Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment

Inaccurate forecasts of demand during promotions diminish the already meager profit margins of retailers. No forecasting method described in the literature can accurately account for the combination of seasonal sales variations and promotion-induced sales peaks over forecasting horizons of several weeks or months. We address this research gap by developing a forecasting method for seasonal, frequently promoted products that generates accurate predictions, can handle a large number of sales series, and requires minimal training data. In our method's first stage, we forecast the seasonal sales cycle by fitting a harmonic regression model to a decomposed training set, which excludes promotional and holiday sales, and then extrapolate that model to a testing set. In the second stage, we integrate the resulting seasonal forecast into a multiplicative demand function that accounts for consumer stockpiling and captures promotional and holiday sales uplifts. The final model is then fitted using ridge regression. We use sales data from a grocery retailing chain to compare the forecasting accuracy of our method with popular seasonal and promotion demand forecasting models at multiple aggregation levels for both short and long forecasting horizons. The significantly more accurate forecasts generated by our model attest to the merit of the approach developed here.     

REPLACEMENT PARTS MANAGEMENT: THE VALUE OF INFORMATION

Exponential smoothing (ES) and weighted moving average (WMA) are the predominant methods used to predict future demand for replacement parts. They require simple calculations and make use of information readily available. By gathering more information and doing additional calculations, more accurate forecasts can be developed. However, the cost of collecting the additional data could exceed the inventory cost savings from the better demand forecast. This paper presents a straightforward method for determining when the benefits of a more complex forecasting method outweigh the total costs required to use the method.     

Demand forecasting based on natural computing approaches applied to the foodstuff retail segment

The purpose of this paper is to compare the accuracy of demand forecasting between two classical linear forecasting models (Autoregressive and Integrated Moving Average -ARIMA and Holt-Winter) and two nonlinear forecasting models based on natural computing approaches (Wavelets Neural Networks - WNN and Takagi-Sugeno Fuzzy System - TS), all applied to the aggregated retail sales of three groups of perishable food products from 2005 to 2013. Moreover, this paper evaluates the impact of demand forecasting accuracy on the demand satisfaction rate and on the overall economic performance of retail business operations. The most accurate model, WNN, had a demand satisfaction rate of 98.27% for Group A, 98.83% for Group B and 98.80% for Group C. WNN estimated a loss of revenue of R$1329.14 million/year with a minimum loss of 166 tons/year, which means that the results of WNN are 37.67% more efficient than the TS, 57.49% higher than the ARIMA and 76.79% higher than HW. This paper presents three main contributions: (i) it examines a question not evaluated in the literature on demand forecasting based on natural computing approaches in the foodstuff retail segment that generates better practical results, (ii) it proposes that a single forecasting model could be applied to different product groups and serves the organization as a whole with a good relationship between the cost and the benefit of the process and (iii) like previous studies, it proves that demand forecasting plays an important role and can generate a competitive advantage for the organization to be incorporated into its strategy.     

Demand and Revenue Management of Deteriorating Inventory on the Internet: An Empirical Study of Flash Sales Markets

In this study, we investigate demand and revenue management of deteriorating inventory in flash sales markets (FSM). Retailers operating FSM source excess inventories of products from secondary markets in fixed lot sizes and offer them as part of deals that get no replenishments and expire after running for predetermined periods (e.g., 24 hr) or when they sell out, whichever occurs first. We develop a demand forecasting model that incorporates the effects of sentiments conveyed by consumers in discussion forum posts associated with different deals on the deals’ empirical demand rates. We then conduct a survival analysis to find that the empirical demand rates projected from our forecasting model are significant predictors of the deals’ actual time to stockout, even after controlling for their initial inventory provisions and markdowns. We also find that the predicted effects of these demand rates on stockout times are stronger at low markdowns. Our investigation offers insights into different strategies that sellers operating FSM can pursue to improve their inventory performance. These strategies involve decisions that sellers must make both a priori, before deals start, and a posteriori, according to real‐time detection of departures from projected demand rates as deals run their course.     

