Classification-based model selection in retail demand forecasting

Retailers supply a wide range of stock keeping units (SKUs), which may differ for example in terms of demand quantity, demand frequency, demand regularity, and demand variation. Given this diversity in demand patterns, it is unlikely that any single model for demand forecasting can yield the highest forecasting accuracy across all SKUs. To save costs through improved forecasting, there is thus a need to match any given demand pattern to its most appropriate prediction model. To this end, we propose an automated model selection framework for retail demand forecasting. Specifically, we consider model selection as a classification problem, where classes correspond to the different models available for forecasting. We first build labeled training data based on the models’ performances in previous demand periods with similar demand characteristics. For future data, we then automatically select the most promising model via classification based on the labeled training data. The performance is measured by economic profitability, taking into account asymmetric shortage and inventory costs. In an exploratory case study using data from an e-grocery retailer, we compare our approach to established benchmarks. We find promising results, but also that no single approach clearly outperforms its competitors, underlying the need for case-specific solutions.     

A note on the Robust Trend and ARARMA methodologies used in the M3 Competition

This brief note describes two of the forecasting methods used in the M3 Competition, Robust Trend and ARARMA. The origins of these methods are very different. Robust Trend was introduced to model the special features of some telecommunications time series. It was subsequently found to be competitive with Holt’s linear model for the more varied set of time series used in the M1 Competition. The ARARMA methodology was proposed by Parzen as a general time series modelling procedure, and can be thought of as an alternative to the ARIMA methodology of Box and Jenkins. This method was used in the M1 Competition and achieved the lowest mean absolute percentage error for longer forecasting horizons. These methods will be described in more detail and some comments on their use in the M3 Competition conclude this note.     

Predicting relative forecasting performance: An empirical investigation

The relative performances of forecasting models change over time. This empirical observation raises two questions. First, is the relative performance itself predictable? Second, if so, can it be exploited in order to improve the forecast accuracy? We address these questions by evaluating the predictive abilities of a wide range of economic variables for two key US macroeconomic aggregates, namely industrial production and inflation, relative to simple benchmarks. We find that business cycle indicators, financial conditions, uncertainty and measures of past relative performances are generally useful for explaining the models’ relative forecasting performances. In addition, we conduct a pseudo-real-time forecasting exercise, where we use the information about the conditional performance for model selection and model averaging. The newly proposed strategies deliver sizable improvements over competitive benchmark models and commonly-used combination schemes. The gains are larger when model selection and averaging are based on both financial conditions and past performances measured at the forecast origin date.     

Screening the impacts of municipal service policy changes

The planning of municipal service delivery systems requires accurate forecasts of demand, and particularly of the effects the quality of service delivery has on demand. A metholology for this problem should meet three criteria, if it is to be useful for municipal planning: it must be low-cost and use generally available data; it must be based on user behavior, so that the effects of policy changes can be correctly attributed; and it must allow testing of the transferability of the results, since this is required for general forecasting use. This paper develops such a methodology, based on econometric analysis of data from a number of service areas within a number of regions, forming a double cross-section. Empirical tests of the methodology were performed for two local government services where the effect of service quality on demand is important: sewer and highway construction, which have been hypothesized to affect the patterns of development within regions; and solid waste collection, where the level of service provided affects how much waste enters the collection system and how much is littered, burned or recycled. The two case studies and other analyses suggest that the methodology is a useful tool for testing whether policy changes have an effect on the demand for service, but not for accurate demand forecasting. Thus, these simple models are relevant for the role of screening the effect of policy changes, but more detailed and localized approaches are necessary for system design.     

Location and service mix decisions for a managed health care network

For managers of managed health care organizations, the problem of designing a competitive multiple facility network cannot be solved by existing mathematical models. This paper thus presents a nonlinear integer model for determining a facilities design strategy that embodies the economic tradeoffs encompassed in a competitive strategy: minimizing cost and maximizing market share. The integrated location and service mix model determines the number, location and service offerings of facilities that maximize profitability in a two-level hierarchical referral delivery network where an organization's market share is represented by a multiplicative competitive interaction model. To demonstrate the usefulness of the proposed integrated model, a series of problems is solved by an interchange heuristic and compared to the solutions derived by a simpler approach that ignores market competition.     

Determinants of consumer demand for circular economy products. A case for reuse and remanufacturing for sustainable development

We investigate determinants of consumer demand for circular (reused and remanufactured) products. Based on exploratory choice-based conjoint experiments with a sample of 800 adults in the United Kingdom, we examine two types of premium segment electronic appliances: a mobile phone and a robot vacuum cleaner. We find that consumers prefer partly circulated products over fully or not at all circulated products and that circular products can likely successfully enter the existing market at the retail price of a new product. Interestingly, circular products compete for market share primarily with new products, leaving the market share of second-hand options less affected. The results show a promising path for firms considering a transition to circular business models.     

