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Register a new userUnderstanding Retail Productivity by Simulating Management Practise
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Added by
Uwe Aickelin
Publication date
2008
fields
Informatics Engineering
and research's language is
English
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Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents could offer potential for fostering sustainable organizational capabilities in the future. Our research so far has led us to conduct case study work with a top ten UK retailer, collecting data in four departments in two stores. Based on our case study data we have built and tested a first version of a department store simulator. In this paper we will report on the current development of our simulator which includes new features concerning more realistic data on the pattern of footfall during the day and the week, a more differentiated view of customers, and the evolution of customers over time. This allows us to investigate more complex scenarios and to analyze the impact of various management practices.
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Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents could offer potential for fostering sustainable organizational capabilities in the future. The project is still at an early stage. So far we have conducted a case study in a UK department store to collect data and capture impressions about operations and actors within departments. Furthermore, based on our case study we have built and tested our first version of a retail branch simulator which we will present in this paper.
We apply Agent-Based Modeling and Simulation (ABMS) to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents do offer potential for developing organizational capabilities in the future. Our multi-disciplinary research team has worked with a UK department store to collect data and capture perceptions about operations from actors within departments. Based on this case study work, we have built a simulator that we present in this paper. We then use the simulator to gather empirical evidence regarding two specific management practices: empowerment and employee development.
Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail performance. Despite the fact we are working within a relatively novel and complex domain, it is clear that using an agent-based approach offers great potential for improving organizational capabilities in the future. Our multi-disciplinary research team has worked closely with one of the UKs top ten retailers to collect data and build an understanding of shop-floor operations and the key actors in a department (customers, staff, and managers). Based on this case study we have built and tested our first version of a retail branch agent-based simulation model where we have focused on how we can simulate the effects of people management practices on customer satisfaction and sales. In our experiments we have looked at employee development and cashier empowerment as two examples of shop floor management practices. In this paper we describe the underlying conceptual ideas and the features of our simulation model. We present a selection of experiments we have conducted in order to validate our simulation model and to show its potential for answering what-if questions in a retail context. We also introduce a novel performance measure which we have created to quantify customers satisfaction with service, based on their individual shopping experiences.
There is no doubt that management practices are linked to the productivity and performance of a company. However, research findings are mixed. This paper provides a multi-disciplinary review of the current evidence of such a relationship and offers suggestions for further exploration. We provide an extensive review of the literature in terms of research findings from studies that have been trying to measure and understand the impact that individual management practices and clusters of management practices have on productivity at different levels of analysis. We focus our review on Operations Management (om) and Human Resource Management (hrm) practices as well as joint applications of these practices. In conclusion, we can say that taken as a whole, the research findings are equivocal. Some studies have found a positive relationship between the adoption of management practices and productivity, some negative and some no association whatsoever. We believe that the lack of universal consensus on the effect of the adoption of complementary management practices might be driven either by measurement issues or by the level of analysis. Consequently, there is a need for further research. In particular, for a multi-level approach from the lowest possible level of aggregation up to the firm-level of analysis in order to assess the impact of management practices upon the productivity of firms.
In the next decade, transient searches from the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope will increase the sample of known Type Ia Supernovae (SN Ia) from $sim10^3$ to $10^5$. With this reduction of statistical uncertainties on cosmological measurements, new methods are needed to reduce systematic uncertainties. Characterizing the underlying spectroscopic evolution of SN Ia remains a major systematic uncertainty in current cosmological analyses, motivating a new simulation tool for the next era of SN Ia cosmology: Build Your Own Spectral Energy Distribution (BYOSED). BYOSED is used within the SNANA framework to simulate light curves by applying spectral variations to model SEDs, enabling flexible testing of possible systematic shifts in SN Ia distance measurements. We test the framework by comparing a nominal Roman SN Ia survey simulation using a baseline SED model to simulations using SEDs perturbed with BYOSED, and investigate the impact of neglecting specific SED features in the analysis. These features include semi-empirical models of two possible, predicted relationships: between SN ejecta velocity and light curve observables, and a redshift-dependent relationship between SN Hubble residuals and host galaxy mass. We analyze each BYOSED simulation using the SALT2 & BBC framework, and estimate changes in the measured value of the dark energy equation-of-state parameter, $w$. We find a difference of $Delta w=-0.023$ for SN velocity and $Delta w=0.021$ for redshift-evolving host mass when compared to simulations without these features. By using BYOSED for SN Ia cosmology simulations, future analyses (e.g., Rubin and Roman SN Ia samples) will have greater flexibility to constrain or reduce such SN Ia modeling uncertainties.
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