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This study aims to investigate how to identify and address production levelling problems in assembly lines utilising an intensive manual workforce when higher productivity levels are urgently requested to meet market demands.

A mixed-methods approach was used in the research design, integrating case study analysis, interviews and qualitative/quantitative data collection and analysis. The methodology implemented also introduces to the literature on operational performance a novel combination of data analysis methods by introducing the use of the Natural Language Understanding (NLU) methods.

First, the findings unveil the impacts on operational performance that transportation, limited documentation and waiting times play in assembly lines composed of an intensive workforce. Second, the paper unveils the understanding of the role that a limited understanding of how the assembly line functions play in productivity. Finally, the authors provide actionable insights into the levelling problems in manual assembly lines.

This research supports industries operating assembly lines with intensive utilisation of manual workforce to improve operational performance. The paper also proposed a novel conceptual model prescriptively guiding quick and long-term improvements in intensive manual workforce assembly lines. The article assists industrial decision-makers with subsequent turnaround strategies to ensure higher efficiency levels requested by the market.

The paper offers actionable findings relevant to other manual assembly lines utilising an intensive workforce looking to improve operational performance. Some of the methods and strategies examined in this study to improve productivity require minimal capital investments. Lastly, the study contributes to the empirical literature by identifying production levelling problems in a real context.

This study aims to devise generalized unconstrained optimization models for ascertaining the optimal level of product quality and production capacity level by modeling both product price and production cost as a function of product quality. Further, interrelations among investment for quality, product quality and production volume are considered. This study contributes toward the extant research, in that nuances related to price, production volume, and product quality are fused together such that two broad operational strategies of product quality optimization and production capacity optimization can be contrasted.

To achieve the research objectives, the authors evolve unconstrained optimization models such that optimal product quality level and optimal production capacity level can be obtained employing the principles of differential calculus aimed at maximizing the manufacturer's profit. Specifically, nuances related to quality technology and efficiency, and quality loss cost has also been integrated in the integrated model. Thereafter, employing numerical analysis for a generalized product, the detailed workings of evolved models are demonstrated. The authors further carry out the sensitivity analysis to understand the impact of investment for quality onto the manufacturer's profit for both operational strategies.

The research demonstrates that the manufacturer would be better off adopting production capacity optimization strategy as an operational policy, as opposed to product quality optimization policy for the manufacturer's profit maximization. Further, considering the two operational strategies, the manufacturer does not obtain the highest possible theoretical profit when pertinent variables (product quality and production capacity) are set at highest possible theoretical level. This research discusses that in low-volume and high-margin products, it might be useful to adopt a product quality optimization strategy as a production capacity optimization strategy results in significantly high quality loss cost.

The findings of our study have a significant implication for industries such as steel-making, cement production, automotive industry wherein the conventional wisdom dictates that higher level of production capacity utilization always results in higher level of revenues. However, the authors deduce that beyond certain production capacity utilization, striving for higher utilization does not fetch additional profit. This work also adds to the extant research literature, in that it integrates the nuances of product quality, production volume and pricing in an integrative manner.

The authors analyse a growth model to explain how economic fluctuations are primarily driven by productive capacities (i.e. capacity utilization driven by innovations and know-how) and productive inefficiencies.

This study’s methodology consists of the combination of the economic growth model, à la Solow–Swan, with a sigmoidal production function (in capital), which may explain growth, poverty traps or fluctuations depending on the relative levels of inefficiencies, productive capacities or lack of know-how.

The authors show that economies may experience economic growth, poverty traps and/or fluctuations (i.e. cycles). Economic growth is reached when an economy experiences both a low level of inefficiencies and a high level of productive capacities while an economy falls into a poverty trap when there is a high level of inefficiencies in production. Instead, the economy gets in cycles when there is a large level of the lack of know-how and low levels of productive capacity.

The authors conclude that more capital per capita (greater savings and investment) and greater productive capacity (with less lack of know-how) are the economic policy keys for an economy being on the path of sustained economic growth.

The purpose of this paper is to analyze Toyota's production levelling process in an attempt to understand the various social and technical factors required to produce to a changing market. Unfortunately, most outsiders who explore production levelling do not realize that it involves various departments outside of manufacturing. Consequently, due to the dynamic nature of production levelling many unintended social and management factors between departments makes cooperation difficult.

This paper conducts a case study at one of Toyota's automobile plants to examine the level of departmental and social integration that is applied when implementing production levelling. Based on observations, the problems of production levelling are analyzed and, accordingly, possible solutions are explored.

The findings of this work show that Toyota achieves production levelling because it is viewed as a company‐wide activity that cuts across many departments in promoting manufacturing consistency. Production levelling criteria include both the design and manufacturing aspects which brings evidence that manufacturing is limited in its ability to eliminate and reduce market fluctuation. The work also illustrates that Toyota reinforces departmental cooperation through its human resources policies, and many other unique management mechanisms.

The paper provides new insight on how Toyota achieves production levelling by considering a more holistic and social‐technical approach. In particular, interdepartmental activities are emphasized in achieving company‐wide goals that impact how departments agree to operate.