Mismatch and the Forecasting Performance of Matching Functions

This paper investigates the role of structural imbalance between job seekers and job openings for the forecasting performance of a labour market matching function. Starting from a Cobb–Douglas matching function with constant returns to scale (CRS) in each frictional micro market shows that on the aggregate level, a measure of mismatch is a crucial ingredient of the matching function and hence should not be ignored for forecasting hiring figures. Consequently, we allow the matching process to depend on the level of regional, qualificatory and occupational mismatch between unemployed and vacancies. In pseudo out‐of‐sample tests that account for the nested model environment, we find that forecasting models enhanced by a measure of mismatch significantly outperform their benchmark counterparts for all forecast horizons ranging between one month and a year. This is especially pronounced during and in the aftermath of the Great Recession where a low level of mismatch improved the possibility of unemployed to find a job again. The results show that imposing CRS helps improve forecast accuracy compared to unrestricted models.     

公交实时信息化服务对乘客出行行为意向影响

为了解城市公交信息化服务对公交乘客出行方式转移的影响,以上海市杨浦区为例,进行关于公交信息化服务下乘客出行方式转移的调查,获取个人属性、公交出行经历、公交出行评价、出行方式转移意向以及公交信息服务需求等信息。从公交信息使用者角度,重点关注公交信息化服务对乘客出行方式转移的影响,分别构建信息化服务情境下(公交到站时间与计划出发时间相差过大和车辆未在提示的时间内到达)的公交出行方式转移二项logit模型。模型结果表明:乘客平均候车时间、是否经常使用公交到站服务、是否有道路拥挤车辆未按时进站等原因而产生出行延误的经历、公交信息准确性评价与是否有必要提示同公交线路连续班次的到站时间等因素对不同情景下的乘客出行方式转移具有显著影响。     

Modeling the demand and supply in a new B2B-upstream market using a knowledge updating process

Business-to-Business (B2B) services companies invest heavily in acquiring very expensive assets that they hire out to serve their clients (e.g. UPS buys huge warehouses and hires them out to companies), and hence they engage in careful long-term planning and forecasting, especially when it concerns a new market. It is interesting to note that the client-firms, on the other hand, decide to hire those assets based mostly on the prevailing short-term market forces. Hence, it is important for the companies which provide the assets for hire to also build the prevailing short-term market trends into their long-term forecasting and planning. In this paper, we develop a model for tracking these two simultaneously evolving and interacting patterns, namely the asset-availability (i.e. supply) and utilization (i.e. demand) patterns, in order to better understand the underlying processes, and thereby provide a basis for better forecasting. We test our models using three sets of data collected from the oil drilling industry, and find the proposed model to provide a good fit and forecasting efficiency.     

Strategic pricing of differentiated consumer durables in a dynamic duopoly: A numerical analysis

Under different modes of competitive pricing behavior, profit-maximizing price trajectories are derived for durable products in a dynamic duopoly. Open-loop co-operative and non-cooperative pricing behavior is analyzed within a comprehensive model where sales of differentiated products are described by interlocked diffusion processes with realistic demand characteristics. Because of analytic complexity, the optimal trajectories implied by the control and differential-game problems are derived numerically across an extensive set of plausible market scenarios. Manipulation of initial market conditions enables derivation of optimal competitive pricing as a function of timing of entry.     

Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices

In this article we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price; hence, we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by ordinary least squares and the elastic net, and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component in a mean reverting jump diffusion model in the residuals. The models’ forecasts are evaluated with use of four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included. It is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure to properly forecast correlated time series.     

Short interval forecasting of emergency phone call (911) work loads

There has been an increasing emphasis over the last 5 to 10 years to improve productivity in the Service Sector of the U.S. economy. Much of the improvement obtained by these managers has come about through better scheduling of the work force in these organizations. Effective scheduling of this personnel requires good estimates of demand, which may exhibit substantial variations between days for certain times of the year. The Indianapolis Police Department (IPD) Communications area is one such organization that exhibits varying workloads and is interested in improving staff scheduling of dispatch operators.This article explores the use of six different forecasting techniques for predicting daily emergency call workloads for the IPD's communications area. Historical call volume data are used to estimate the model parameters. A hold-out sample of five months compares forecasts and actual daily call levels. The forecast system utilizes a rolling horizon approach, where daily forecasts are made for the coming month from the end of the prior month. The forecast origin is then advanced to the end of the month, where the current month's actual call data are added to the historical database, new parameters are estimated, and then the next month's daily estimates are generated. Error measures of residual standard deviation, mean absolute percent error, and bias are used to measure performance. Statistical analyses are conducted to evaluate if significant differences in performance are present among the six models.The research presented in this article indicates that there are clearly significant differences in performance for the six models analyzed. These models were tailored to the specific structure and this work suggests that the short interval forecasting problems faced by many service organizations has several structural differences compared to the typical manufacturing firm in a made-to-stock environment. The results also suggests two other points. First, simple modeling approaches can perform well in complex environments that are present in many service organizations. Second, special tailoring of the forecasting model is necessary for many service firms. Historical data patterns for these organizations tend to be more complex than just trend and seasonal elements, which are normally tracked in smoothing models. These are important conclusions for both managers of operating systems and staff analysts supporting these operating systems. The design of an appropriate forecasting system to support effective staff planning must consider the nature, scope, and complexity of these environments.     

