True Demand Research Articles

Estimating Primary Demand for a Heterogeneous-Groups Product Category under Hierarchical Consumer Choice Model

This paper discusses the estimation of primary demand (i.e., the true demand before the stockout-based substitution effect occurs) for a heterogeneous-groups product category that is sold in the department store setting, based on historical sales data, product availability, and market share information. For such products, a hierarchical consumer choice model can better represent purchasing behavior. This means that choice occurs on multiple levels: A consumer might choose a particular product group on the first level and purchase a product within that chosen group on the second level. Hence, in the present study, we used the nested multinomial logit (NMNL) choice model for the hierarchical choice and combined it with non-homogeneous Poisson arrivals over multiple periods. The expectation-maximization (EM) algorithm was applied to estimate the primary demand while treating the observed sales data as an incomplete observation of that demand. We considered the estimation problem as an optimization problem in terms of the inter-product-group heterogeneity, and this approach relieves the revenue management system of the computational burden of using a nonlinear optimization package. We subsequently tested the procedure with simulated data sets. The results confirmed that our algorithm estimates the demand parameters effectively for data sets with a high level of inter-product-group heterogeneity.

Maximum likelihood approach for demand unconstraining problem with censoring information incompleteness

The forecast of passenger demand in Revenue Management is usually based on historical booking data that reflects the number of sales rather than true demand which is constrained by booking limits. That is why the process of demand forecasting under such circumstances is called unconstraining. The goal of every unconstraining approach is to get empirical or theoretical estimation of true demand. The application of the maximum likelihood method to unconstraining problems in Revenue Management is advocated in the article based on the construction of the distribution function for the censored demand depending on availability of the censoring information. Numerical results are presented on comparative analysis of existing unconstraining methods and the method used in the article. It is demonstrated that maximum likelihood method proves to be more efficient in case of high percentage of censoring. Another important advantage of the method connected to the fact that it enables one to process the situation of censoring information incompleteness when some elements of the observed sample data are known to be censored or not and for the others this information is not available. Mathematical computer environment Wolfram Mathematica has been used for obtaining all the numerical results presented in the article.

A non-parametric approach to demand forecasting in revenue management

In revenue management, the profitability of the inventory and pricing decisions rests on the accuracy of demand forecasts. However, whenever a product is no longer available, true demand may differ from registered bookings, thus inducing a negative bias in the estimation figures, as well as an artificial increase in demand for substitute products. In order to address these issues, we propose an original Mixed Integer Nonlinear Program (MINLP) to estimate product utilities as well as capturing seasonal effects. This behavioral model solely rests on daily registered bookings and product availabilities. Its outputs are the product utilities and daily potential demands, together with the expected demand of each product within any given time interval. Those are obtained via a tailored algorithm that outperforms two well-known generic software for global optimization.

On the estimation of the true demand in call centers with redials and reconnects

In practice, in many call centers customers often perform redials (i.e., reattempt after an abandonment) and reconnects (i.e., reattempt after an answered call). In the literature, call center models usually do not cover these features, while real data analysis and simulation results show ignoring them inevitably leads to inaccurate estimation of the total inbound volume. Therefore, in this paper we propose a performance model that includes both features. In our model, the total volume consists of three types of calls: (1) fresh calls (i.e., initial call attempts), (2) redials, and (3) reconnects. In practice, the total volume is used to make forecasts, while according to the simulation results, this could lead to high forecast errors, and subsequently wrong staffing decisions. However, most of the call center data sets do not have customer-identity information, which makes it difficult to identify how many calls are fresh and what fractions of the calls are redials and reconnects.Motivated by this, we propose a model to estimate the number of fresh calls, and the redial and reconnect probabilities, using real call center data that has no customer-identity information. We show that these three variables cannot be estimated simultaneously. However, it is empirically shown that if one variable is given, the other two variables can be estimated accurately with relatively small bias. We show that our estimations of redial and reconnect probabilities and the number of fresh calls are close to the real ones, both via real data analysis and simulation.

