The impact of private label foods on supply chain governance

Purpose – The purpose of this paper is to investigate the impact of the introduction of private label (PL) foods upon the governance of the food supply chains. Design/methodology/approach – The authors conducted a multi-case study research examining the launch and development of PL cheeses in four large national-wide retail chains. The paper focused on the category of Products of Designated Origin (PDO) cheeses, including the popular feta cheese. Data collection involved semi-structured interviews and secondary sources of information. Data analysis involved single-case and within-case analyses. Findings – There is a strong motive to launch and develop PL cheeses due to increasing consumer demand. Retailers choose suppliers based on criteria such as: compliance to quality assurance standards, modernisation of processing facilities, implementation of legislation, credibility, experience, and reputation. Retailers use contracts and prefer small suppliers than medium-sized companies. Supply chain governance turns from market to hierarchy status, which performs better in terms of supply chain cost, food quality, and consumer satisfaction. The structure of food industry is also affected by pressure put on medium-sized food companies. Research limitations/implications – The paper is based on a multiple case study design that does not provide static generalisations, yet it offers a stepping stone to building new theory about supply chain governance, how it evolves and its effects on supply chain performance. Practical implications – The introduction of PL cheeses favours small and dynamic cheese processing units willing to adopt retailer standards and prices over larger units, which poses a real threat to the survival of regional-wide food companies. Originality/value – Few studies have examined how supply chain governance evolves and what triggers a change in governance structures

Volumetric Particle Tracking Velocimetry (PTV) Uncertainty Quantification

We introduce the first comprehensive approach to determine the uncertainty in volumetric Particle Tracking Velocimetry (PTV) measurements. Volumetric PTV is a state-of-the-art non-invasive flow measurement technique, which measures the velocity field by recording successive snapshots of the tracer particle motion using a multi-camera set-up. The measurement chain involves reconstructing the three-dimensional particle positions by a triangulation process using the calibrated camera mapping functions. The non-linear combination of the elemental error sources during the iterative self-calibration correction and particle reconstruction steps increases the complexity of the task. Here, we first estimate the uncertainty in the particle image location, which we model as a combination of the particle position estimation uncertainty and the reprojection error uncertainty. The latter is obtained by a gaussian fit to the histogram of disparity estimates within a sub-volume. Next, we determine the uncertainty in the camera calibration coefficients. As a final step the previous two uncertainties are combined using an uncertainty propagation through the volumetric reconstruction process. The uncertainty in the velocity vector is directly obtained as a function of the reconstructed particle position uncertainty. The framework is tested with synthetic vortex ring images. The results show good agreement between the predicted and the expected RMS uncertainty values. The prediction is consistent for seeding densities tested in the range of 0.01 to 0.1 particles per pixel. Finally, the methodology is also successfully validated for an experimental test case of laminar pipe flow velocity profile measurement where the predicted uncertainty is within 17% of the RMS error value