Food waste is an increasing problem worldwide. Around one third of all produced food is estimated to end up as waste. This has big environmental and economic consequences. In literature, little is known about the causes of waste, its key performance indicators and how to reduce it. Some root causes were found, but none were quantitatively confirmed. E-groceries are a relatively new type of supermarket, that operate on a big scale and have access to detailed data about their customers and their shopping behavior. This data enables detailed quantification of the waste. Picnic is one of these supermarkets, operating in the Netherlands and Germany. A case study focused on reduction of food waste was performed at their fulfilment centers. Also at Picnic, little was known about waste and its causes. This research aims at identifying root causes in a quantitative way and using gained insightsto reduce waste. The research question was: “What are the root causes of food waste at an online supermarket’s fulfilment centres, and how can food waste be reduced?” The case study that was performed consisted of three parts: • An exploratory part, in which data was collected and investigated. Chilled productscaused 80 % of the waste, while only consisting of 20 % of the assortment. • A qualitative part, in which KPIs and expected root causes were identified and hy-potheses generated. Waste amount in units was used as a key performance indicator.Expected root causes were identified for four domains: assortment, supply chain, ful-filment center processes and other. • A quantitative part, in which variables were constructed by means of separate analyses to measure these expected root causes. Twelve variables were constructed for assortment and supply chain related factors. The significance of the hypothesized root causes were tested with multivariate regression tools, focusing only on chilled products. Two data sets have been tested, both on granularity article x fulfilment center x financial period. The first containing the full data, including a majority of zero waste data points. The second containing only waste. All factors showed a significant relationship with waste, in at least one of the regression models, confirming that the identified factors are indeed root causes of food waste. The results were used to improve waste reduction processes at Picnic. The gained insights in root causes ensured effective handling of waste cases: actions could be taken according to the root cause of specific cases. This efficient continuous improvement has led to a waste reduction of 40 %, expressed in costs per item sold. Advisors/Committee Members: Atasoy, Bilge (mentor), Schott, Dingena (graduation committee), Gorte, Frank (graduation committee), Delft University of Technology (degree granting institution).

We have tried to create a bin-packing algorithm that assigns items from a customer-order to totes such that the amount of totes is minimized. Analyzing the bin-packing algorithm that was used before this thesis had been written, taught us that xxx.xx% of the customer-orders was packed non optimal. In this thesis four algorithms are applied to Picnic data. The order in which the algorithms assign items to a tote has major consequences for the solutions. Eight different ways to order the items are combined with each algorithm, resulting in 32 different tote-calculations. Out of those 32 tote-calculations, the Best Fit Algorithm with items ordered in decreasing normalized values generates the best results. Remarkably, ordering items randomly also gives good solutions. This brought us to introducing a new method, where each customer-order is calculated at most eight times, each time shuffling the items before rerunning the algorithm and remembering the better solution. This heuristic is optimal for xxx.xx% of the customer-orders.

Applied Mathematics

Advisors/Committee Members: de Oliveira Filho, Fernando (mentor), Aardal, Karen (graduation committee), van Gijzen, Martin (graduation committee), Gorte, Frank (graduation committee), Delft University of Technology (degree granting institution).