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The paper aims to empirically investigate how demand variability impacts product category inventory levels and stockout rates of retail firms. Additionally, the moderating effect of product variety on these performance metrics is explored.

Using data from 78 individual retail stores of a single firm located in China, the authors develop a three stage least squares regression model to examine the simultaneous impact of product variety and demand variability on product inventory levels and stockout rates.

The results indicate that product variety and demand variability both negatively influence product category inventory levels and stockout rates. However, contrary to results for manufacturing or distributor environments, product variety dampens the negative relationship between demand variability and inventory.

For products or categories with a high amount of demand variability, retailers can leverage more product variety to help improve their inventory performance. This is likely due to product substitution behaviors.

The authors show that previously examined relationships between product variety, demand variability, and firm performance are different in the retail environment than in the manufacturing or distributor context.

The purpose of this paper is to explore different aggregate production planning (APP) strategies (inventory levelling, validation of the workforce and flexible production alternatives: overtime and/or outsourcing) by using a system dynamics model in a two-level, multi-product, multi-period manpower intensive supply chain (SC). Therefore, the appropriateness of using systems dynamics as a research method, by focusing on managerial applications, to analyse APP policies is proven. From the combination of systems dynamics and APP, recommendations and action strategies are considered for each scenario to understand how the system performs and to improve decision making on APP in the SC context.

The research design analyses a typical factory setting with representative parameter settings for five different conventional APP policies – inventory levelling, workforce variation, overtime, outsourcing and a combination of overtime and outsourcing – through deterministic systems dynamics-based simulation. In order to validate the simulation model, the results from published APP models were replicated. Then, optimisation is conducted for this deterministic setting to determine the performance of all these typical policies with optimal parameter settings. Next, a Monte Carlo stochastic simulation is used to assess the robustness of such performances in a variety of demand settings. Different aggregate plans are tested and the effect that events like demand variability and production times have on the SC performance results is analysed.

The results support the assertion that the greater the demand variability, the higher the flexibility costs (overtime, outsourcing, inventory levelling, and contracts and firings). As greater inter-month oscillations appear, which must be covered with additional alternatives, the optimum number of employees must be determined by analysing the interchanges and marginal costs between capacity oversizing costs (wages, idle time, storage) and the costs to undersize it (penalties for lowering safety stocks, delayed demand, greater use of overtime and outsourcing). Accordingly, controlling the times to avoid increased costs and penalties incurred by delayed demand becomes an essential important task, but one that also depends on the characteristics of this variability.

This paper has developed a modelling approach for APP in a manpower intensive SC by applying system dynamics. It includes a simulation model, the analysis of several scenarios, the impact on performance caused by variability events in the parameters, and some recommendations and action strategies to be subsequently applied. The modelling methodology proposed can be employed to design-specific models for each SC.

This paper proposes an APP system dynamics approach in a two-level, multi-product, multi-period manpower intensive SC for the first time. This model bridges the gap in the literature relating to simulation, specifically system dynamics and its application for APP. The paper also provides a qualitative description of the various pros and cons of each analysed policy and how they can be combined.

The demand amplification effect has been well described in the literature over many years. This paper reports the results of a research project carried out in the UK automotive component supply chain, which set out to eliminate the effect in practice. The paper describes the progression from the identification of demand amplification, through a practical approach for its measurement, to the development of a methodology to overcome the negative impacts of the effect across three echelons of the supply chain. The results of a six‐month trial implementation are reported.

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.

The purpose of this paper is to present a dynamic model to measure the degree of system’s leanness under dynamic demand conditions using a novel integrated metric.

The multi-stage production system model is based on a system dynamics approach. The leanness level is measured using a new developed integrated metric that combines efficiency, WIP performance as well as service level. The analysis includes design of experiment technique at the initial analysis to examine the most significant parameters impacting the leanness score and then followed by examining different dynamic demand scenarios. Two scenarios were examined: one focussed low demand variation with various means (testing the impact of demand volumes) while the second focussed on high demand variation with constant means (testing the impact of demand variability).

Results using the data from a real case study indicated that given the model parameters, demand rate has the highest impact on leanness score dynamics. The next phase of the analysis thus focussed on investigating the effect of demand dynamics on the leanness score. The analysis highlighted the different effects of demand variability and volumes on the leanness score and its different components leading to various demand and production management recommendations in this dynamic environment.

The presented lean management policies and recommendations are verified within the scope of similar systems to the considered company in terms of manufacturing settings and demand environment. Further research will be carried to extend the dynamic model to other dynamic manufacturing and service settings.

The developed metric can be used not only to assess the leanness level of the systems which is very critical to lean practitioners but also can be used to track lean implementation progress. In addition, the presented analysis outlined various demand management as well as lean implementation policies that can improve the system leanness level and overall performance.

The presented research develops a novel integrated metric and adds to the few literature on dynamic analysis of lean systems. Furthermore, the conducted analysis revealed some new aspects in understanding the relation between demand (variability and volume) and the leanness level of the systems. This will aid lean practitioners to set better demand and production management policies in today’s dynamic environment as well as take better decisions concerning lean technology investments.