The Informational Role of Manufacturer Returns Policies: How They Can Help in Learning the Demand

Returns policies are usually thought of as being a way to insure retailers against excess inventory. The work of Pellegrini (1986), Chu (1993), Lin (1993) and Padmanabhan and Png (1997) highlights the fact that there is considerably more to returns policies than just a mechanism for insurance. Our work identifies a heretofore undocumented rationale for returns policy: its role in learning the demand for a new product. The model of manufacturer–retailer interaction assumes that the demand is uncertain but correlated across time periods. Thus, its true nature is revealed over time via the realization of sales. The speed of such learning depends on the size of the inventory at the retail level which in turn can be strategically influenced by the manufacturer through a returns policy. In such environments, we show that a returns policy may be a way for the manufacturer to learn the demand of a new product.     

Selecting A Forecasting Method For Investory Control: A Safety Stock Based Criterion

The paper presents results related to the application of several forecasting methods to problems of inventory control. In particular it is shown that an accuracy measure based on an inventory control criterion is more useful in ranking the forecasting methods than currently used measures based on forecast errors. Résumé Cette étude présente des résultats reliés à l'application de plusieurs méthodes de prévision dans un contexte de gestion des stocks. Il est démontré en particulier qu'une mesure de précision basée sur un critère de gestion des stocks est plus utile pour évaluer les méthodes de prévision que les mesure usuelles qui fondées sur les erreurs de prévisions.     

基于K线图的收益管理浮动价格博弈

研究价格浮动与座位存量分配控制相结合,通过价格动态调整以缩小预测误差所带来的航班收益损失。首次在收益管理系统中融入顾客与航空公司的博弈性,提出收益管理K线图研究思想,以直观反映价格波动和市场变化情况。虚拟舱位容量概念的提出,以减少旅客市场的季节性影响,将最大超售量作为虚拟舱位容量的一部分。虚拟舱位容量算法简洁易行。仿真运算结果显示,该方法通过直接获取潜在需求数据,能够提高航班座位利用率,并使航空公司不同航班在原有收益管理系统基础上分别提高2%至10%甚至更高的收益。     

A COMPARISON OF THE ECONOMIC ORDER QUANTITY AND QUICK RESPONSE INVENTORY REPLENISHMENT METHODS

When determining order quantity, logistics managers can choose between ordering the Economic Order quantity (EOQ) or a Quick Response (QR) quantity. QR is a general term that collectively describes several rapid‐replenishment inventory methods such as Just‐In‐Time or Continuous Replenishment. The QR order quantity is defined as the minimum inventory needed to support operations until the next delivery. The EOQ and the QR methods are substantially different because the EOQ minimizes the joint cost of ordering and holding inventory whereas the QR method minimizes only the cost of holding inventory. The goals of this research are to compare the costs of the two methods and to propose rules that help managers select the more appropriate method to use in specific situations.     

Store inventory can affect demand: Empirical evidence from magazine retailing

In retailing, inventory analysis and inventory practice have traditionally been based on the assumption that underlying demand does not vary with inventory levels. A growing body of research supports the contention that the validity of this assumption has significant implications for optimal inventory policies. The concern for such inventory effects motivated a major US magazine publisher to conduct the market study documented in this article. It presents empirical evidence that demand can indeed vary with inventory, and it quantifies the magnitude of these inventory effects which are twofold. An inventory decrease for one brand can, first, result in a decrease of demand for the brand and, second, in an increase of demand for a competing brand. These observations support the expansion of the traditional Newsvendor model to include inventory effects as well as the practice to make inventory decisions for retail categories rather than individual brands.     

