The art of the deal: Machine learning based trade promotion evaluation

As campanhas promocionais são estratégias de Marketing complexas, involvendo as dinâmicas dos retalhistas e dos consumidores finais. Estas promoções são sensíveis ao seu enquadramento promocional, ou seja, o seu sucesso depende das promoções passadas e das iniciativas e respostas da competição. No setor de bens de consumo, a intensidade promocional tem vindo a aumentar, sendo que as vendas promocionais são atualmente uma porção muito significativa das vendas totais, o que faz do planeamento promocional um processo crucial. Neste contexto, este trabalho propõe um sistema de apoio à decisão capaz de avaliar o sucesso de uma promoção hipotética, tendo em conta dados históricos, para ajudar no processo de planeamento promocional de duas linhas de produtos de uma empresa de bens de consumo. No cerne deste sistema de apoio à decisão, um modelo preditivo, baseado em algoritmos de Machine Learning, que usará dados de promoções passadas e outras variáveis explicativas, de forma a prever melhor o desempenho de uma promoção futura. Este trabalho usa vários conceitos de diversas áreas, nomeadamente, Marketing, Economia, Forecasting, Machine learning e Data mining, que são introduzidas sucintamente.Trade promotions are a complex marketing strategy to drive up sales, involving retailer and consumer dynamics. Furthermore, these events are time-sensitive, influenced by past promotions and both competitor initiatives and responses. In the Consumer Packaged Goods (CPG) sector, the proportion of price-promoted sales to regular-priced sales has increased to a very significant level. Given their relevance to the manufacturer's revenue, proper promotional planning is crucial. In this context, this work proposes a decision support system capable of evaluating a hypothetical trade promotion's success, based on historic data, to be used for the promotional planning process of two key product lines of a CPG manufacturer. At the core of this decision support system, a predictive model, based on machine learning algorithms, will leverage both time series data and predictor variables, in order to better predict future promotional performance. This work pulls from many different branches of knowledge namely, Marketing, Economics, Forecasting, Machine learning and Data mining, areas which are briefly introduced

Aplicação de modelos preditivos para o setor alimentar : um estudo comparativo

Mestrado em Econometria Aplicada e PrevisãoNa sociedade atual a inovação surge como um papel cada vez mais preponderante nas empresas. O presente relatório surge no âmbito de um estágio curricular desenvolvido numa empresa líder a nível mundial no comércio grossista de azeites, com o principal objetivo de encontrar um modelo capaz de prever os preços das suas mercadorias. Para tal, foram analisadas várias metodologias, fazendo uma junção entre modelos tradicionais e mais inovadores e recentes. Sendo por isso, analisados os modelos ARIMA; ARIMAX; VAR como modelos mais tradicionais, em contradição às redes neuronais artificiais do tipo MLP; GMDH. Para o estudo de caso foram utilizados os dados dos três azeites de mais interesse para a empresa, distribuídos por dois conjuntos temporais diferentes, permitindo assim a análise do impacto da dimensão da amostra nas previsões. Estudou-se o impacto de variáveis independentes (nomeadamente meteorológicas, macroeconómicas, entre outras que afetam a produção da azeitona), têm nos preços de compra do azeite. Os resultados apontam para um melhor desempenho do modelo VAR em todos os grupos de dados em análise, obtendo assim as melhores previsões dentro do conjunto de modelos. Destaca-se ainda, a preferência de modelos mais tradicionais quando a série tem um menor comprimento temporal, e uma melhor eficácia das redes neuronais em conjuntos de dados mais elevados, destacando ainda a preferência da rede do tipo GMDH face à rede MLP. Conclui-se ainda, que dentro do vasto conjunto de variáveis em análise, é uma variável binária que influencia a produção (safra), a que possuí maior impacto nas previsões.In today's society, innovation appears as an increasingly prevalent role in companies. This report comes as a part of a curricular internship developed at a world leader in the wholesale of olive oil with the main objective of finding a model capable of predicting the prices of its goods. To this end, several methodologies were analyzed, making a junction between traditional and more innovative and recent models. Therefore, the ARIMA models were analyzed; ARIMAX; VAR as more traditional models, in contradiction to artificial neural networks of the MLP type; GMDH. For the case study, data from the three olive oils of most interest to the company was used, distributed over two different time sets. Thus, allowing the analysis of the impact of the sample size on the forecasts. The impact of independent variables (namely meteorological, macroeconomic, among others that affect olive production) was studied on the purchase prices of olive oil. The results point to a better performance of the VAR model in all groups of data under analysis, thus obtaining the best forecasts within the set of models. Also, noteworthy is the preference for more traditional models when the series has a shorter time length, and a better efficiency of neural networks in higher data sets, also highlighting the preference of the GMDH type network over the MLP network. It is also concluded that, within the vast set of variables under analysis, it is a binary variable that influences production (safra), which has the greatest impact on forecasts.info:eu-repo/semantics/publishedVersio