Increasing complexity and scale in hospitality company operations combined with intensifying competition in a maturing market is creating an unstable and rapidly changing strategic and operational environment for the hospitality industry. Such instability often leads to the generation of unnecessary and undesirable variety in organisational structures, processes and products. The consequence of this is frequently an increase in complexity for the hospitality operations manager. This issue is addressed through an analysis of the sources of variability and a consideration of potential techniques to avoid, reduce or eliminate the incidence of this instability as a means to reduce complexity and enhance performance.

In many industrial contexts, firms are encountering increasingly uncertain demand. Numerous factors are driving this phenomenon; however, a major change that is spreading among different sectors is the ever‐growing attention to customers. Companies have identified that customers are critical not only because they directly influence the success of specific products or firms, but also because they play a fundamental role in many internal processes. Although the role of customers in business processes has been deeply analysed, the issue of demand forecasting and the role of customers has not been fully explored. The present study aims to examine the impact of heterogeneity of customer requests on demand forecasting approaches, based on three action research cases. Based on the analysis of customer behaviour, an appropriate methodology for each case is designed based on clustering customers according to their demand patterns.

Objectives are achieved by means of three action research case studies, developed in cooperation with three different companies. The paper structures a general methodology based on these three experiences to help managers in better dealing with uncertain demand.

By means of proper analysis of customers' heterogeneity and by using simple statistical techniques such as cluster analysis, forecasting performance can significantly improve. In these terms, this work claims that focusing on customers' heterogeneity is a relevant topic both for practitioners and researchers.

The paper proposes some specific guidelines to forecast demand where customers' differences impact significantly on demand variability. In these terms, results are relevant for practitioners. Moreover, the paper claims that this issue should be better analysed in future researches and proposes some guidelines for future works.

The purpose of this paper is to empirically examine the impact of supply chain risks (SCRs) and demand management strategies (DMSs) on the company performance in order to study the use of DMSs in delivering improved results even in the presence of SCRs. The SCRs considered under the study are as follows: demand variability, constrained capacity and quality of services delivery, and competitive performance, customer satisfaction and financial performance are the measures considered for company performance.

This study is based on a survey of 439 businesses in India representing 10 groups of services industries (information technology/IT enabled services, business process outsourcing, IT infrastructure, logistics/transportation, healthcare, hospitality, personal services, consulting, education and training, consumer products and retail), using structural equation modeling (SEM) methods.

The findings reveal that presence of demand variability risk has significant influence upon the use of demand planning and forecasting, controlling customer arrival during peaks and shifting demand to future. Mismatch of capacity against demand (unused capacity) leads to the use of techniques to influence business during lean periods, thereby resulting in enhanced supply chain (SC) and financial performance. Controlling customer arrival during peaks to shift the demand to lean periods leads to enhanced financial performance. Presence of delivery quality risk does not significantly influence the use of DMS. Also, short-term use of customer and business handling techniques does not exert significant influence on company performance.

The study has limitations as follows: (1) respondents are primarily from India while representing global organizations, (2) process/service redesign to relieve capacity as a DMS is not considered and (3) discussion on capacity management strategies (CMSs) is also excluded.

SC managers can be resourceful in shifting the peak demand to future with the application of techniques to control customer arrival during peaks. The managers can also help enhance business by influencing business through offers, incentives and promotions during lean periods to use available capacity and improve company performance.

This study is one of the first empirical works to explore how presence of SCRs influences the use of DMS and impacts the three types of company performance. The study expands current research on demand management options (DMOs) by linking three dimensions of company performance based on the data collected from ten different groups of service industry.

This paper seeks to describe a conceptualisation of the multiple facets of the bullwhip effect between stocking levels within and between value chains and value systems.

The paper provides a conceptual discussion of the bullwhip effect. It is refined and re‐defined.

The bullwhip effect has usually been explored between inter‐organisational stocking levels. Recently, it has also been explored within intra‐organisational stocking levels. A broader descriptive framework is introduced, one that positions the bullwhip effect construct in intra‐ and inter‐organisational, as well as intra‐ and inter‐channel, stocking levels in and between value chains and value systems.

A research agenda is provided that goes beyond current definitional boundaries and state‐of‐the‐art research of the bullwhip effect.

The refined and re‐defined bullwhip effect is of interest to practitioners. It considers inter‐organisational and intra‐organisational stocking levels. In addition, it considers intra‐ and inter‐channel stocking levels. It is of great concern to achieve best practices in business.

The principal contributions are – a dynamics model of the bullwhip effect construct; a principle of stocking level variability; a typology of stocking level variability; a framework that describes different levels of analysis of the bullwhip effect; and a re‐definition of the bullwhip effect construct – within or between value chains and value systems.

Increased demand variability in supply chains (the bullwhip effect) has been discussed in the literature. The practical measurement of this effect, however, entails some problems that have not received much attention in the literature and that have to do with the aggregation of data, incompleteness of data, the isolation of demand data for defined supply chains that are part of a greater supply web. This paper discusses these conceptual measurement problems and discusses experiences in dealing with some of these problems in an industrial project. Also presents empirical results of measurements of the bullwhip effect in two supply chains.