How inventory management systems mistreat retail project quantity items and other bimodally distributed products

To insure appropriate inventory levels in retail outlets, replenishment managers concern themselves with both timing of shipments and quantity of product available within the store. While organizations regularly use inventory management or enterprise resource planning systems to procure inventories, we find that these systems can incorrectly manage a sizable group of products found within retail organizations. To illustrate, we explore a collection of goods called retail project quantity items, subsequently explaining how customers purchase and use these items in a fashion that leads to a bimodal distribution of demand. We then employ simulations to demonstrate how the use of a bimodal lead-time demand distribution, rather than a normal or Poisson distribution, improves respective product service levels. Based on these results, we offer a modified reorder point heuristic and discuss how inventory managers should alter existing replenishment processes to insure sufficient stocks of retail project quantity items. Lastly, we discuss broader implications for inventory management systems and bimodally distributed products.     

Top‐Down Versus Bottom‐Up Demand Forecasts: The Value of Shared Point‐of‐Sale Data in the Retail Supply Chain

Accurate demand forecasts are critical to maintaining customer service levels and minimizing total costs, yet increasingly difficult to achieve. Using weekly point‐of‐sale (POS) and order data for 10 ready‐to‐eat cereal stock‐keeping units from 18 regional U.S. grocery distribution centers, this research empirically investigates two demand forecasting issues: (1) the accuracy of top‐down versus bottom‐up demand forecasts; and (2) whether shared POS data improve demand forecast accuracy. The results reveal a previously unexplored relationship between demand forecast methodology and the use of shared POS data. We find that the superiority of the top‐down or bottom‐up forecasting as the more accurate demand forecast method depends on whether shared POS data are used.     

THE IMPACT OF POINT‐OF‐SALE DATA INACCURACY AND INVENTORY RECORD DATA ERRORS

The presence or absence of error in point‐of‐sale (POS) data and inventory system records directly affects retailer performance. This study identifies various error sources in retail supply chains and studies the influence of inventory and POS (demand) errors in a simulated retail outlet according to fill rate and average inventory. Other things being equal, we find that inventory record error reduces fill rate more than demand error. This study adds further evidence to other studies that suggest the costs caused by errors in POS systems may be overstated.     

Technology revenue management system for customer groups in hotels

This paper discusses revenue management; a technique that focuses on decision making that will maximize profit from the sale of perishable inventory units. New technologies management plays an important role in the development of revenue management techniques. Each new advancement in technology management leads to more sophisticated revenue business capabilities. Today decision support revenue management systems and technologies management are crucial factors for the success of businesses in service industries. This paper addresses the specific case of customer groups in hotels. This paper introduces a new decision support system that sets the revenue maximization criteria for a hotel. The aforementioned system includes a set of demand forecasting methods for customers and addresses a general case considering individual guests and customer groups. The system also incorporates deterministic and stochastic mathematical programming models that help to make the best decisions. The actual revenue depends upon which reservation system the hotel uses. A simulation engine makes a comparison between different heuristics of room inventory control: the results include performance indexes such as occupancy rate, efficiency rate, and yield; it compares results and chooses one of them. The system proves its suitability for actual cases by testing against actual data and thus becoming an innovative and efficient tool in the management of hotels' reservation systems.     

Changing the game to compete: Innovations in the fashion retail industry from the disruptive business model

Unprecedented competition and emergent technologies have posed a challenge to many traditional retailers in recent years. Yet within this competitive environment, emerging innovative business models have thrived and successfully disrupted the industry. We analyze the nature of disruptive business-model innovations and the ways they disrupt the fashion retail industry. To that end, we examine three disruptors in the industry: born-digital brands, AI-enabled demand forecasting and product design, and collaborative consumption. After introducing the concept of disruptive business-model innovation, we discuss the three disruptors’ effects on the fashion industry. We find that all of these models keenly answer fundamental needs unmet by current business models, such as offering quality products at a competitive price, curated services, and sustainable consumption. At the same time, all three disruptors suggest effective operation models for handling demand uncertainty, inventory management, and timely responses to the market, all of which are inherent issues for current push supply chains and forecast-based, inventory-driven systems. Based on this analysis, we discuss important implications for both academics and industry practitioners.     

