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This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

This paper investigates the logistics management in the e-grocery sector. It contrasts the key issues faced by practitioners and the topics addressed in the academic literature, to identify potential misalignments between research and practice and propose avenues for future efforts.

This work adopts a twofold methodological approach. From an academic perspective, a systematic literature review (SLR) is performed to define the topics addressed so far by scholars when analysing e-grocery logistics. From a managerial perspective, a Delphi study is accomplished to identify the most significant issues faced by logistics practitioners in the e-grocery context and the associated significance.

The study develops a conceptual framework, identifying and mapping the 9 main logistics challenges for e-grocery along 4 clusters, in the light of a logistics-related revision of the SCOR model: distribution network design (area to be served, infrastructures), order fulfilment process (picking, order storage, consolidation, delivery), logistics-related choices from other domains (product range, stock-out management) and automation. These elements are discussed along three dimensions: criticalities, basic and advanced/automation-based solutions. Finally, the main gaps are identified – in terms of both under-investigated topics (order storage and stock-out management) and investigated topics needing further research (picking and automation) – and research questions and hypotheses are outlined.

This paper provides a threefold contribution, revolving around the developed framework. First, it investigates the state of the art about e-grocery logistics, classifying the addressed themes. Second, it explores the main issues e-grocery introduces for logistics practitioners. Third, it contrasts the two outcomes, identifying the misalignment between research and practice, and accordingly, proposing research directions.

Perishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures.

The inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation.

Product waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects.

To manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products.

This paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors.

This study aims to enhance storage and distribution operations at a pharmaceutical supply center (PSC) in primary health care (PH) using lean health care (LH) tools. Supply centers for health products, medications and supplies have unique characteristics compared to centers for other goods due to complex processes, specific services, diverse stakeholders and multiple interactions. The authors adapt LH tools to address these complexities and meet industry-specific needs.

The investigation unit is a PSC in a large southern Brazilian city, and the processes analyzed are the storage and distribution of medications. The authors performed action research from June 2019 to February 2020. Data collection and problem diagnosis involved the development of a value stream mapping.

The authors adapted the overall equipment effectiveness calculation, efficiency analysis, and loss classification for PSC operations. Eighteen core issues were found: waiting, movement, transport, stock, inadequate processing, defects and human potential losses. The authors proposed waste reduction tools and practices. Inadequate storage conditions may compromise medicine quality, efficacy and safety. This can result from lacking physical structures or noncompliance with procedures. Next, the authors recommend simulating scenarios for validation before implementation.

The study explored ways to enhance layout and medicine distribution at the PSC, focusing on reducing loss and cost impact.

Originality lies in LH application in a PSC of PH, often applied in secondary or tertiary health levels like hospitals. The novelty necessitated adaptations of tools for future PSC applications.

The rapid growth in volume and value of on-demand restaurant food delivery, accelerated by the COVID-19 pandemic, is causing a paradigm shift in the food service sector. However, there is a lack of hospitality management research into this emerging phenomenon. To address this gap, this paper defines and develops a novel conceptual model and typology and proposes a research agenda for ghost production in the context of food service.

This paper uses the Servuction model to explore, define and model the radical separation between food service production sites, points of sale and consumer interaction from the perspective of on-demand restaurant food delivery. A novel typology is developed and illustrated with eight industry examples from the UK and an accompanying cost benefit analysis. Future research priorities are identified.

In the hospitality literature, little attention has been paid to changes on-demand restaurant food delivery brings to production and business models of food service organisations, resulting in significant gaps between food service practice and theory. The knock-on effects to stakeholders include increased convenience for customers, uncertain employment status of riders and, for restaurants, striking a balance between capturing new markets and losing control of the customer. Additionally, for aggregators, there is a lack of profitability in existing models, despite holding the balance of power (and data).

The concept of “ghost production” and its associated typology is novel and offers a contribution to hospitality management literature by defining the term, scope and scale of this new phenomenon. Practical implications are proposed.

The purpose of this article is to solve the issue that apple-picking robots are easily interfered by branches or other apples near the target apple in an unstructured environment, leading to grasping failure and apple damage.

This study introduces the system units of the apple-picking robot prototype, proposes a method to determine the apple-picking direction via 3D point cloud data and optimizes the path planning method according to the calculated picking direction.

After the field experiments, the average deviation of the calculated picking direction from the desired angle was 11.81°, the apple picking success rate was 82% and the picking cycle was 11.1 s.

This paper describes a picking control method for an apple-picking robot that can improve the success and reliability of picking in an unstructured environment and provides a basis for automated and mechanized picking in the future.

After completion of the case study, the students will be able to examine the service failures faced by Nykaa; propose a resolution to the service failures faced by Nykaa through service recovery strategies; analyse the service quality gaps faced by Nykaa and propose strategies to bridge the gaps; learn and comprehend more about business models in e-commerce; and evaluate the e-commerce business models for deciding the best fit for Nykaa.

“Nykaa” was the brainchild of Falguni Nayar, who was an IIM alumna and had worked with the Kotak Mahindra Group for nearly 20 years as a venture investor and merchant banker. After representing the group’s global operation in the UK and the USA, she became the head of the institutional equities division. In 2005, she was the Managing Director of Kotak Mahindra Bank’s project. Nayar had reached the pinnacle of her career, but something was troubling her. In 2012 when Nayar noticed anomalies in India’s beauty and personal care market, her goals were realised. Unlike in other nations such as Japan or France, the availability of beauty products in India was limited, despite significant demand, owing to product unavailability in many areas. Nayar founded “Nykaa”, an online portal for multi-beauty, personal care and well-being items that also later branched out into fashion. However, after 10 years of its establishment, Nykaa was facing challenges regarding services outages, casting doubts over Nykaa’s business model practiced by it for the past 10 years.

This case is intended for discussion in the undergraduate, postgraduate management courses and executive MBA courses with marketing specialisation, services marketing and strategic marketing.

Teaching notes are available for educators only.

CSS 11: Strategy

The purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.

A multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.

Study results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.

This is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.