A Maximum Entropy Joint Demand Estimation and Capacity Control Policy

We propose a tractable, data‐driven demand estimation procedure based on the use of maximum entropy (ME) distributions, and apply it to a stochastic capacity control problem motivated from airline revenue management. Specifically, we study the two fare class “Littlewood” problem in a setting where the firm has access to only potentially censored sales observations; this is also known as the repeated newsvendor problem. We propose a heuristic that iteratively fits an ME distribution to all observed sales data, and in each iteration selects a protection level based on the estimated distribution. When the underlying demand distribution is discrete, we show that the sequence of protection levels converges to the optimal one almost surely, and that the ME demand forecast converges to the true demand distribution for all values below the optimal protection level. That is, the proposed heuristic avoids the “spiral down” effect, making it attractive for problems of joint forecasting and revenue optimization problems in the presence of censored observations.

A recursive framework for time-dependent characteristics of tested and maintained standby units with arbitrary distributions for failures and repairs

The time-dependent unavailability and the failure and repair intensities of periodically tested aging standby system components are solved with recursive equations under three categories of testing and repair policies. In these policies, tests or repairs or both can be minimal or perfect renewals. Arbitrary distributions are allowed to times to failure as well as to repair and renewal durations. Major preventive maintenance is done periodically or at random times, e.g. when a true demand occurs. In the third option process renewal is done if a true demand occurs or when a certain mission time has expired since the previous maintenance, whichever occurs first. A practical feature is that even if a repair can renew the unit, it does not generally renew the alternating process. The formalism updates and extends earlier results by using a special backward-renewal equation method, by allowing scheduled tests not limited to equal intervals and accepting arbitrary distributions and multiple failure types and causes, including failures caused by tests, human errors and true demands. Explicit solutions are produced to integral equations associated with an age-renewal maintenance policy.

Timing of Product Allocation: Using Probabilistic Selling to Enhance Inventory Management

This paper examines probabilistic selling (PS) as an inventory-management mechanism, paying special attention to the impact of the timing of product assignment to buyers of probabilistic goods. In practice, sellers tend to offer probabilistic products only after major demand uncertainty has been resolved. By deferring product assignments, a firm is able to obtain more information about demand for each specific product before deciding which product to assign to consumers. However, our analysis demonstrates that PS can be an effective inventory-management mechanism even if the firm allocates products before knowing which product will be more popular and, thus, scarcer. Interestingly, we show that it can be more profitable for the firm to allocate products to consumers before, rather than after, learning the true demand for a product because, although early allocation imposes higher inventory costs (as a result of larger required inventory levels), it also enables the firm to charge higher prices. Our results also reveal that, when introducing probabilistic goods, the firm should order less inventory (relative to the case where probabilistic goods are not offered) if costs are very low but more inventory otherwise. Finally, we show that PS, as an inventory-management mechanism, can create a win–win situation, both improving profit and increasing social welfare. This paper was accepted by J. Miguel Villas-Boas, marketing.

A Newsvendor Who Chooses Informational Effort

We study a newsvendor who can acquire the services of a forecaster, or, more generally, an information gatherer (IG) to improve his information about demand. When the IG's effort increases, does the average ex ante order quantity rise or fall? Do average ex post sales rise or fall? Improvements in information technology and in the services offered by forecasters provide motivation for the study of these questions. Much depends on our model of the IG and his efforts. We study an IG who sends a signal to a classic single‐period newsvendor. The signal defines the newsvendor's posterior probability distribution on the possible demands and the newsvendor uses that posterior to calculate the optimal order. Each of the possible posteriors is a scale/location transform of the same base distribution. When the IG works harder, the average scale parameter drops. Higher IG effort is always useful to the newsvendor. We show that there is a critical value of order cost. For costs on one side of this value more IG effort leads to a higher average ex ante order and for costs on the other side to a lower average order. But for all costs, more IG effort leads to higher average ex post sales. We obtain analogous results for a “regret‐averse” newsvendor who suffers a penalty that is a nonlinear function of the discrepancy between quantity ordered and true demand.

Evolutionary Marginal Cost Pricing Scheme Implementation Based on Stochastic Traffic Flow Information

Traditionally, to implement the first-best marginal cost pricing scheme in a traffic network requires the information on the exact demand function or true origin-destination demand, which, however, is rarely available in practice. To overcome this dilemma, the trial-and-error method has been proposed to find the marginal cost toll through an iterative process using the observed traffic volumes. This method guarantees the convergence of tolls and flows to the system optimal state under the assumption of deterministic traffic conditions. However, in reality, the uncertainty of transportation network has been recognized well that induces the variability of link flow and travel time. Therefore, this paper proposes an evolutionary implementation method that iteratively finds the first-best marginal cost toll pattern according to the observed stochastic link flow information and the known travel time functions. The proof of the convergence of the iterative algorithm is presented. The paper also analyzes the effect of the sampling error of the link flow data on the convergence of the algorithm and shows that the biases from the flow observation will not affect the convergence. The numerical tests are provided for the illustration of the algorithm.