Adaptive learning,endogenous uncertainty,and asymmetric dynamics

I present a simple model where forecasting confidence affects aggregate demand. It is shown that this model has similar stability properties, under statistical and evolutionary learning, as a model without a confidence affect. From this setup, I introduce “Expectational Business Cycles” where output fluctuates due to learning, heterogeneous forecasting models and random changes in the efficient forecasting model. Agents use one of two forecasting models to forecast future variables while heterogeneity is dictated via an evolutionary process. Increased uncertainty, due to a shock to the structure of the economy, may result in a sudden decrease in output. As agents learn the equilibrium, output slowly increases to its equilibrium value. Expectational business cycles tend to arrive faster, last longer and are more severe as agents possess less information.     

The use of RAS and related models in manpower forecasting

With the increasing availability of statistics describing the occupational structures of different industries manpower forecasters are beginning to develop more sophisticated models. The economic rationale of such models has tended to be obscured by the mathematics involved and an understandable eagerness to put the data to use in forecasting. In addition, the systematic testing of the explanatory power of these models has been neglected where it has not been hampered by the shortage of data series. The RAS model has featured prominently in manpower discussions and this paper attempts an evaluation of its predictive ability and economic interpretation subject to the limits imposed by the data available for the British engineering industry. It is argued that such a model plays a useful initial role in the development of models for sectoral manpower forecasting but must lead on to a more sensitive treatment of the labour market and the decision problem of investing in human capital.     

Daily peak electrical load forecasting with a multi-resolution approach

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders in the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are (a) a general and formal introduction to the multi-resolution modelling approach, (b) a discussion of modelling approaches at different resolutions implemented via generalised additive models and neural networks, and (c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.     

基于IOWA算子的组合预测模型在物流需求预测中的应用

根据2000~2009年宁波市物流需求的数据,采用灰色GM 1,,1,模型和一元线性回归模型进行组合优化,建立了基于诱导有序加权平均(IOWA)算子的物流需求量组合预测模型。结果表明基于IOWA算子的组合预测模型能有效提高预测精度,说明了该方法用于物流需求预测的可行性和有效性,并在此基础上对2010~2013年宁波市物流需求作出预测。     

基于创新扩散理论的ERP应用实证研究

结合大量文献及对企业ERP实施的调研分析,基于创新扩散理论(DOI)构建了包含企业内外部影响因素(ERP兼容性、ERP复杂性、ERP相对优越性、企业管理机制、企业复杂性、外部竞争压力、高层管理者支持、业务流程重组和实施顾问)和衡量ERP实施成功的两大标准(ERP扩散广度和ERP扩散深度)的ERP创新实施成功模型,提出相应理论假设;并以中国企业为样本进行问卷调研和统计分析,实证结果表明,其中ERP相对优越性、ERP复杂性、高层管理者支持、企业复杂性、实施顾问、竞争压力和企业管理机制对ERP实施成功具有显著影响。     

供应商供给策略形成的牛鞭效应分析

在采用满足市场需求经营策略下，针对供应链上成员共享与没有共享信息的情况，我们分别给出了供应链上各成员为满足用户需求而对最终产品需求进行预计的模型，利用所建立的模型讨论了该经营策略形成的牛鞭效应，并指出了供应链上每往上游前进一步，最终产品需求预测放大的数量。最后，讨论了信息共享对控制该种策略下牛鞭效应的效果。     

Improving Electricity Market Price Forecasting with Factor Models for the Optimal Generation Bid

In liberalized electricity markets, the electricity generation companies usually manage their production by developing hourly bids that are sent to the day‐ahead market. As the prices at which the energy will be purchased are unknown until the end of the bidding process, forecasting of spot prices has become an essential element in electricity management strategies. In this article, we apply forecasting factor models to the market framework in Spain and Portugal and study their performance. Although their goodness of fit is similar to that of autoregressive integrated moving average models, they are easier to implement. The second part of the paper uses the spot‐price forecasting model to generate inputs for a stochastic programming model, which is then used to determine the company's optimal generation bid. The resulting optimal bidding curves are presented and analyzed in the context of the Iberian day‐ahead electricity market.