Improving Demand Forecasting: The Challenge of Forecasting Studies Comparability and a Novel Approach to Hierarchical Time Series Forecasting

Bedarfsprognosen sind in der Wirtschaft unerlässlich. Anhand des erwarteten Kundenbe-darfs bestimmen Firmen beispielsweise welche Produkte sie entwickeln, wie viele Fabri-ken sie bauen, wie viel Personal eingestellt wird oder wie viel Rohmaterial geordert wer-den muss. Fehleinschätzungen bei Bedarfsprognosen können schwerwiegende Auswir-kungen haben, zu Fehlentscheidungen führen, und im schlimmsten Fall den Bankrott einer Firma herbeiführen. Doch in vielen Fällen ist es komplex, den tatsächlichen Bedarf in der Zukunft zu antizipie-ren. Die Einflussfaktoren können vielfältig sein, beispielsweise makroökonomische Ent-wicklung, das Verhalten von Wettbewerbern oder technologische Entwicklungen. Selbst wenn alle Einflussfaktoren bekannt sind, sind die Zusammenhänge und Wechselwirkun-gen häufig nur schwer zu quantifizieren. Diese Dissertation trägt dazu bei, die Genauigkeit von Bedarfsprognosen zu verbessern. Im ersten Teil der Arbeit wird im Rahmen einer überfassenden Übersicht über das gesamte Spektrum der Anwendungsfelder von Bedarfsprognosen ein neuartiger Ansatz eingeführt, wie Studien zu Bedarfsprognosen systematisch verglichen werden können und am Bei-spiel von 116 aktuellen Studien angewandt. Die Vergleichbarkeit von Studien zu verbes-sern ist ein wesentlicher Beitrag zur aktuellen Forschung. Denn anders als bspw. in der Medizinforschung, gibt es für Bedarfsprognosen keine wesentlichen vergleichenden quan-titativen Meta-Studien. Der Grund dafür ist, dass empirische Studien für Bedarfsprognosen keine vereinheitlichte Beschreibung nutzen, um ihre Daten, Verfahren und Ergebnisse zu beschreiben. Wenn Studien hingegen durch systematische Beschreibung direkt miteinan-der verglichen werden können, ermöglicht das anderen Forschern besser zu analysieren, wie sich Variationen in Ansätzen auf die Prognosegüte auswirken – ohne die aufwändige Notwendigkeit, empirische Experimente erneut durchzuführen, die bereits in Studien beschrieben wurden. Diese Arbeit führt erstmals eine solche Systematik zur Beschreibung ein. Der weitere Teil dieser Arbeit behandelt Prognoseverfahren für intermittierende Zeitreihen, also Zeitreihen mit wesentlichem Anteil von Bedarfen gleich Null. Diese Art der Zeitreihen erfüllen die Anforderungen an Stetigkeit der meisten Prognoseverfahren nicht, weshalb gängige Verfahren häufig ungenügende Prognosegüte erreichen. Gleichwohl ist die Rele-vanz intermittierender Zeitreihen hoch – insbesondere Ersatzteile weisen dieses Bedarfs-muster typischerweise auf. Zunächst zeigt diese Arbeit in drei Studien auf, dass auch die getesteten Stand-der-Technik Machine Learning Ansätze bei einigen bekannten Datensät-zen keine generelle Verbesserung herbeiführen. Als wesentlichen Beitrag zur Forschung zeigt diese Arbeit im Weiteren ein neuartiges Verfahren auf: Der Similarity-based Time Series Forecasting (STSF) Ansatz nutzt ein Aggregation-Disaggregationsverfahren basie-rend auf einer selbst erzeugten Hierarchie statistischer Eigenschaften der Zeitreihen. In Zusammenhang mit dem STSF Ansatz können alle verfügbaren Prognosealgorithmen eingesetzt werden – durch die Aggregation wird die Stetigkeitsbedingung erfüllt. In Expe-rimenten an insgesamt sieben öffentlich bekannten Datensätzen und einem proprietären Datensatz zeigt die Arbeit auf, dass die Prognosegüte (gemessen anhand des Root Mean Square Error RMSE) statistisch signifikant um 1-5% im Schnitt gegenüber dem gleichen Verfahren ohne Einsatz von STSF verbessert werden kann. Somit führt das Verfahren eine wesentliche Verbesserung der Prognosegüte herbei. Zusammengefasst trägt diese Dissertation zum aktuellen Stand der Forschung durch die zuvor genannten Verfahren wesentlich bei. Das vorgeschlagene Verfahren zur Standardi-sierung empirischer Studien beschleunigt den Fortschritt der Forschung, da sie verglei-chende Studien ermöglicht. Und mit dem STSF Verfahren steht ein Ansatz bereit, der zuverlässig die Prognosegüte verbessert, und dabei flexibel mit verschiedenen Arten von Prognosealgorithmen einsetzbar ist. Nach dem Erkenntnisstand der umfassenden Literatur-recherche sind keine vergleichbaren Ansätze bislang beschrieben worden

Retail forecasting: research and practice

This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice

Analyzing a Shopper’s Visual Experience in a Retail Store and the Impact on Impulse Profit