A survey of indoor positioning technology from dual aspects in the emergency responding

As the indoor positioning information is indispensable for the emergency responding, there are many contributions committed on this approach. Nevertheless, these efforts have paid more attention on the development of sensor techniques instead of the true user demand. Therefore, we try to fill this gap by investigating indoor positional applications from both evacuee view and responder view. Moreover, three typical scenarios have been proposed to shed light on combining existing localisation solutions in applications.

Pricing with Limited Knowledge of Demand

How should a firm price a new product for which little is known about demand? We propose a pricing rule that can be used if the firm can estimate (even roughly) the maximum price it can charge and still expect to sell some units, and the firm need not know in advance the quantity it will sell. The rule is simple: Set price as though the demand curve were linear. We show that if the true demand curve is one of many commonly used demand functions, or even a more complex function, and if marginal cost is known and constant, the firm can expect its profit to be close to what it would earn if it knew the true demand curve. We derive analytical performance bounds for a variety of demand functions, calculate expected profit performance for randomly generated demand curves, and evaluate the welfare implications of our pricing rule.

Demand Determinants for Urban Freight Consolidation Center - A Case of Korea

Incorrect estimation of demand is one of the key reasons for failures of the urban freight consolidation center (UFCC) which is becoming more popular due to its ability to improve economic, environmental, and social sustainability of the urban freight logistics system. In order to help estimate the true potential demand for UFCC in Korea, this research identified transport company’s characteristics that indicate demand for UFCC. Freight transport companies’ operational characteristics and their demand response information were collected by survey and in-depth interview. The data were analyzed by rank order correlation and ordered logistic regression. All methods suggest that the estimation of demand for UFCC should be focused on those transport companies that generate large revenue but mainly use light (small and medium) capacity cargo vehicles. Other identified potential users are food and beverage transporters, transporters of distribution companies’ goods, container vehicles, and long contracted & self-owned vehicles. Those companies dealing with container and express parcel deliveries and making predominantly home deliveries could be also a good prospect for UFCC.

The New (Old) Case for the Ethics of Business

In this paper, I argue for the ethics of business based on the way that business activity may embody a vocation to partake in “the Good.” Following a Platonist framework for ethics and recent work on vocations by Robert M. Adams, I argue that understanding the ethics of vocations allows us to avoid the charges that business persons have to do something more for others—often couched in terms of social responsibility, sustainability, or consideration of stakeholders—in order to legitimize their careers ethically. Rather, I claim, the ethics of a business vocation, as in any vocation, rests first and foremost in the way that a person pursues projects that answer an invitation to partake in the good things of this world. Thus, the promotion of the well-being of others, while ethically admirable and constitutive of some vocations, is not fundamental for understanding the ethics of vocations themselves, even in business. There are important implications for the ethics of markets, and I also consider a recent challenge to my approach that claims that true business vocations demand a more direct promotion of the well-being of others.

An Energy-Based Assessment on Dynamic Amplification Factor for Linear Static Analysis in Progressive Collapse Design of Ductile RC Frame Structures

Progressive collapse is a mechanical process that exhibits nonlinear and dynamic characteristics. The nonlinear dynamic effect on the progressive collapse resistance demand can be accurately evaluated by the nonlinear dynamic (ND) method. In engineering practice, however, the simplified and easy-to-use linear static (LS) method is often adopted. That is accomplished by using a dynamic amplification factor (DAF) to correct the LS resistance demand to approximate the true ND resistance demand. In this paper, the analytical expression of the DAF is established based on the energy conservation principle. The collapse-resisting substructure is firstly simplified as a single-degree-of-freedom (SDOF) equivalent. Then the energy conservation equation and the static balance equation of the SDOF equivalent are established to obtain the ND and LS demands. Finally, the DAF is obtained by dividing the ND demand by the LS demand. The DAF is validated through a series of the numerical examples including a SDOF system, a 3-storey planar frame and an 8-storey 3-D RC frame model structures.