Optimising supermarket promotions of fast moving consumer goods using disaggregated sales data: A case study of Tesco and their small and medium sized suppliers

The use of price promotions for fast moving consumer goods (FMCG’s) by supermarkets has increased substantially over the last decade, with significant implications for all stakeholders (suppliers, service providers & retailers) in terms of profitability and waste. The overall impact of price promotions depends on the complex interplay of demand and supply side factors, which has received limited attention in the academic literature. There is anecdotal evidence that in many cases, and particularly for products supplied by small and medium sized enterprises (SMEs), price promotions are implemented with limited understanding of these factors, resulting in missed opportunities for sales and the generation of avoidable promotional waste. This is particularly dangerous for SMEs who are often operating with tight margins and limited resources. A better understanding of consumer demand, through the use of disaggregated sales data (by shopper segment and store type) can facilitate more accurate forecasting of promotional uplifts and more effective allocation of stock, to maximise promotional sales and minimise promotional waste. However, there is little evidence that disaggregated data is widely or routinely used by supermarkets or their suppliers, particularly for those products supplied by SMEs. Moreover, the bulk of the published research regarding the impact of price promotions is either focussed on modelling consumer response, using claimed behaviour or highly aggregated scanner data or replenishment processes (frameworks and models) that bear little resemblance to the way in which the majority of food SMEs operate. This thesis explores the scope for improving the planning and execution of supermarket promotions, in the specific context of products supplied by SME, through the use of dis-aggregated sales data to forecast promotional sales and allocate promotional stock. An innovative case study methodology is used combining qualitative research to explore the promotional processes used by SMEs supplying the UK’s largest supermarket, Tesco, and simulation modelling, using supermarket loyalty card data and store level sales data, to estimate short term promotional impacts under different scenarios and derive optimize stock allocations using mixed integer linear programming (MILP). ii The results suggest that promotions are often designed, planned and executed with little formalised analysis or use of dis-aggregated sales data and with limited consideration of the interplay between supply and demand. The simulation modelling and MILP demonstrate the benefits of using supermarket loyalty card data and store level sales data to forecast demand and allocate stocks, through higher promotional uplifts and reduced levels of promotional wast

Fraud investigation in the extravirgin olive oil supply chain : Identification of vulnerable points and development of novel fraud detection methods

With the globalisation of the food supply system, food fraud can have international impacts, sometimes with far-reaching and lethal consequences. Extra virgin olive oil (EVOO) is considered one of the most frequently reported commodities, suffering from fraud. Knowledge about risk factors and precise laboratory and broad on-site screening methods will help to combat fraud in the EVOO supply chain network. The main objectives of this thesis are to develop strategies to combat fraud in the EVOO supply chains through knowledge about weak spots and underlying risk factors and the development of novel detection methods. To achieve these goals, firstly, the EVOO supply chain was assessed for their vulnerability using the SSAFE food fraud vulnerability assessment tool. These assessments indicate that the EVOO supply chain is fairly vulnerable. B2B companies and retailers in the EVOO supply chain are more vulnerable to fraud than olive oil producers and food manufacturers due to the additional vulnerability related to opportunities in time and place and a lack of control measures. Fraud vulnerability across the EVOO supply chain was not only determined by the place of the actor in the chain (node), but also by the scale and location of the companies. Four novel methods were developed in this thesis for EVOO authentication. Monochloropropanediol (MCPD) esters and glycidyl esters (GEs) analysis by gas chromatography-tandem mass spectrometry (GC-MS/MS) was applied to defect EVOO adulteration with lower grade oils. The limit of fraud detection of lower grade olive oils in EVOO was 2% when using 3-MCPD esters, 5% for 2-MCPD esters and 13–14% for GEs. These results imply that the method is fairly useful for confirmatory analysis. However, 3-MCPD analysis by GC-MS/MS is currently a tedious and time-consuming method, it is not recommended to use this method to analyse a large number of suspect samples when a quick response is required. In addition, three rapid and non-destructive techniques were developed. The volatile organic compounds (VOCs) fingerprint analysis by proton transfer reaction-quadrupole ion guide time of flight-mass spectrometry in combination with multivariate statistics proved to be a promising screening methodology for the distinction of EVOO from its lower grade counterparts, as well as from other vegetable oils that are potential adulterants. In the one class classification evaluation, the k-nearest neighbours model presented the best results, which showed that more than 95% of oil samples were correctly predicted. For this most successful model, formic acid, dimethyl sulphide and hexenal are key compounds for the distinction of EVOO from the other oils. Except for the VOCs analysis, the spectral analysis by handheld near infrared spectroscopy combined with multivariate statistics also proved to be good methodology to discriminate EVOO from its lower grade counterparts. The EVOO samples were 100% correctly identified. Pomace olive oil (POO) was efficiently discriminated from EVOO, but 7% of the refined olive oil samples were predicted incorrectly. Furthermore, it was found that the relevant spectral information for the distinction of the oils strongly correlated with the degree of unsaturation of the oils as well as their levels of chlorophylls, carotenoids and moisture. In addition, a newly developed ultrasonic pulse echo system appeared to be a rapid and non-destructive method for the characterisation of vegetable oils. The ultrasonic velocity of EVOO differed significantly from those of POO and the oils of other botanical origin, but not from the velocity of refined olive oil. Furthermore, it was found that the underlying reason for the ultrasonic velocity differences between oils was the variation of the density and viscosity of the oils.  In conclusion, this study shows that the intermediaries between producers and consumers are more vulnerable to fraud due to the opportunities to commit fraud, as well as the greatest lack of adequate food fraud control measures. The results of this thesis also show that the newly developed methods cannot easily to be circumvented by fraudsters and they can be effectively applied for the distinction of EVOO from its lower grade counterparts and some vegetable oils. The insights in the weak spots in the EVOO supply chain network in combination with the newly developed fraud methods add to and reinforce the strategies to combat fraud in the EVOO supply chain. This all will help to ensure that consumers get what they are paying for and to fight unfair competition

