Forecast Customer Research Articles

A Study On Reviewer Characteristics On E-Commerce Applications

Online testimonials have actually really come to be an essential source of information for customers before making an enlightened procurement decision. Early endorsements of a point typically have a tendency to have a high end result on the being successful item sales. In this paper take the task to have a look at the regimens characteristics of very early consumers utilizing their uploaded evaluations on considerable ecommerce systems. Below quantitatively define extremely early consumers based upon their score routines, the helpfulness rankings obtained from others as well as additionally the link of their assessments with item charm. Right here discover that to start with an exceptionally very early customer typically tends to note a greater normal ranking score; as well as also 2nd of all a very early client generally has a tendency to release much better statements. Our analysis of product analyses additionally reveals those exceptionally very early clients' ratings along with advises a one-of-a-kind margin-based embedding variation for truly really early customer forecast. In addition to moreover boosted the version with ranking and also additionally pre-owned two thinking’s for spammer expedition. One is if precise same Statement along with different user ID's under particular similar category/product with accurate exact same tally. Substantial experiments on 2 different ecommerce datasets have actually revealed that our assessment exceeds a series of inexpensive demands.

Data Set: 187 Weeks of Customer Forecasts and Orders for Microprocessors from Intel Corporation

Problem definition: This data set contains 187 consecutive weeks of Intel microprocessor demand information for all five distribution centers in one of its five sales geographies. For every stock keeping unit (SKU) at every location, the weekly forecasted demand and actual customer orders are provided as well as the SKU’s average selling price category. These data are provided by week and by distribution center, producing 26,114 records in total. Academic/practical relevance: The 86 SKUs in the data set span five product generations. It provides years of product evolution across generations and price points. Methodology: As a data set paper, its purpose is to provide interesting and rich real-world data for researchers developing forecasting, inventory, pricing, and product assortment models. Results: The data set demonstrates the presence of significant forecast bias, heterogeneity of forecast errors between distribution centers, generational differences, product life cycles, and pricing dynamics. Managerial implications: This data set provides access to a rich pricing and sales setting from a major corporation that has not been made available before.

Major government customer and management earnings forecasts

This paper examines whether customer base composition in the US, that is, whether a firm’s major customers are government entities or publicly traded companies, affects the properties of its management earnings forecasts (MEFs). Using a sample of 1168 MEFs from 1998 to 2014, we find that firms whose major customers are government entities (i.e., government suppliers) issue more precise and more accurate MEFs than firms whose major customers are public companies (i.e., corporate suppliers). Moreover, when managers disclose negative information to the market, earnings forecasts issued by government suppliers have greater price impact than those issued by corporate suppliers. Collectively, our empirical results suggest that having major government customers has a positive impact on the quality of MEFs.

Optimism Propagation

We document that managerial biases spread across firms along supply chains. Supporting a causal interpretation, we show that beliefs trickle up the supply chain, not down, and that biases in supplier forecasts are only affected by customer forecasts issued before, not after, the supplier's forecast. We further find that bias propagation increases when suppliers have less confidence in their own views and when the perceived precision and importance of customer forecasts increase. Biases cause changes in the corporate policies of suppliers, suggesting that contagious beliefs in production networks contribute to fluctuations of business and financing cycles.

The value of sharing planning information in supply chains

Purpose – The development of information technology has made it possible for companies to get access to information about their customers ' future demand. This paper outlines various approaches to utilize this kind of visibility when managing inventories of end products on an operative level. The purpose is to explain the consequences, for capital tied up in inventory, of sharing four different types of planning information (point-of-sales data, customer forecasts, stock-on-hand data, planned orders) when using re-order point (R,Q) inventory control methods in a distribution network. Design/methodology/approach – A simulation study based on randomly generated demand data with a compound Poisson type of distribution is conducted. Findings – The results show that the value of information sharing in operative inventory control varies widely depending on the type of information shared, and depending on whether the demand is stationary or not. Significantly higher value is achieved if the most appropriate types of information sharing are used, while other types of information sharing rather contribute to decreased value. Sharing stock-on-hand information is valuable with stationary demand. Customer forecast and planned order information are valuable with non-stationary demand. The value of information sharing increases when having fewer customers, and when the order quantities are large. Sharing point-of-sales data is not valuable, regardless of the demand type. Research limitations/implications – The use of simulation methodology is a limitation, because the study has to be limited to a specific model design, and because it is not based on primary empirical data. The study is especially limited to dyadic relationships in supply chains, and to distribution networks with a rather limited number of customers. Practical implications – Guidance is given about what type of information should be appropriate to share when different types of demand patterns and distribution networks, and how order batch sizes and lead times affect the value of information sharing when using re-order point (R,Q) methods. Originality/value – Very limited research providing specific assessments of potential inventory control consequences when sharing planning information in various contexts has been found in the literature. The findings and conclusions also question some previous research on information sharing.