Demand Estimation When There are Unobservable Substitutions Amongst Choice Alternatives

Accurate estimates of the future demand and the substitution probabilities between products are important inputs for retail assortment optimization. However, these quantities are difficult to estimate as the estimation involves understanding and modeling of consumer’s choice behavior when a complete choice set is available as well as when the choice set is incomplete (i.e., their response to out-of-stock situations). In this paper, we propose a prospect theory based reference dependent preference structure for consumer choice and use the information available in store scanner sales and inventory data to estimate both the true demand for the products as well as the substitution probabilities between products. We use a combination of theoretical arguments, simulations, and empirical analysis to establish that the proposed reference dependent logit (RDL) model yields robust and efficient estimates. We also compare the performance of RDL against the multinomial logit, two models that use exogenous substitution probabilities, and experts’ opinions regarding what the true substitution probabilities could be.

A Maximum Entropy Joint Demand Estimation and Capacity Control Policy

We propose a tractable, data-driven demand estimation procedure based on the use maximum entropy (ME) distributions, and apply it a stochastic capacity control problem motivated from airline revenue management. Specifically, we study the two fare-class "Littlewood" problem in a setting where the firm has access to only potentially censored sales observations. We propose a heuristic that iteratively fits an ME distribution to all observed sales data, and in each iteration selects a protection level based on the estimated distribution. When the underlying demand distribution is discrete, we show that the sequence of protection levels converges to the optimal one almost surely, and that the ME demand forecast converges to the true demand distribution for all values below the optimal protection level. That is, the proposed heuristic avoids the "spiral down" effect, making it attractive for problems of joint forecasting and revenue optimization problems in the presence of censored observations.

Patient access again

So, here we go with another model of GP access that will sort us all out. ‘Patient access’ promises to transform the delivery of primary care.1 But before the NHS rushes headlong into another poorly understood change, we should understand its impact. Changing GP appointment systems is fraught, as Tony Blair discovered on BBC’s Question Time in 2005, when, in his desire to quickly fix delays to see a GP, the imposed targets led to the unintended consequences of many practices offering only a ‘book on the day’ system and angered many users. In my practice we’ve been practising the same model for about 4 months and even as a fairly cohesive team, we disagree on whether this is the solution. This system has ruthlessly exposed our true patient demand, maybe 50% …

The Bayesian Approach to the Economics of One-Time Inventory Policy under Uncertainty

This paper deals with the one time buy inventory problem of such products for which there is limited information available for demand prediction. The unknown mean demand is treated as random variable whose prior density is defined by a Beta density function. The Baye's rule is then used to redefine the economics of the inventory model under uncertainty about the mean demand. Assuming that no cost is incurred on collection of data, the expected cost of ignorance becomes the limiting value of the expected savings which accure due to the increase in knowledge about the true mean demand.

Dynamic Pricing Through Data Sampling

We study a dynamic pricing problem, where a firm offers a product to be sold over a fixed time horizon. The firm has a given initial inventory level, but there is uncertainty about the demand for the product in each time period. The objective of the firm is to determine a dynamic pricing strategy that maximizes revenue over the entire selling season. We develop a tractable optimization model that directly uses demand data, therefore creating a practical decision tool. We show numerically that regret-based objectives can perform well when compared with average revenue maximization. This modeling approach could be particularly important for risk-averse managers with limited access to historical data or information about the true demand distribution. Finally, we provide theoretical performance guarantees for this sampling-based solution.

A Critical Assessment of Legal Identity: What It Promises and What It Delivers

Universal provision of legal identity, most commonly birth registration, is viewed as both an intrinsic human right and an instrumental development necessity. Legal identity is commonly associated with citizen access to certain benefits and opportunities, protection of human rights, and generation of vital statistics to inform development planning. Research conducted in Nepal, Cambodia, and Bangladesh demonstrates that significant obstacles to obtaining legal identity include financial and procedural barriers, discriminatory laws and policies, institutional incapacity, and citizens’ lack of knowledge and motivation. Robust alternative markets for legal identity reveal the true public demand for identity documents. In the developing country context, the provision of legal identity alone does not necessarily deliver desired development outcomes. Thus, universal civil registration is a necessary and worthy goal, but focus should remain on the ultimate development goals behind universal registration.