Stock replenishment policies for a vendor-managed inventory in a retailing system

Using Vendor Managed Inventory (VMI), the vendor determines the replenishment decisions at the location of buyers (retailers). This strategy is used primarily for handling demand fluctuations stemming from the Bullwhip effect, leading the system to prevent from holding excessive inventory that result in a reduction in the overall cost of the supply chain. The main advantages of VMI for vendors are higher levels of accessibility to inventory information and more direct contact with the customers. Similarly, VMI has some pros for the buyers, such as shared risk with upper levels of supply chain and reduction in their holding costs of inventory. In this paper, a vendor-managed inventory system is developed containing one vendor and two buyers in which the main assumption is that back-ordering and lost sales are permitted. In this system, (r, Q) and (R, T) replenishment policies are compared according to their performances to see which one performs more cost-efficiently when partial back-ordering is allowed. In accordance, mathematical models utilizing (r, Q) and (R, T) replenishment policies are developed, and algorithms for deriving the optimal replenishment decision variables are proposed. Moreover, significant differences between the two replenishment policies are discussed. The main finding obtained by this research is that when shortage is permitted, both (r, Q) and (R, T) replenishment policies under VMI have pros and cons in different contexts.     

EXPRESSING INVENTORY CONTROL POLICY IN THE TURNOVER CURVE

Estimation of the inventory level for an entire class of items is a valuable time saver when control of inventories at the aggregate level, rather than the item level, is of interest. Inventory approximation by location in supply chain network configuration and evaluation of inventory control policy shifts, are two examples of application. In this article, various popular inventory policies are related to a general function known as an inventory turnover curve that expresses inventory levels from the combined demand of multiple items. By knowing some basic item characteristics of representative items in a product class, the type of inventory policy being used, and the current aggregate inventory level, an inventory turnover curve can be constructed. This resulting turnover curve can be used to estimate inventory levels within 4.6%, on the average, of theoretically predicted ones.     

供应链中的CPFR发展研究

CPFR是一个用来协调各级供应链管理活动的工具,包括供应链贸易伙伴间的生产和采购规划、需求预测和库存补货。从CPFR的起源、目标和过程,以及实施CPFR的利益、驱动因素以及实现CPFR所存在的障碍等方面,分析CPFR的未来发展趋势。     

A dynamic forecasting model for nursing manpower requirements in the medical service industry

Today there is an increasing demand for quality health care throughout the world. Such demand requires the medical service industry to equip with high technologies and high caliber professionals (e.g., doctors and nurses), to contain cost and improve quality of care. Since the medical service industry constantly suffers from the shortage of nursing manpower, forecasting the supply and demand of nursing manpower is an important issue. This study proposes a forecasting model for nursing manpower requirement using system dynamics. Data were collected from authorized institutions in Korea and were used as inputs of the model. The results show that the demand for nursing manpower exceeded the supply during the forecasting period (2006–2020), which requires both national and the medical service industry attention for sustained quality health care.     

Forecasting Real Inventories and the Anomaly of Money Illusion

While the transmission mechanism of inventory behavior in the business cycle has been studied, less effort has been devoted to applied forecasting of inventory change. Inventory fluctuations have accounted for a sizable portion of the changes in U.S. GDP during recessions over the past fifty years. In this paper, we report on out-of-sample forecasts of manufacturing and trade inventories generated by regression and neural network methodology. Our forecasting model is Metzlerian in approach, in that the divergence between actual and targeted sales is hypothesized as the primary cause of inventory imbalance. Our forecasts also rely on the slow adjustment of inventory investment to sales surprises. However, the likely presence of money illusion is a caveat to users, and we address several distortions it introduces to inventory management measures.