The theory of self‐organized criticality (SOC) suggests that interdependencies and interactions among components of a system cause the system to perpetually organize itself towards a critical state. A small change in the value of any component of the system can affect the entire system (like a domino effect). Using SOC theory, we develop hypotheses to associate changes in a firm's production level and its stock prices. Change in performance and stock prices is theorized to vary positively with change in production below the critical level of production (called sub‐critical production), and vary negatively above the critical level of production (called supra‐critical production). Increasing (decreasing) economies of scale operate during sub (supra) critical levels of production. Change in production level from either the sub‐critical or supra‐critical level is posited to take the firm towards the critical production level. There are two reasons to investigate changes in production level: first, prior market research has not fully explained changes in stock prices, and, second, production (as modeled in microeconomic theory as a system of interacting input factors) provides an appealing case to investigate its SOC character. If presence of SOC‐like behavior for production process is observed, then statistical properties of critical states of production can be studied to provide better prediction abilities. Market evaluations of production‐related changes imply that change in production is a fundamental economic triggering process that can explain variations in stock prices. The results, based on analyzing 40 quarters of data, generally support the hypotheses that change in stock prices are associated with changes in production level, and that stock prices fall (rise) when the firm operates in supra (sub) critical production levels.

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 purpose of this paper is to develop alternative analytical measures for the degree of operating leverage (DOL) that reflect the impact of uncertain demand shocks in the product's market on optimal production levels, sales and profits of the firm.

The elasticity measures are constructed according to a theoretical formulation of optimal production level that corresponds to demand shocks for given predetermined levels of fixed cost.

The paper suggests two main findings. First, the analytical marginal DOL is at least twice the traditional DOL depending on the structure of the shock, the production function and demand's elasticity. The traditional DOL is equal to the measure only when large-scale negative demand prompts the firm to abandon production. Second, the paper also provides an analytical measure of DOL in terms of elasticity of profit to sales rather than to production level. Both theoretically and empirically elasticity of profit to sales can be better measured and better reflects risk.

This paper should be extended to encompass multiple shocks on demand and supply while investigating the empirical multi variants distribution of the shocks.

The model can be used by managers who are well informed about the fixed and variable costs of their firm. The model determines the mean profit- risk trade off which is an important factor in all investment decision problems.

Surprisingly and according to the best knowledge, this paper is the first attempt in the literature for alternative analytical DOLs’ formulations that is coherent with basic economic theories of optimal production level under risk.

The concept of coopetition is founded on the complementarity‐based nature of this strategy. However, coopetition research has devoted relatively little attention to complementarity issues and their impact on coopetition results. By bridging the coopetition and economics of complementarities research fields, we develop a model representing a classical optimization problem in complementarities as applied to coopetition in order to evaluate potential risks deriving at an operational level from implementing a coopetition strategy. The model we develop is a situated one and is based on empirical data from a longitudinal case study of coopetition in the mineral water and soft drinks industry. The results highlight a potential risk of coopetition strategies – namely, thresholds effects – as well as the associated risks a wrong understanding of complementarities in a coopetition setting may entail.

In the multilayered capability framework the authors integrate two layers, namely functional level production capabilities and shop floor-level production routines (PRs). The authors examine how these two layers are interlinked, and additionally, they explore how these layers contribute to firm performance.

The authors tested the hypotheses using structural equation modeling (SEM) on a sample of manufacturing firms.

Regarding the capability layers, the authors found that at the functional level, production dynamic capabilities (PDCs) drive the renewal of production ordinary capabilities (POCs), and that at the shop floor level, deployment of Industry 4.0 (I4.0) is influenced by lean production. Regarding the direct links between capability layers, the authors showed that PDCs and POCs have different roles in shaping shop floor PRs: PDCs is linked to I4.0, and lean methods is impacted by POCs. Concerning performance implications, only PDC and POC have significant impact on firm performance (the latter is negative), while PRs do not.

Although, contextual factors (e.g. technology intensity, size) do not influence our findings, the potential country-effect and the dominance of medium-sized firms offer future research directions.

If production managers want to contribute to business performance, they should be more susceptible to resource renewal (PDCs) than to their general (POCs) or specific (PRs) exploitation efforts. As they exploit current resource stocks, they face a trade-off: they must consider that beyond their positive impacts on operational performance, their implications on business performance will be controversial.

Scholars usually examine one layer of capabilities, either capabilities or routines, and associate that with one dimension of performance, either financial and market measures or operational indicators. The authors propose a multilayered capability framework with a complex view on performance implications.

In recent years there has been an increasing interest in make-to-stock and make-to-order combined strategies in food manufacturing operations. However, most scholarly work to-date has neglected the role of hierarchical production planning (HPP) in guiding small- and medium-sized enterprise (SME) implementation of such strategies. The purpose of this paper is to address food SME manufacturers’ readiness to adopt such strategies, in terms of internal integration and their capability to adopt formalised planning approaches.

This study adopted an action research methodology to explore the potential impact of HPP in SME food manufacturers. Selected companies had identified product variety management as a challenge and also had recognised the need to enhance internal integration. Given this, the research team, from a theoretical perspective, proposed the use of HPP set within a broader decision-making conceptual framework to improve internal integration and planning.

This paper adopts the fundamental position that HPP provides a useful framework in the establishment of strategic and tactical level constraints and priorities which then act as specific guides at the operational level, and presents empirical evidence in a food SME manufacturing context. In the cases the authors studies, the cascading effect of this decision-making framework focused attention on key metrics, encouraged greater internal integration and delivered tangible, significant improvements in performance. This was greatly facilitated by the provision of new key data on the cost of certain managerial trade-offs which these firms faced.

SMEs are of a scale that requires a formalised planning approach; however production planning systems are typically designed for large scale enterprises. This paper addresses the need of SMEs in this regard. Well-established supply chain metrics were used to establish the benefits of both HPP and resulting improvement in internal integration and beyond, in terms of improvement in the quality of planning decisions.