:Under an intense internationally competitive business environment, it is important to understand the production efficiency of the baking industry, where efficient management is becoming increasingly important to ensure the sustainable development of the company. Thus, this study uses data envelopment analysis (DEA) to appraise the performance of a well-known baking company (85°C) and uses input and output constructs to measure its technical efficiency and scale efficiency scores to understand the major reasons for efficiency losses from 2011 to 2016. The empirical results indicate that low technical efficiency is the major reason for lower pure technical efficiency, since the scale efficiency is higher than pure technical efficiency. This means 85°C is still improving overall operating efficiency and space efficiency. Moreover, the results also show that the III-generation operations style is more technically efficient and pure-technically efficient compared to those of I-generation and II-generation. Furthermore, the company’s financial performance is dependent upon the producer's ability to stay on the production frontier due to the result of a positive relationship between return on assets (ROA) and technical efficiency. Last but not least, this study shows that 85°C can gain higher performance and efficiency by enhancing technical efficiency and reinforcing strategic alignments with business goals.
In recent years, the bakery market has grown rapidly. Alongside its growth and fast change, it is very important to comprehend the productivity change of the bakery industry. Nowadays, effective management is more and more important to ensure the sustainable development of enterprises. Thus, productivity change of 22 self-owned stores of a famous bakery company (85 • C) from 2011 to 2016 was quantitatively analyzed and evaluated by adopting Malmquist index model in this study. Based on the Malmquist index model, the overall mean for total productivity change of 85 • C increased slightly from 2011 to 2016, and the productivity change was easily affected by technical progress. Moreover, the results also show that the north-district self-owned stores (which are located in subtropical climate) have the worst technical progress and total factor productivity change during 2011-2016 period by adopting the non-parametric Kruskal-Wallis and Dunn post-hoc test.
Setup time consists of all the activities that need to be completed before the production process takes place. The extant scheduling predominantly relies on simplistic methods, like the average value obtained from historical data, to estimate setup times. However, such methods are incapable of representing the real industry situation, especially when the setup time is subject to significant uncertainties. In this situation, the estimation error increases proportionally to the problem size. This study proposes a Random-Forest-based metaheuristic to minimize the makespan in an Unrelated Parallel Machines Scheduling Problem (UPMSP) with uncertain machine-dependent and job sequence-dependent setup times (MDJSDSTs). Taking the forging industry as an example, the numerical experiments show that the error percentage for the setup time estimation substantially decreases when the proposed approach is applied. This improvement is particularly significant when large-scale problems are sought. Overall, this study highlights the role of advanced analytics in bridging the gap between scheduling theory and practice. INDEX TERMS Scheduling, unrelated parallel machines, setup times, random-forest, metaheuristic.
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