Real-time data collection to improve energy efficiency: A case study of food manufacturer

The rising price and demand for energy are significant issues for the food sector, which consumes a substantial amount of energy throughout the supply chain. Hence, improving energy efficiency has become an essential priority for the food sector. However, most food businesses have limited awareness of the recent technological advancements in real-time energy monitoring. Thus, the concept of “Internet of Things” (IoT) has been investigated to increase the visibility, transparency, and awareness of various energy usage levels. This paper presents a case study of a beverage factory where the implementation of an IoT-enabled sensing technology based on the embodied product energy (EPE) model helped to reduce the energy consumption. This arrangement made provision for the collection of real-time energy data within a food production system to support informed and energy-aware operational decisions, which lead to optimized energy consumption and significant savings of approximately 163,000 kWh in the year 2017. Practical applications Given the importance of energy efficiency and Internet of Things (IoT), especially in the food manufacturing industry, this research reports a baseline application at a beverage company in India. The results allowed the company to use energy more efficiently to have an advantage over its competitors and better market positioning. More data could be incorporated into the energy management system with the use of IoT. The availability and accuracy of such valuable data would help managers to make better energy-efficient decisions

Implementation by simulation; strategies for ultrasound screening for hip dysplasia in the Netherlands

Background: Implementation of medical interventions may vary with organization and available capacity. The influence of this source of variability on the cost-effectiveness can be evaluated by computer simulation following a carefully designed experimental design. We used this approach as part of a national implementation study of ultrasonographic infant screening for developmental dysplasia of the hip (DDH). Methods: First, workflow and performance of the current screening program (physical examination) was analyzed. Then, experimental variables, i.e., relevant entities in the workflow of screening, were defined with varying levels to describe alternative implementation models. To determine the relevant levels literature and interviews among professional stakeholders are used. Finally, cost-effectiveness ratios (inclusive of sensitivity analyses) for the range of implementation scenarios were calculated. Results: The four experimental variables for implementation were: 1) location of the consultation, 2) integrated with regular consultation or not, 3) number of ultrasound machines and 4) discipline of the screener. With respective numbers of levels of 3,2,3,4 in total 72 possible scenarios were identified. In our model experimental variables related to the number of available ultrasound machines and the necessity of an extra consultation influenced the cost-effectiveness most. Conclusions: Better information comes available for choosing optimised implementation strategies where organizational and capacity variables are important using the combination of simulation models and an experimental design. Information to determine the levels of experimental variables can be extracted from the literature or directly from experts

Grip force vectors for varying handle diameters and hand sizes

Grip force was measured along two orthogonal axes and vector summed. Sixtyone participants recruited from a manufacturing facility (29 men and 32 women) grasped instrumented cylinders (2.54, 3.81, 5.08, 6.35, and 7.62 cm diameter) using a maximal voluntary power grip. Two orthogonal force measurements relative to the third metacarpal were resolved into a magnitude and corresponding angle. On average, magnitude increased 34.8 N as handle diameter increased from 2.54 cm to 3.81 cm, and then monotonically declined 103.8 N as the handle diameter increased to 7.62 cm. The average direction monotonically decreased from 59.20 to 37.7° as handle diameter decreased from the largest to the smallest. When the diameter was smallest, the greatest force component, F,, (168.6 N), was in the direction where the fingertips opposed the palm. Conversely, when the diameter was largest, the smallest component, F,, (77.7 N), was in the same direction. These values are averaged for the left and right hand. The angle for the largest diameter increased with increasing hand size. These relationships should be useful for the design of handles that require gripping in specific directions, such as for hand tools and controls. Actual or potential applications of this research include the design of handles that require gripping in specific directions, such as for hand tools and controls, that reduce effort, and that prevent fatigue and overexertion