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In a dynamic environment such as the supply chain, even basic supplier‐customer systems with structurally simple information and material flow formations have a tendency to exhibit operational complexity. The operational complexity of supplier‐customer systems is primarily characterised by the uncertainty of the system. As the operational complexity of a system increases there is an associated increase in the amount of information required to monitor and manage that system. Based on this understanding, a novel information‐theoretic entropy‐based methodology for measuring and analysing the operational complexity of supplier‐customer systems has been developed. This paper makes contributions in the theoretical, conceptual and practical developments of the methodology. The methodology can quantitatively detect and prioritise operational complexity hotspots. At the interface, the framework can identify and quantify the transfer of operational complexity. Within the internal manufacturing system, the framework provides a comparative operational complexity measure across sub‐systems such as flows and products. This entropy‐based methodology provides a tool for identifying and measuring four classes of operational complexity transfer corresponding to the extent to which organisations generate, absorb, export and import operational complexity.

We examine the effect of firm ownership status on three environmentally relevant variables: energy efficiency, toxic emissions, and spending on pollution abatement. Prior research has demonstrated that public firms invest less than private firms and suggests this difference is due pressure from investors to strongly favor short over long-term earnings. We extend this logic to other firm behavior, examining whether publicly owned facilities invest in energy efficiency and pollution reduction differently than privately owned facilities. Using data from the US Census of Manufactures from 1980 to 2009, information on pollution from the Environmental Protection Agency Toxic Release Inventory (TRI) and pollution abatement spending from the Pollution Abatement Costs and Expenditures survey, we find that facilities switching to public ownership are less energy efficient and spend less on pollution abatement than their privately owned counterparts. However, we also find that facilities switching to public ownership have lower toxic emissions than other facilities. We also examine how different sources of external pressures alter these results and find that increased regulatory scrutiny is correlated with increased energy efficiency, toxic emissions, and abatement spending. More concentrated institutional ownership in public firms is associated with lower energy efficiency as is a greater brand focus. These latter results are broadly consistent with the idea that publicly owned firms respond to pressures from investors with a reduced focus on environmentally relevant variables. However, since facilities switching to public ownership have lower toxic emissions, this suggests that there are two competing pressures in publicly owned facilities: cost pressures, consistent with lowered energy efficiency, and public perceptions, consistent with lower toxic emissions, particularly since TRI data became available. In this sense, the combination of ownership and transparency of information appears to influence how firms prioritize different stakeholders.

The purpose of this paper is to discuss complexity assessment in supply chains, to describe a methodology for measuring supply chain complexity in distributive trade and to illustrate the measurement of supply chain complexity and mechanisms to cope with supply chain complexity in distributive trade.

The paper discusses literature on measuring supply chain complexity and uses five case studies among wholesale companies to illustrate a methodology to measure supply chain complexity and to discuss strategies to cope with supply chain complexity.

The study confirms the multifaceted nature of supply chain complexity. The paper identifies eight drivers of supply chain complexity and uses these to illustrate the measurement of supply chain complexity in a wholesale environment. The paper identifies six strategies used by wholesalers for coping with supply chain complexity and identifies interrelations between supply chain complexity drivers and these strategies.

The research is based on case studies in wholesale companies; future research may include survey research, including other sectors to analyze industry differences but may also focus on other parts of the supply chain.

The study provides insights into how to identify and measure complexity in a supply chain and what can be done to manage supply chain complexity.

The measurement approach is new to supply chain management and is based on multiple drivers of supply chain complexity. The research is focused on wholesalers, which is a segment that has received limited academic attention in supply chain research to date.

Changes frequently made to material delivery schedules (MDSs) accumulate upstream in the supply chain (SC), causing a bullwhip effect. This article seeks to elucidate how dynamic complexity generates MDS instability at OEMs in the automotive industry.

An exploratory multiple-case study methodology involved in-depth semistructured interviews with informants at three automotive original equipment manufacturers (OEMs).

Dynamic complexity destabilizes MDSs primarily via internal horizontal interactions between product and process complexities and demand and SC complexities. A network of complexity interactions causes and moderates such instability through complexity absorption and generation and complexity importation and exportation.

The multiple-case study contributes to empirical knowledge about the dynamics of MDS instability. Deductive research to validate the identified relationships remains for Future research.

In revealing antecedents of complexity’s effect on MDS instability, the findings imply the need to develop strategies, programs, and policies dedicated to improving capacity scalability, supplier flexibility, and the flexibility of material order fulfillment.

Building on complexity literature, the authors operationalize complexity transfer and develop a framework for analyzing dynamic complexity in SCs, focusing on complexity interactions. The identification and categorization of interactions provide a granular view of the dynamic complexity that generates MDS instability. The identified and proposed importance of readiness of the SC to absorb complexity challenges the literature focus on external factors for explaining complexity outcomes. The results can be used to operationalize such dynamic interactions by introducing new variables and networks of relationships. Moreover, the work showcases how a complexity perspective could be used to discern the root causes of a complex phenomenon driven by non-linear relationships.

Manufacturing organizations and networks are heavily dependent on the flow of information within and across organization boundaries. A disruption in information flow might interrupt the operations of the organization and make management even more difficult. The purpose of this paper is to incorporate information theory approach to investigate the perturbation introduced into a manufacturing organization as a result of disruption in the flow of critical information needed in manufacturing operations.

This study proposes the use of entropy theory to assess the level of risk introduced by different sources of perturbation into the material flow stream and the use of discrete event simulation to investigate the impact of the resulting disruption on collaborating members.

