Slow-moving goods are common in many retail settings and occupy a vast part of retail shelves. Since stores sell these products irregularly and in small quantities, the replenishing distribution center may only place batched orders with manufacturers every few weeks. While order quantities are often fixed, the challenge for manufacturers facing such intermittent demand is to forecast the order timing. In this paper, we explore the value of Point-of-Sales (PoS) data to improve a food manufacturer’s order timing forecast for slow-moving goods. We propose an inventory modeling approach that uses the last order, PoS data from retail stores, and the expected lead time demand to estimate the retailer’s channel inventory. With this dynamic estimate, we can ‘nowcast’ the retailer’s inventory and predict his next order. To illustrate our methodology, we first conduct an experimental simulation and compare our results to a Croston variant and a moving average model. Next, we validate our approach with empirical data from a small German food manufacturer that serves a grocery retailer with a central distribution center and 53 hypermarkets. We find that, on average, our approach improves the accuracy of order-timing predictions by 10–20 percent points. We overcome a shrinkage-induced bias by incorporating an inventory correction factor. Our approach describes a new way of utilizing PoS data in multi-layered distribution networks and can complement established forecasting methods such as Croston. Particular applications arise when the order history is short (e.g., product launch) or represents a bad predictor for future demand (e.g., during COVID-19).

Many new ventures position themselves along the additive manufacturing (AM) value chain to benefit from the quickly maturing technology. Yet, their business models and sources of value creation are largely unidentified. We compile a unique dataset with 160 entrepreneurial AM-firms using a leading crowd-sourced database and organize the data in a card file system. We code the data along multiple dimensions and apply a Latent Class Analysis to identify unique segments of firms that focus on different complementary activities along the AM value chain. By using the NICE framework, we additionally reveal the value creation mechanisms of each identified class. We identify four unique segments of AM-firms that focus on different complementary activities: (i) hardware providers, (ii) software and data experts, (iii) full-service providers, and (iv) manufacturing orchestrators. While a lot of value creation across these segments is currently still driven by novelty and innovation, AM-firms also introduce lock-in and complementary products and services to capture value beyond production. In characterizing value chain structures, we outline how firms can position themselves in this emerging industry.

Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.

Computational traceback models are important tools for investigations of widespread food-borne disease outbreaks as they help to determine the causative outbreak location and food item. In an attempt to understand the entire food supply chain from farm to fork, however, these models have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in their home location. This paper aims to fill this gap by modelling food-flows from supermarkets to consumers in a large-scale gravity model for Hesse, Germany. Modelling results show that on average, groceries are sourced from two to four postal zones with half of all goods originating from non-home postal zones. The results contribute to a better understanding of the last link in the food supply chain. In practice, this allows investigators to relate reported outbreak cases with sourcing zones and respective food-retailers. The inclusion of this information into existing models is expected to improve their performance.