Optimizing Customer Forecasts for Forecast‐Commitment Contracts

We study a “Forecast‐Commitment” contract motivated by a manufacturer's desire to provide good service in the form of delivery commitments in exchange for reasonable forecasts and a purchase commitment from the customer. The customer provides a forecast for a future order and a guarantee to purchase a portion of it. In return, the supplier commits to satisfy some or all of the forecast. The supplier pays penalties for shortfalls of the commitment quantity from the forecast, and for shortfalls of the delivered quantity from the customer's final order (not exceeding the commitment quantity). These penalties allow differential service among customers. In Durango‐Cohen and Yano (2006), we analyzed the supplier's problem for a given customer forecast. In this paper, we analyze the customer's problem under symmetric information, both when the customer is honest and when he strategically orders more than his demand when doing so is advantageous. We show that the customer gains little from lying, so the supplier can use his control over the contract parameters to encourage honesty. When the customer is honest, the contract achieves (near‐)coordination of the supply chain in a great majority of instances, and thus provides both excellent performance and flexibility in structuring contracts.

Management Insights

Management Insights

The impact of forecast information quality on supply chain performance

PurposeThis paper aims to describe the extent of supplier access to customer forecast information and the perceived quality of such information and also to explain the impact of forecast information access and forecast information quality (FIQ) on supply chain performance.Design/methodology/approachFIQ is defined, and a measurement instrument is developed from theory. The analysis is based on a survey of the most important suppliers of 136 Swedish companies.FindingsFindings show that a large proportion of the suppliers receive customer forecasts, but that the FIQ is lower further upstream in the supply chain and, in some variables, lower for make‐to‐order suppliers. The greatest information quality deficiency of the forecast was that it was considered unreliable. The only significant difference in supply chain performance found between make‐to‐stock suppliers with and without access to forecast was related to the use of safety stock in finished goods inventory.Research limitations/implicationsThe study contains two types of conclusions: those developed from the conceptual discussion in the theoretical framework and those of the empirical study. In the theoretical framework, measurement instruments for FIQ and supply chain performance (corrective actions, preventive actions and customer service performance) were developed. The study identified several empirical relationships, but it was conducted on a sample with a lot of variation.Practical implicationsThe understanding of the performance impact of FIQ. FIQ shows quality deficiencies on all variables, which indicates room for improvement.Originality/valueResearch on supply chain information quality as well as dyadic research approaches are rare.

Revenue management and customer forecasts: A bridge too far for the UK visitor attractions sector?

Recent research pertaining to the UK and Scottish visitor attraction sector has suggested that more sophisticated approaches to issues of pricing, revenue and yield per visitor may be forthcoming. This paper suggests that such optimism related to attraction management is erroneous and far from the reality of current practice. Furthermore, the sector has seen significant growth at a time when demand has been at best static and has faced increasing competition from the wider leisure, retail and fastmoving consumer goods sectors. In many cases, UK attractions operate a reactive management model which fails to anticipate market movements and flounders in the face of competitors. Quantitative analysis is limited in this sector, making data-driven management techniques difficult to utilise. Accordingly, many new attraction developments have been predicated on little more than ‘forecast optimism’ and unrealistic business plans based on limited quantitative data. This paper will examine the recent growth of one sector of the UK visitor attraction market, ‘science and environment centres’, in order to demonstrate that limited management information systems, naive forecasting and poor business planning have provided the foundation for ill-conceived growth.

Supply chain management: a supplier perspective

A new way of researching into supply chain management is introduced by adopting a supplier perspective. Details are given of mixed integer linear planning and simulation models. The planning model takes into consideration firm and forecast orders (customer's forecast purchasing orders) and the behaviour of the supplier's suppliers and suppliers' subcontractors. The simulation model takes into account the dynamic behaviour of the supply chain and includes the planning production behaviour of a supplying company based on the planning model. The supplier manager can use the simulation model to determine what kind of parameters most affect company performances and then propose new management rules. Quantitative results that prove the benefits of integrating forecast orders for an aeronautic supplier have been provided.

Using simulation to estimate the uncertainty in Union Electric Company's residential customer forecast

An estimation of uncertainty for Union Electric Company's residential customer forecast is made using Monte Carlo simulation. The methodology is an improvement over the deterministic simulation methodology the company used when it filed an integrated resource plan with the Missouri Public Service Commission in 1993. Two different sensitivity analysis methodologies are applied to the company's 1994 residential customer forecast. One methodology involves testing each discreet combination of model inputs within the uncertainty range. The other methodology involves simulating the distribution of model inputs. Neither methodology produces adequate results by itself. A combination of the two methodologies is used to efficiently determine an acceptable conclusion about forecast sensitivity.