Investing in performance drivers

Purpose: This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design: This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings: Heavy and unrestrained investment in R&D in the Indian pharmaceutical industry can negatively impact the performance and revenues of such firms. Originality: The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Applying analytical hierarchy process for addressing the agile manufacturing drivers

In order to incorporate agile manufacturing (AM) in materials and systems, the manufacturing sectors have drivers to face obstacles. Agility is generally accepted for satisfying diverse consumer demands as a new strategic principle in the automotive industry. There has now been a prerequisite for evaluating AM in industry. An organization’s effectiveness relies on their ability to find and pay special attention to the crucial success drivers to achieve a high level of efficiency. This paper suggests a number of Agile Manufacturing Drivers (AMDs) to evaluate AM that is deemed suitable to the production industry. In order to prioritise performance drivers, the analytical hierarchy process (AHP) approach is used to summarise the perspective of an expert. The proposed AMDs are believed to encourage and assist the manufacturing sector in producing agile products to achieve higher efficiency so as to improve competition.

Business model innovation drivers as antecedents of performance

Purpose The innovation of business model (BM) is a strategic process for many firms, from which depends competitiveness and sustainability. Despite its theoretical relevance in management sciences, research on business model innovation is in its infancy and lacks of research consistency and theoretical connections to the theme of “performance”. With the aim to contribute in bridging this gap, this paper aims to identify and analyse drivers of business model innovation performance. Design/methodology/approach This research is based on an integrative literature review methodology. Findings BMI performance drivers are conditions related to various dimensions (i.e. processes, resources, market, BM structure, etc). that, when fulfilled, allow the BMI to have higher performance. BMI performance drivers are antecedents of BMI performance, and their identification is of both theoretical and practical value. The authors find and report a set of 35 BMI performance drivers. Originality/value The value of this research is both theoretical and practical. From a theoretical point of view, the identified “Business Model Innovation performance drivers” define and identify a variable of BMI performance, from a practical perspective, and they provide a comprehensive set of key conditions whose attainment should be planned, pursued and monitored by managers.

Performance drivers in knowledge-intensive entrepreneurial firms: a multidimensional perspective

Purpose The purpose of this paper is to offer a comprehensive perspective on different facets of knowledge management and their effects on the performance of knowledge-intensive entrepreneurial ventures. Design/methodology/approach The empirical setting involves small and medium-sized enterprises located in the State of São Paulo, Brazil. Primary data for 223 knowledge-intensive entrepreneurship (KIE) firms was obtained through questionnaires applied to ventures which applied to the innovative research in small business program, a small business innovation research-like initiative run by the São Paulo Research Foundation. Econometric results assessed the drivers of competitiveness in terms of firm growth, research and development intensification and technology transfer. Findings Results highlight the complexity involved in establishing effective knowledge management processes in terms of driving KIE performance. Notwithstanding, some interesting insights on the moderation effects of strategic knowledge management (SKM) systems over technical skills could be identified with particular emphasis for the case of academic spin-offs. Ecosystem drivers present a good explanation power for technology transfer practices but fall short in providing answers for firm-level growth dynamics. It is also noteworthy that public and private investments in KIE firms are similarly associated with positive impacts – contrary to the view that private investors perform better than governmental sources in picking promising small ventures. Originality/value The assessment has provided novel evidence for a sample of KIE ventures concerning the appraisal of performance drivers associated with three dimensions of knowledge management, namely, technical knowledge, SKM and ecosystem features. Firms’ outcomes were addressed from a multidimensional perspective, thus providing a comprehensive perspective of the events under scrutiny.