The retail industry in the U.S. contributed 1.14 trillion in value added (or 5.9%) to the GDP in 2017, an increase of 3.7% from the previous year. While store closures have dominated the news in the recent past (e.g., Toys-R-Us, Sears, and Bon-Ton) due to ineffective supply chain practices, inadequate in-store experiences, and competition from e-tailers, other retailers such as Ross, T. J. Maxx, Burlington Coat Factory, and Kroger have been expanding their footprint. Brick-and-mortar stores are unique as they allow shoppers the ability to see, touch, and try products, in addition to exploring new products. Kohl’s CEO has even indicated that 90% of their revenue is still generated in brick-and-mortar stores. Besides reducing supply chain costs, retailers have been paying considerable attention to redesigning their stores by varying layouts and displays to improve shopping experience and remain profitable. However, a lack of scientific methods that correlate layout changes to improved experience has often led to time-consuming and expensive trial-and-error approaches for the retailers. This research focuses on the design of such brick-and-mortar stores by developing a quantitative approach that models the visual interaction between a 3D shopper’s field of view and the rack layout. This visual interaction has been shown to influence shopper purchasing habits and their overall experience. While some metrics for visual experience have been proposed in the literature, they have been limited in many ways. The objective of this research is to develop new models to quantify visual experience and employ them in layout design models. Our first contribution consists of quantifying exposure (which rack locations are seen) and the intensity of exposure (how long they are seen) by accounting for the dynamic interaction between the human 3D field of regard with a 3D rack layout. We consider several rack designs/layouts that we noticed at nearby retail stores, ranging from the typical rectangular racks placed orthogonal to the main aisle to racks with varying orientations, curvatures, and heights. We model this 3D layout problem as a series of 2D problems while accounting for obstructions faced by shoppers during their travel path (both uni- and bi-directional). We also validate our approach through a human subjects study in a Virtual Environment. Our findings suggest that curving racks in a layout with racks oriented at 90° could increase exposure by 3-121% over straight racks. Further, several layout designs could increase exposure by over 500% with only a 20% increase in floor space. In our second contribution, we introduce the Rack Orientation and Curvature Problem (ROCP) for a retail store, which determines the best rack orientation and curvature that maximizes marginal impulse profit (after discounting for floor space cost). We derive impulse profit considering the probability a shopper will see a product category, the probability the shopper will purchase a product from that category if seen, and the product category’s unit profit. We estimate the probability that a shopper will see a location through a novel approach that considers (i) the effective area of that location, (ii) probability distribution of a shopper’s head position based on real shopper head movements, and (iii) exposure estimates from our approach in Contribution 1. To solve the ROCP, we design a particle swarm optimization approach and conduct a comprehensive experimental study using realistic data. Our findings suggest that layouts with either high-acute and straight-to-medium-curved racks or high-obtuse and high-curved racks tend to maximize marginal impulse profit. Profit increases ranging from 70-233% over common rack layouts (orthogonal and straight racks) can be realized depending on the location policy of product categories. The sensitivity of these solutions to shopper volume, cost of floor space, travel direction, and maximum aspect ratio is also evaluated. The implications of our proposed models and findings are wide-ranging to retailers. First, they provide retailers with insights on how design parameters affect both exposure and marginal impulse profit; this can help avoid expensive experiments with layout changes. Second, they reveal hot-warm-cold spots for specific layout designs, allowing for effective product location assignments. Finally, these insights can help enhance shopper interactions with products (i.e., ability to see more products, find products faster), which can improve their shopping experience and drive up sales

Forecasting and Risk Management Techniques for Electricity Markets

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects

Advances in Food Processing (Food Preservation, Food Safety, Quality and Manufacturing Processes)

This e-book aims to compile advances in the area of food manufacturing including packaging to address issues of food safety, quality, fraud, and how these processes (new or old) could affect the organoleptic characteristics of foods, with the aim to promote consumers’ satisfaction. Moreover, food supply issues are explored. New and improved technologies are employed in the area of food manufacturing to address consumer needs in terms of quality and safety. The issues of research and development should be taken into account seriously before launching a new product onto the market. Finally, food fraud and authenticity are very important issues, and the food industry should focus on addressing them

Genetic Algorithm for Solving the Integrated Production-Distribution-Direct Transportation Planning

This paper proposes a model of integrated production, distribution and transportation planning for 4-echelon supply chain system that consists of a manufacturer using a continuous production process, a distribution center, distributors and retailers. By means of time-dependent demand at all retailers and direct transportation from one echelon to its successive echelons, the purpose of this paper is to determine production/replenishment and transportation policies at manufacturer, distribution center, distributors and retailers in order to minimize annually total system cost. Due to the proposed model is classified as a mixed integer non-linear programming so it is almost impossible to solve the model using the exact optimization methods and a lot of time is needed when the enumeration methods is applied to solve only a small scale problem. In this paper, we apply the genetic algorithm for solving the model. Using integer encoding for constructing the chromosome, the best solution is going to be searched. Compared with enumeration method, the difference of the result is only 0.0594% with the consumption time is only 0.5609% time that enumeration methods need