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: a short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications

Revision of the EU Green Public Procurement Criteria for Food and Catering Services

This report forms the basis to revise the existing EU Green Public Procurement (GPP) criteria for Food and Catering services. This will assist in the reduction of negative impacts of the, public procurement of these services, on the environment, human health and natural resources. The revision of EU GPP requires in-depth information about the technical and environmental performance of the service group as well as about the current procurement processes. The scientific body of evidence gathered will be cross-checked with sector experienced stakeholders, to develop consensus on how the criteria should be revised to deliver optimum environmental improvements. This Report which will be the basis for producing the draft EU GPP criteria proposals that will be discussed in the Ad Hoc Working Group (AHWG) meetings. Chapter 1 focuses on the definition for and scoping of Food and Catering Services to overview of the different scope and definitions and to narrow down the services in order to obtain a homogenous scope. Chapter 2, market analysis, presents the market overview and the market structure by focusing on the contract catering market. The environmental analysis (Chapter 3) provides a detail analysis of the main environmental hotspots for the food product categories by reviewing a number of LCA studies. Other non-LCA aspects including ethical and health consideration are also summarised. The technical analysis focus on the current schemes and labels identified as important for the revision of the current EU GPP criteria. Chapter 4 provide improvement options, based on the best sector practices and national cases studies which could assist the revision of the EU GPP, and reduce the sectors environmental impacts, by drawing on findings from the market and technical analysis.JRC.B.5-Circular Economy and Industrial Leadershi

Sustainability Assessment at the 21st century

The sustainability of the human society is endangered by the global human-ecological crisis, which consists of many global problems that are closely related to each other. In this phenomenon, the global population explosion has a central role, because more people have a larger ecological footprint, a larger consumption, more intensive pollution, and a larger emission of carbon dioxide through their activities.This book presents the current state of sustainability and intends to provide the reader with a critical perspective of how the 21st century societies must change their development model facing the new challenges (internet of things, industry 4.0, smart cities, circular economy, sustainable agriculture, etc.), in order to achieve a more liveable world

The economic development of the Greek olive-oil industry with special reference to Messenia Province

This study examines the economic development of the Greek OliveIndustry. Its focal point is the impact of recent socio-economicprocesses on the structure and organisation of the industry. In thefirst part, which is concerned with the rural sector, it is argued thatolive cultivation and its development through time, has beenconstrained by a number of social, structural and institutional factorswhich are identified and their influence is then discussed. It iscontended that recent changes due to the imposition of the EEC regimehave brought about socio-economic processes which have considerablyaffected the mode of organisation of the rural sector in particular andthe whole industry in general.In the second part of this study, which is concerned with the urbansector, it is argued that during the last decade, rapid change hastransformed the outlook of the second-stage processing of the industry.This change has affected the structure in two ways. First, there hasbeen a large increase inkhe number of small packing units which operatein domestic market niches and compete for a share in the export trade.Secondly, there has been a concentration of output and economic powerin the hands of three leading packers, two multinational subsidiaries,and the cooperative enterprise Eleour7_giki. The financial base of thisindustrial change, though, is somewhat artificial. In particular,expansion in production and the modernisation process which has beentaking place recently, are largely based on the CAP support system tothe second-stage processing and packing, and also to large amounts ofearnings which every year go through tax evasion