Modeling food logistics management

Topic 1. Analysing the actual variation in quality parameters along fresh produce supply chains under different storage facilities, logistic systems, and supply chains.

The problem of food losses is urgent in developing countries. Yearly losses for fruit and vegetables can be as high as 70%. Scarce resources, such as water used during crop production, are being wasted because of food losses after harvesting. Reducing food losses can therefore help to improve the sustainability of fresh produce chains. Recent studies showed that at least 30% of post-harvest losses (PHLs) are avoidable. This presents a great opportunity to reduce PHLs. Several solutions to reduce PHLs in fresh produce chains have been put forward in literature. However, the proposed solutions fall short in that they are only either from a logistics control or from a quality control perspective, and context factors are not considered. Recent literature, however, shows that integrating quality control and logistics control could be a more effective way to reduce the incidence of food losses in fresh produce chains. There is thus a need to improve concurrently quality control and logistics control activities along fresh produce chains to reduce post-harvest losses.
However, there is not yet a tool to analyse systematically crucial control and logistic decisions that increase the chance of post-harvest losses, taking into account the context characteristics, wherein these activities take place. Moreover, few real-life data are available on the actual variation in quality parameters under different logistic systems and control activities.

The PhD project aims at developing a diagnostic tool to analyse the influence of control and logistics decisions on post-harvest losses, given the typical context characteristics of Zimbabwean fruit/supply chains, in order to develop effective interventions to reduce food losses. The MSc project will be further demarcated.

For more information on this topic please contact Lesley Macheka.

Topic 2. Further developing and testing a diagnostic instrument of quality controlled logistics in food supply chains.

Western-European consumers have become not only more demanding on product availability in retail outlets but also on other food attributes such as quality, integrity, and safety. When (re)designing food supply-chain networks, from a logistics point of view, one has to consider these demands next to traditional efficiency and responsiveness requirements. The concept ‘quality controlled logistics’ (QCL) hypothesizes that if product quality in each step of the supply chain can be predicted in advance, goods flows can be controlled in a pro-active manner and better chain designs can be established resulting in higher product availability, constant quality, and less product losses. Van der Vorst et al. defined the six elements of the QCL concept and discusses opportunities of using real-time product quality information for improvement of the design and management of ‘AgriFood Supply Chain Networks’. A previous MFQ thesis project developed a preliminary diagnostic instrument for assessment each of these six elements and applied the instrument in a banana and tomato trading company. However, no full chain assessment has been done and not all aspects of the instrument have received the same attention.

Aim of this thesis project is to further refine the diagnostic instrument and test its applicability in a complete supply chain. Part of the assignment is to typify food supply chains by its specific characteristics and impacts on the benefits of QCL. Objective is to assess the opportunities of QCL on the supply chain, and potentially (also depending on the background of the student) to model and quantify the impact using optimisation or simulation models.

For more information on this topic please contact Jack van der Vorst.

Topic 3. A diagnostic instrument for sustainability improvement of food supply chains.

Food companies are increasingly challenged to balance business performance and economic gains with environmental and social performance. Therefore, in 2012, we started a collaborative project on this topic named SCALE (Step Change in Agri-food Logistics Ecosystems). SCALE aims to improve the sustainability of food and drink supply chain logistics in the context of rising food demands, increasing energy prices and the need to reduce environmentally damaging emissions. More in particular, SCALE aims to deliver a number of tools and frameworks valuable for the agri-food sector to secure a step change in operational practices, which will improve the efficiency and sustainability of supply chain logistics. In the project, we developed a sustainability research framework for food supply chains logistics including drivers, strategies, performance indicators, metrics and improvement opportunities to measure and potentially enhance sustainability performances. Next to this, we analysed and diagnosed the current status of Dutch food & drinks companies and logistics service providers using this framework.

Now that we have the preliminary findings it becomes important to develop a diagnostic instrument for sustainable food supply chains. Key factors and indicators should be identified and defined that facilitate or obstruct the sustainability of the food chain. The instrument could be tested in case studies and supported by structured interviews with Dutch food industry and logistics service industry.

For more information on this topic please contact Jack van der Vorst.

Topic 4. Simulation of a fresh food supply chain to reduce spoilage: the impact of technological and logistical measures.

About 30-50% of food gets spoiled in modern supply chains. In a current research project technological as well as logistical measures are evaluated to reduce food spoilage. By simulation one can quantify and study the effect of diverse measures on food spoilage as well as on costs. In Decision Science 2, students have learned about simulating supply chains in Enterprise Dynamics (ED). Such a model can be extended to include perishability, and to study options to reduce food spoilage. These options, can be logistical measures like improving the replenishment policy of the actors in the supply chain, or redesigning the supply chain, or technological improvements, such as choice of cultivar, package, or improving the climate (temperature and humidity) under which products flow through the chains. In a previous project an MFQ student has developed a basic model. This model can be used as a starting point of future work.

This project can be extended in multiple directions, applied to different products (meat, vegetables, fruits, flowers), and can be combined with lab research (to estimate quality decay model as inputs to the simulation model), and may include an internship

For more information on this topic please contact Jack van der Vorst.

Topic 5. Determining strawberry shelf life using visual data” and “Modelling quality and logistics in the strawberry supply chain.

Strawberries are highly perishable products, that typically have large variation in quality between and within batches of strawberry punnets from the same origin (e.g. the same production region, truck, farmer, or field). Problems related to poor strawberry quality frequently arise, resulting in large product value losses, stock-outs at retail level, and poor customer satisfaction. To optimise the match between available strawberry batches and demanded quality features in the international market (e.g. a customer far away might require higher quality than a local customer), distributors / wholesalers would like to estimate the quality and variation in quality of individual strawberry batches. These quality estimates can be used to predict the remaining shelf life (and the acceptance period) of a strawberry batch, and to assess whether a batch fulfils the quality requirements set by a customer.
Research indicates that visual information (both in the visible and invisible area) might be used to determine two key determinants of strawberry quality, being
i. the ‘red’-level, which is a key determinant for consumer acceptability, and
ii. the infection level with Botrytis cinerea, which is an important strawberry spoilage driver.
If this data would be available for individual strawberry batches this would allow for an advanced quality driven logistics concept, which brings a large potential for reduction of waste, cost, customer complaints, etc.
Aim of the two foreseen thesis projects will be to:
1. Determine if and how well we can use visual data of strawberries to predict the start of the acceptance period / the remaining shelf life. Also determine where in the chain this method can be used. Advantages and enables/disablers should be analysed. This study involves laboratory research and literature review.
2. Assess the possibilities for use of visual strawberry quality data in a supply chain context using quantitative modelling techniques. Key is to assess the applicability of this information in logistics decision making. Using the case of the strawberry supply chain, simulation models will be developed to assess the value of this kind of information on strawberry quality/safety and logistics performance indicators. This part is more related to the supply chain business and will foremost involve modelling activities.

For more information on this topic please contact Jack van der Vorst.