Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network

Current time-series forecasting problems use short-term weather attributes as exogenous inputs. However, in specific time-series forecasting solutions (e.g., demand prediction in the supply chain), seasonal climate predictions are crucial to improve its resilience. Representing mid to long-term seasonal climate forecasts is challenging as seasonal climate predictions are uncertain, and encoding spatio-temporal relationship of climate forecasts with demand is complex. We propose a novel modeling framework that efficiently encodes seasonal climate predictions to provide robust and reliable time-series forecasting for supply chain functions. The encoding framework enables effective learning of latent representations -- be it uncertain seasonal climate prediction or other time-series data (e.g., buyer patterns) -- via a modular neural network architecture. Our extensive experiments indicate that learning such representations to model seasonal climate forecast results in an error reduction of approximately 13\% to 17\% across multiple real-world data sets compared to existing demand forecasting methods.Comment: 15 page

Stochastic demand forecast and inventory management of a seasonal product a supply chain system

Estimation of seasonal demand prior to an active demand season is essential in supply chain management. The business cycle of the seasonal demand is divided into two stages: stage-1, the slow-demand period, and stage-2, the peak-demand period. The focus here is to determine an appropriate demand forecast for the peak-demand period. In the first set of forecasting model, a standard gamma and an inverse gamma prior distribution are used to forecast demand. The parameters of the prior model are estimated and updated based on current observation using Bayesian technique. The forecasts are derived for both complete and incomplete datasets. The second set of forecast is derived by ARIMA method using Box-Jenkins approaches. A Bayesian ARIMA is proposed to forecast demand from incomplete dataset. A partial dataset of a seasonal product, collected from the US census bureau, is used in the models. Missing values in the dataset often arise in various situations. The models are extended to forecast demand from an incomplete dataset by the assumption that the original dataset contains missing values. The forecast by a multiplicative exponential smoothing model is used to compare all the forecast. The performances are tested by several error measures such as relative errors, mean absolute deviation, and tracking signals. A newsvendor inventory model with emergency procurement options and a periodic review model are studied to determine the procurement quantity and inventory costs. The inventory cost of each demand forecast relative to the cost of actual demand is used as the basis to choose an appropriate forecast for the dataset. This study improves the quality of demand forecasts and determines the best forecast. The result reveals that forecasting models using Bayesian ARIMA model and Bayesian probability models perform better. The flexibility in the Bayesian approaches allows wider variability in the model parameters helps to improve demand forecasts. These models are particularly useful when past demand information is incomplete or limited to few periods. Furthermore, it was found that improvements in demand forecasting can provide better cost reductions than relying on inventory models

Indian Organised Apparel Retail Sector and DSS (Decision Support Systems)

Indian apparel retail sector poses interesting challenges to a manager as it is evolving and closely linked to fashions. Appealing mainly to youth, the sector has typical information requirements to manage its operations. DSS (Decision Support Systems) provide timely and accurate information & it can be viewed as an integrated entity providing management with the tools and information to assist their decision making. The study exploratory in nature, adopts a case study approach to understand practices of organized retailers in apparel sector regarding applications of various DSS tools. Conceptual overview of DSS is undertaken by reviewing the literature. The study describes practices and usage of DSS in operational decisions in apparel sector and managerial issues in design and implementation of DSS. A multi brand local chain and multi brand national chain of apparel was chosen for the study. Varied tools were found to be used by them. It was also found that for sales forecasting and visual merchandising decisions, prior experience rather than any DSS tool was used. The benefits realized were; “help as diagnostic tool”, “accuracy of records and in billing”, “smooth operations”. The implementation issues highlighted by the store managers were; more initial teething problems rather than resistance on the part of employees of the store, need for investment of time & money in training, due to rapid technological advancements, time to time updation in DSS tools is required . Majority of operational decisions like inventory management, CRM, campaign management were handled by ERP (Enterprise Resource Planning) or POS (Point of Sale). Prioritization as well as quantification of benefits was not attempted. The issues of coordination, integration with other systems in case of ERP usage, training were highlighted. Future outlook of DSS seems bright as apparel retailers are keen to invest in technology.

Colour & Trend Forecasting in a Sustainable World

This paper challenges the underlying philosophy of the colour and trend forecasting sector and the process currently used to market and promote trends that are contributing to excessive mass consumer consumption and the mass production of devaluing fashion product. It discusses how the trend forecasting sector needs to change to support a sustainable fashion system, beginning with colour. Such change is essential in light of the popularity of nostalgic tendencies, the availability of new shopping channels and in recognition of enhanced shopping experiences enjoyed by consumers while seeking fashion products that better resonate with their own tastes and lifestyle choices

Almost Periodically Correlated Time Series in Business Fluctuations Analysis

We propose a non-standard subsampling procedure to make formal statistical inference about the business cycle, one of the most important unobserved feature characterising fluctuations of economic growth. We show that some characteristics of business cycle can be modelled in a non-parametric way by discrete spectrum of the Almost Periodically Correlated (APC) time series. On the basis of estimated characteristics of this spectrum business cycle is extracted by filtering. As an illustration we characterise the man properties of business cycles in industrial production index for Polish economy

Hierarchical sales forecasting system for apparel companies and supply chains

The typical problems facing with apparel companies and supply chains are forecasting errors, because fashion markets are volatile and difficult to predict. For that reason, the ability to develop accurate sales forecasts is critical in the industry. There are several research studies related to forecasting apparel goods, but very often only for one level. However, apparel companies and supply chains deal with a number of levels at which the forecasts could exist and require consistent forecasts at all of them. The paper presents a hierarchical middle-term forecasting system designed for this purpose on the basis of a literature review. The system is built by the top-down forecasting approach and verified by means of a case study in a particular apparel company. The weaknesses of the system are identified during discussion of the results acquired. A generalised concept of the ANN forecasting model is designed for elimination these weaknesses.Web of Science21611

Annual Report on Cotton Economics Research 2001/02

Crop Production/Industries,