller and simpler ones. Each of these problems is then solved in parallel or sequentially. The Lagrangian method of decomposition factors complicated constraints into the solution, while providing a ‘cost’ for violating these constraints. This cost makes the problem easier to solve by providing an upper bound to the maximization problem, which is critical when planning for inventory levels at Amazon’s scale.
The history of Amazon’s forecasting algorithm
Historical patterns can be leveraged to make decisions on inventory levels for products with predictable consumption patterns — think household staples like laundry detergent or trash bags. However, most products exhibit a variability in demand due to factors that are beyond Amazon’s control.
Today, Amazon’s forecasting team has drawn on advances in fields like deep learning, image recognition and natural language processing to develop a forecasting model that makes accurate decisions across diverse product categories. Arriving at this unified forecasting model hasn’t been the result of one “eureka” moment. Rather, it has been a decade-plus long journey.
Bionic Demand Planning: A more connected company to a changing world.
Garvis, the first bionic demand planning system, puts planners at the heart of a future of demand planning that focuses on: Multi-tenant architecture, User trainable, Whitebox AI that learns along with the planner to identify unseen insights and decrease forecast error by up to 40%, Multi-factor modelling that provides actionable insights at an enterprise level, No external implementation leading to evergreen systems informed by a semantic user-layer. Reducing cost and increasing decision making efficiency.
How Instacart fixed its A.I. and keeps up with the coronavirus pandemic
Like many companies, online grocery delivery service Instacart has spent the past few months overhauling its machine-learning models because the coronavirus pandemic has drastically changed how customers behave.
Starting in mid-March, Instacart’s all-important technology for predicting whether certain products would be available at specific stores became increasingly inaccurate. The accuracy of a metric used to evaluate how many items are found at a store dropped to 61% from 93%, tipping off the Instacart engineers that they needed to re-train their machine learning model that predicts an item’s availability at a store. After all, customers could get annoyed being told one thing—the item that they wanted was available—when in fact it wasn’t, resulting in products never being delivered. ‘A shock to the system’ is how Instacart’s machine learning director Sharath Rao described the problem to Fortune.
The algorithms big companies use to manage their supply chains don’t work during pandemics
Even during a pandemic, Walmart’s supply chain managers have to make sure stores and warehouses are stocked with the things customers want and need. COVID-19, though, has thrown off the digital program that helps them predict how many diapers and garden hoses they need to keep on the shelves.
Normally, the system can reliably analyze things like inventory levels, historical purchasing trends, and discounts to recommend how much of a product to order. During the worldwide disruption caused by the COVID-19 pandemic, the program’s recommendations are changing more frequently. “It’s become more dynamic, and the frequency we’re looking at it has increased,” a Walmart supply chain manager, who asked not to be named because he didn’t have permission to speak to the media, told The Verge.