1,721,085 research outputs found
A model for selecting the appropriate level of aggregation in forecasting processes
No full textDemand forecasting is a major issue in several industrial sectors. A relevant choice for companies is the proper level of forecast aggregation. Forecasters need to properly identify what is the object of the forecasting process, in terms of time bucket (e.g., forecasts are produced on a daily level or on weekly one), set of items the demand refers to (e.g., single item or group of items), set of locations the demand refers to (e.g., single store or chain of stores). Managers can follow two basic approaches: on the one hand they can adopt a detailed forecasting approach, i.e., they can forecast demand for the item at the store by simply looking at the demand for the specific item/store; on the other hand they can adopt an aggregated forecasting approach. In this paper, we aim at figuring out what is the balance between the strengths and weaknesses of these two options, and to identify the contingent variables that might lead managers to adopt one approach rather than the other. In this paper we study the aggregation across locations by evaluating the components of forecasting error under the assumption of stationary demand. Finally, we suggest metrics that one can adopt to support the choice of the appropriate forecasting process, thus providing help to managers in defining the proper level of aggregation for a specific situatio
Best practices in demand forecasting: Tests of universalistic, contingency and configurational theories
No full textThe paper focuses on best practices in demand forecasting. Literature has addressed the best practice concept under three different perspectives. According to the universalistic perspective, some forecasting practices are universally effective regardless of the context in which companies operate. In the contingent perspective, the effectiveness of forecasting practices depends on the specific kind of context each company faces. A third perspective is the configurational one, which asserts that there are synergistic effects among best practices. In this work, we compare these different perspectives by designing and testing different sets of propositions that underline the aforementioned perspectives. Analysis is conducted by collecting data of more than 500 companies in different countries via the 4th edition of the Global Manufacturing Research Group (GMRG IV) questionnaire. The results demonstrate that each perspective has some empirical support. © 2012 Elsevier B.V. All rights reserved
A model for selecting the appropriate level of aggregation in forecasting processes
No full textDemand forecasting is a major issue in several industrial sectors. A relevant choice for companies is the proper level of forecast aggregation. Forecasters need to properly identify what is the object of the forecasting process, in terms of time bucket (e.g., forecasts are produced on a daily level or on weekly one), set of items the demand refers to (e.g., single item or group of items), set of locations the demand refers to (e.g., single store or chain of stores). Managers can follow two basic approaches: on the one hand they can adopt a detailed forecasting approach, i.e., they can forecast demand for the item at the store by simply looking at the demand for the specific item/store; on the other hand they can adopt an aggregated forecasting approach. In this paper, we aim at figuring out what is the balance between the strengths and weaknesses of these two options, and to identify the contingent variables that might lead managers to adopt one approach rather than the other. In this paper we study the aggregation across locations by evaluating the components of forecasting error under the assumption of stationary demand. Finally, we suggest metrics that one can adopt to support the choice of the appropriate forecasting process, thus providing help to managers in defining the proper level of aggregation for a specific situation. © 2007 Elsevier B.V. All rights reserved
The role of the forecasting process in improving forecast accuracy and operational performance
No full textSeveral operations decisions are based on proper forecast of future demand. For this reason, manufacturing companies consider forecasting a crucial process for effectively guiding several activities and research has devoted particular attention to this issue. This paper investigates the impact of how forecasting is conducted on forecast accuracy and operational performances (i.e. cost and delivery performances). Attention is here paid on three factors that characterize the forecasting process: whether structured techniques are adopted, whether information from different sources is collected to elaborate forecasts, and the extent to which forecasting is used to support decision-making processes. Analyses are conducted by means of data provided by the fourth edition of the Global Manufacturing Research Group survey. Data was collected from 343 companies belonging to several manufacturing industries from six different countries. Results show that companies adopting a structured forecasting process can improve their operational performances not simply because forecast accuracy increases. This paper highlights the importance of a proper forecasting-process design, that should be coherent with how users intend to exploit forecast results and with the aim that should be achieved, that is not necessarily improving forecast accuracy. © 2010 Elsevier B.V. All rights reserved
Forecasting practices: Empirical evidence and a framework for research
No full textDemand forecasting is a relevant issue both in research and practice. In the past several papers have investigated
forecasting practices. This research uses data from the GMRG survey to achieve three main objectives. First of all, we aim
to describe current practices in the machinery and textile sectors; in particular we investigate: (i) aims and usage of the
demand forecast for decision making; (ii) structure of the forecasting process; (iii) algorithms and tools adopted; (iv) both
upstream and downstream cooperative forecasting and (v) performance. On a second perspective we investigate contingent
variables such as structure (company size and sector), strategy (improvement priorities) and demand characteristics (e.g.