The result of the analysis carried out on the effect of system failure on supply chain performance revealed that the retailer experiences the most uncertainty in the supply chain while the holding cost constitutes the most unpredictable cost measure when a system failure breach occurs. For the manufacturer and wholesaler, the holding cost is responsible for most of the uncertainty in predicting the impact of the threat on inventory management cost, while the backlog cost holds the highest complexity level for the retailer.

Once this methodology is well developed for use in industrial networks, it can serve as a risk assessment, risk monitoring and risk prediction tool. The paper also calls for a proactive approach to disruption risk management.

This paper proposes a novel approach to assess the impact of information disruption, using entropy theory coupled with simulation methodology.

This paper aims to investigate a workforce policy response to the ageing workforce, emerging social and demographic trends, ongoing and rapid change, the growth in complexity and the changing nature of work.

The main method for constructing this paper is analysis of selected texts rather than the more conventional empirical studies. The approach is that of scenario construction, guided by the criteria established by Nowotny et al. Evidence is gathered of emergent trends that affect management practices and structures now and in the foreseeable future. These trends are identified and woven into a commentary that links them in a focus on the priorities for Australian organizations.

The findings from this research suggest that “command‐based” management structures of the bureaucracy, given emergent trends, present as an impediment to organizational success. Rather, a workforce policy reform agenda for both understanding and managing noted historical conditions in a coherent way is proposed.

The conclusions of this paper are significant for large organizations faced with the dynamism of the modern era and the difficulties associated with emerging demographic and social trends. They suggest a workforce policy reform agenda for both understanding and managing these historical conditions in a coherent way.

This paper deals with a topic that is exercising policy makers in all the developed economies. It offers practical advice to assist the development of workforce policy to better manage emergent trends.

Existing works in the supply chain complexity area have either focused on the overall behavior of multi-firm complex adaptive systems or on listing specific tools and techniques that business units (BUs) can use to manage supply chain complexity but without providing a thorough discussion about when and why they should be deployed. This research aims to address this gap by developing a conceptually sound model, based on the literature, regarding how an individual BU should reduce versus absorb supply chain complexity.

This research synthesizes the supply chain complexity and organizational design literature to present a conceptual model of how a BU should respond to supply chain complexity. The authors illustrate the model through a longitudinal case study analysis of a packaged foods manufacturer.

Regardless of its type or origin, supply chain complexity can arise because of the strategic business requirements of the BU (strategic) or because of suboptimal business practices (dysfunctional complexity). Consistent with the proposed conceptual model, the illustrative case study showed that a firm must first distinguish between strategic and dysfunctional drivers prior to choosing an organizational response. Furthermore, it was found that efforts to address supply chain complexity can reveal other system weaknesses that lie dormant until the system is stressed.

The case study provides empirical support for the literature-derived conceptual model. Nevertheless, any findings derived from a single, in-depth case study require further research to produce generalizable results.

The conceptual model presented here provides a more granular view of supply chain complexity and how an individual BU should respond, than what can be found in the existing literature. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response.

There are no other research works that have synthesized the supply chain complexity and organizational design literature to present a conceptual model of how an individual BU should respond to supply chain complexity. As such, this paper improves the understanding of supply chain complexity effects and provides a basis for future research, as well as guidance for BUs facing complexity challenges.

The purpose of this paper is to further advance an existing supplier evaluation model for the purpose of identifying those supplier relations which predominantly threaten or worsen a company's performance. A defined basic set of parameters to determine complexity facilitates the identification of critical locations within a supply network (SN) under certain business conditions.

This paper is based on a structured literature review in scientific periodicals in logistics/supply chain management between 2000 and 2009. Articles are analysed based on a structured framework and the identified complexity parameters are operationalised using quantitative and summable measures. The conceptual model is applied within a multiple case study in the Austrian agricultural industry.

This paper illustrates how complexity in SNs can be operationalised in a company‐specific configuration in order to achieve concrete managerial recommendations. Hence, the model allows evaluating SN‐partners based on selected parameters to determine the contribution of a single partner to the overall complexity.

Due to the literature review executed and the case study approach chosen, the research may lack generalisability. Therefore, continued validation by means of implementing a greater amount of use cases in other companies and industries is advisable.

Applying the model, a company is able to determine tier‐1 to tier‐n suppliers which are predominantly affecting its business from a complexity perspective.

Unlike typical current complexity evaluation approaches, the proposed model respects rapid and continuous applicability, profound conceptualisation and practical feasibility.

The purpose of this paper is to present the result of using process mining to model the production planning (PP) process of a manufacturing company that is supported by enterprise resource planning (ERP) systems.

This paper uses event logs obtained from the case company’s ERP database. The steps for this research are planning process mining implementation, extraction and construction of event log, discovering process model with Heuristic Miner and analysis.

Process model obtained from process mining shows how the PP is actually conducted. It shows the loop in materials requirement planning and create plan order process. Furthermore, the occurrences of changing plan order date and production line indicate the schedule instability in the case company. Further analysis of the material management (MM) event log shows the implication of production plan changes on MM. Continuous change in the plan affects material allocation priority and may result in a mismatch between production needs and the materials available.

The study is only conducted in a single and specific case. Therefore, even though the findings provide good insight, the use of solitary case study does not imply a general result applied to other cases. Hence, there is a need to conduct similar studies on various cases so that a more generic conclusion can be drawn.

The result provides insights into how the current company’s policy of adjusting the production plan to accommodate changing demand impacts their operation. It can help the company to consider a better balance between flexibility and efficiency to improve their process.

The paper demonstrates the use of process mining to capture the real progression of PP based on the data stored in the company’s ERP database, which give an insight into how a real company conducts their PP process, the implication of schedule instability on MM and production. The novelty of this research lies in the use of process mining to attest to the schedule nervousness issue at a process level.