Measurement and Determinants of Financial Performance of Modaraba Companies: A Case Study of Pakistan

Purpose: Profitability measurement is a comparative statistic that describes the relationships between overall profit and other financial determinants of the firm. Design/Methodology/Approach: The focus of this study is to measure the technical (TE), pure technical (PTE) and scale efficiency (SE) scores via Data Envelopment Analysis (DEA) of modaraba companies operating in Pakistan. The next stage is to study the empirical relationship between profitability, liquidity, leverage, and macroeconomic performance drivers. Financial statement data for 2010 to 2019 have been analyzed. Findings: Empirical findings of descriptive statistics, correlation and regression were measured. These empirical results reveal that capital ratio (CR) and operating expenses to net income (OENI) had negative correlation with PTE, SE and TE. Whereas the age of the firm had a negative correlation with PTE and TE and positive correlation with SE, moreover, exchange rate (EXC) PKR to USD, log of total assets (LTA) and management expenses (ME) had negative correlation with SE and positive correlation with PTE and TE. Furthermore, inflation (INF) had negative correlation with PTE and positive correlation with SE and TE. Moreover, number of certificates (NOC) had negative correlation with SE and TE and positive correlation with PTE. Implications/Originality/Value: Findings will be helpful to the management and policy makers for enhancing future financial performance by concentrating on these economic factors. More detailed and extensive data from the financial and non-financial aspects is suggested to support the hypothesized relationship of efficiency measures and determinants.

An Operators Investigation of Performance Drivers Using Diagnostics and Data Analytics

In multi-stage plug-and-perf horizontal well completions, there are a multitude of moving parts and variables to consider when evaluating performance drivers. Properly identifying performance drivers allows an operator to focus their efforts to maximize the rate of return of resource development. Typically, well-to-well comparisons are made to help identify performance drivers, but in many cases the differences are not clear. Identifying these drivers may require a better understanding of performance variability along a single lateral. Data analytics can help to identify performance drivers using existing data from development activities. In the case study below, multiple diagnostics are utilized to identify performance drivers. A combination of completion diagnostics including oil and water tracers, stimulation data, reservoir data, 3D seismic, and borehole image logs were collected on a set of wells in the early appraisal phase of a field. Using oil tracers as the best indication of stage level performance along the laterals, data analytics is applied to uncover the relationships between the tracers and the numerous diagnostics. After smoothing was applied to the dataset, trends between oil tracer recovery, several independent variables and features seen in image logs and 3D seismic were identified. All the analyses pointed to decreasing tracer recovery, and likely decreased oil production, near faulted areas along each lateral. A random forest model showed a moderate prediction power, where the model's predicted tracer recovery on blind stages was able to explain 54% of the variance seen in the tracer response (r2=0.54). This analysis suggests the identification of certain faulted areas along the wellbore could lead to ways of improving individual well economics by adjusting completion design in these areas.

Measuring Performance in the Luxury Industry: Is there a Difference in Performance Between the top and Bottom Players in the Industry?

This paper presents a methodology that consists of a Z-score function applied on a set of indicators for the luxury industry and then standardized to obtain a ranking of the companies on a scale between 10 and 100. To measure the performance for the top 5 LVMH, Estee Lauder, Richemont, Kering, Essilor Luxottica, and bottom 5 Aeffe, Tribhovandas, Van de Velde, Mulberry, Trinity companies (based on Deloitte global ranking in 2019) in the luxury industry and to unveil which are the performance drivers. By applying this methodology it can be made a comparison in performance between the top and the bottom companies in the luxury industry. The performance score is calculated for ten years (2010 - 2019) on public companies from the luxury industry.

Towards a strategic innovation framework to support supply chain performance

PurposeThis paper aims to determine how a strategic innovation framework that integrates the concepts of innovation function, dynamic performance management (DPM) and system-dynamics (SD) modelling can measure performance in a supply chain (SC).Design/methodology/approachThe paper provides a strategic innovation framework for an SC by considering three steps. First, a systemic intervention is presented based on the innovation functions that influence SC performance. Second, an analysis of the system's performance is proposed. Third, a model SD-based simulation is designed. The developed framework is explained by employing a case study of the Colombian pig sector SC.FindingsThe results reveal that identifying and synchronising the system's performance drivers associated with the innovation functions could improve the inventory in the SC.Practical implicationsOn the one hand, managers can use the proposed framework to evaluate the innovation investments and understand their impact on operation performance (e.g. on inventories). On the other hand, policymakers may support decision-making to improve policy design (e.g. through investment in R&D).Originality/valueFew studies discuss the impacts of innovation functions on SC performance. This paper aims to fill this theoretical gap and to contribute to the literature by suggesting a novel framework which includes innovation functions.