number of products) and their relationship with forecasting practices. Finally, the impact of the current forecasting process
on both forecasting accuracy and overall company’s performance is investigated.
In the end we highlight gaps between current research and actual companies’ practices; such gaps are discussed to
identify areas where support, new tools and concepts are needed to improve companies’ practices
The Impact of Forecasting on Performances: Is Accuracy the Only Matter?
No full textSeveral operations decisions are based on some kind of forecast of future demand. For this reason, manufacturing companies pay significant attention towards this process and research has devoted attention to this issue. This paper investigates the impact of how forecasting is conducted on accuracy and operational performances. Attention is here paid on three elements that characterize the forecasting process: whether structured techniques are adopted, whether detailed information is used, and the extent to which forecasting is used in decision making processes. Analyses are conducted by means of data provided by the fourth edition of the Global Manufacturing Research Group questionnaire. Data has been collect from 343 companies belonging to several manufacturing industries from 6 different countries. Empirical analysis shows that the relationship between how forecasting is conducted and operational performances is not fully explained only by taking into consideration forecast accuracy. Results show that companies adopting a structured forecasting process have positive impacts on operational performances (here manufacturing cost and delivery are considered) not only through improved accuracy. The paper highlights the importance of proper design of the forecasting process in manufacturing environments since it can help to better understand the forecasting problem (i.e. demand variability), may reduce bias and coordinates all forecast users
Managing inventories in global sourcing contexts: A contingency perspective
No full textOne of the key problems of global supply chains is how to keep inventories low. Even if there is an evidence that supply chain management tools can help in this direction, an under-investigated point is how companies in different contexts experience the effects of global sourcing and the outcomes on their material inventory level. Based on a model proposed by (Golini and Kalchschmidt, 2011, Int. J. Prod. Econ. 133 (1), 86-94.) the aim of this paper is to verify whether different companies - in terms of contingency variables - experience different impacts of globalisation and supply chain management on the material inventory level. In this work, several contingency variables were selected from the literature i.e., company size, product complexity, type of production, type of purchases, number of suppliers and number of suppliers per item. The results show that when considering groups of companies characterised by different contingent variables, the relationship between globalisation, supply chain investments and material inventory levels is valid only for some groups, whereas it loses its significance for others
Moderating the impact of global sourcing on inventories through supply chain management
No full textIn recent years, companies have paid growing attention to supply chain management at a global level. With regard to the upstream part of the supply chain, the need for better suppliers, the research into specific competences and concerns related to international competition have forced companies to improve their ability to cope with suppliers located in different countries around the world. The literature suggests that the geographical distance of suppliers should create higher inventory levels primarily because of longer and more uncertain lead times. However, as this paper aims to demonstrate, companies can limit this effect by means of specific investments in the supply chain and in their relationships with suppliers. The empirical analysis is based on data from the last edition of the International Manufacturing Strategy Survey (IMSS). The results show that companies performing global sourcing have invested in supply chain management (SCM) and that this has been helpful in keeping their inventories under control. © 2010 Elsevier B.V. All rights reserved
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