A study of leagility and supply chain design

In the context of a dynamic and hyper-competitive business environment, appropriate design of supply chains helps organisations to align resources for improved flow of products and services and satisfy customers’ diverse needs. Researchers have proposed several supply chain designs which are mutually exclusive such as efficient versus responsive, and lean versus agile. Quantitative testing of these designs revealed that many firms’ supply chain designs do not match with what was conceptually expected. In this research, a new approach to supply chain leagility is investigated, proposing all supply chains are leagile with different magnitudes of leanness and agility. In this regard, a new index, ‘deviation from leagility’ (DFL), is introduced, and employed in this study to optimise supply chain design. DFL is the absolute distance of supply chain design from a balanced supply chain. Balanced supply chain is a position where the magnitude of leanness and agility is equal. A comprehensive model of uncertainty including demand, supply, and internal uncertainty is engaged to investigate the impact of uncertainty as a key design driver of supply chains. The partial least squares (PLS) was employed to analyse data collected from Australian firms. The results indicate that higher performance is achievable when the deviation from a balanced supply chain in which both aspects of leanness and agility are equally embedded, is minimised. Results also reveal that DFL is directly and positively influenced by the level of uncertainty; while it is indirectly impacted by the level of competition intensity and customers’ expectation. Analysis of market segment revealed that irrespective of the segment a firm is operating in or for, uncertainty is a significant determinant of designing a supply chain. However, there is higher magnitude of agility for companies active in the up-market segment of an industry compared to the down-market segment

Developing Offspring Strategy in Manufacturing Industry of Iran : An Empirical Study

This thesis examines an innovative strategy, i.e. offspring strategy and its implications on company\'s performance. Generally, offspring strategy is a tailored made strategy which ventures from outsourcing to insourcing and subsequently spinning off the production line to a new enterprise. This thesis uses multiple case study method to explore the different perspectives of offspring strategy ranging from the formulation to implementation of the strategy and the financial and non-financial implications for companies selected in the study

Investigating the relationship between physical activity level, body mass index, and sleep disorders in patients with type II diabetes

Background:The aim of this study was to investigate the relationship between physical activity and BMI with sleep disorders in patients with type 2 diabetes. Methods:This cross-sectional study was performed on diabetes mellitus in Sabzevar in 2017. The statistical population included 827 diabetic patients over 40 years. Data were collected using demographic questionnaire, sleep disturbance questionnaire and Beck's physical activity. The data was analyzed in SPSS version 21 software. Results: Fining showed most of the subjects had HgA1c above 7. The average body mass index is 29.39 ± 4.88 kg / m². The highest frequency was for people with good quality (72.6%). Correlation test results showed that there is a significant relationship between physical activity and sleep quality score. In this way, the quality of sleep increases with physical activity. But there was no significant relationship between BMI and hemoglobin A1C with sleep quality. Conclusion:Regarding the findings and the role of physical activity on improving the quality of sleep in diabetic patients, appropriate exercises for such patients are recommended

Supply Chain Leagility: a New Perspective

To satisfy customers' diverse needs in the current hyper-competitive business environment, it is crucial for companies to adopt an appropriate design of supply chains. Several supply chain designs have been proposed by researchers which are mostly mutually exclusive such as lean versus agile. We questioned the validity of such designs and a new approach to supply chain leagility is probed, proposing all supply chains are leagile with different magnitudes of leanness and agility. An analysis of 299 Australian firms using the Wilcoxon signed-rank test revealed that most companies adopt leagile supply chain rather than pure lean/agile design

Quantifying the degree of supply chain leagility and assessing its impact on firm performance

The purpose of this paper is to investigate the supply chain leagility proposing all supply chains are leagile with different magnitudes of leanness and agility. A new index, "Deviation from Leagility" (DFL), is introduced, aiming to optimise supply chain design and investigate the relationship between supply chain leagility and firm performance. Design/methodology/approach: The partial least squares (PLS) method was employed to analyse data collected from 299 Australian firms by administering a structured questionnaire. Findings: The results indicate that most companies adopt the leagile supply chain rather than the lean or pure agile design. Furthermore, better business performance is achievable when deviation from a balanced supply chain in which both aspects of leanness and agility are equally embedded is minimised. Research limitations/implications: The study is limited to a number of constraints that measure leagility; further research is needed to incorporate different aspects of agility. Practical implications: The findings of this study could provide a guideline for supply chain executives to improve their company's performance by designing a more balanced leagile supply chain. Originality/value: This study is unique in its in-depth empirical investigation of modelling of leagile supply chain using a new index, and also addressing: first, the current mismatch between the well-known mutually exclusive strategies (lean/agile); and second, what has later been found when the proposed models were quantitatively tested