企业需求预测准确性存在的问题及其应对策略(二)

鉴于需求预测在企业经营活动中具有重要地位,且会受到受各种因素影响,本文在对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析的基础上,指出了通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和未来市场情况的偏差,从而持续提高需求预测的准确性,促进企业生产、销售的良性运行。     

西北民族大学后勤集团燃煤采购问题

如何有效的开展需求预测,对单位和企业来说都具有非常重要的意义.     

提高定制产品物料需求预测的准确性

提高定制产品物料需求预测准确性是提高企业竞争能力的必由之路。通过某公司提高定制产品物料需求预测准确性的具体项目实践，介绍了关于提高物料需求预测准确性的具体措施。通过建立一种能为各部门所共同接受的产品语言来加强各部门的信息交流，并减少从客户的实际需求到物料预测之间的中间环节来提高整个物料预测的准确性，并以实例验证其有效性。     

改进BASS模型应用于短生命周期产品需求预测

总结了目前国内外文献对短生命周期产品需求预测的研究,分析了短生命周期产品需求相关的特点.介绍了BASS模型及其发展,通过可行性分析,将BASS模型应用于短生命周期产品预测.利用类似产品补充预测所需信息,并对模型进行了季节性修正.算例预测结果表明,改进后的BASS模型应用于短生命周期产品的需求预测在MAD、RMSE、MAPE等各项误差指标方面都优于其他方法.     

应急物资需求量预测方法探讨

应急物资需求预测是制订应急保障计划、合理筹划应急物资资源的重要依据。由于应急物资需求影响因素多,因此,要根据不同类型物资、不同应急保障环境和不同任务特点等情况运用因素分析或定性定量分析方法,合理确定应急物资需求量。     

汽车销售分析与预测系统研究

销售预测是企业供应链管理的关键环节,通过预测企业可以制定科学的采购、生产、库存、销售等计划,所以销售预测对企业经营决策具有重大的研究意义。在数据仓库的基础上,采用灰色系统理论进行预测,并验证灰色GM(1,1)预测模型的可行性。     

浅谈城市道路交通管理规划

城市道路交通管理规划是城市可持续性发展的前提和基础，本文分析介绍了我国城市道路交通管理规划的现状，阐述了交通管理规划的目的、内容、层次及过程，论述了交通需求预测的分析方法及其相应交通需求模型在交通管理规划中的应用。     

医疗资源配置的动态生成系数灰色预测模型北大核心CSSCI

针对人口密集型都市的医疗资源配置缺乏准确性预测作为实时决策依据的难题,利用灰色理论的小样本建模优势进行预测方法拓展优化,建立等维递补灰色预测方法以提高灰区间白色度和淡化灰平面灰度;挖掘灰色生成系数与外部影响因素间的内在映射关联,提出了动态生成系数优化的灰色理论医疗需求预测方法;动态拟合人口总量变化与灰色生成系数以实现预测模型实时重构,解决了传统灰色预测方法的纯样本序列建模局限,显著提高了预测算法的准确性,输出的医疗需求趋势可有力支撑医疗资源配置决策。     

中国彩色感光材料市场需求分析

通过对中国彩色感光材料市场进行需求预测和市场分析，为中国感光企业认识市场、选定目标、进行决策，提供了科学依据，并借此提高中国感光企业的市场竞争能力。     

建筑施工企业的质量管理与控制

中国建筑业市场竞争日趋激烈,竞争的核心是工程质量问题。介绍了质量管理、质量控制的起源和国内外发展现状,对高层施工管理的特点和重点进行了细致的分析,从项目管理、工程施工等方面论述了质量控制的内容和方法,阐述了整个建筑的施工质量控制过程。本文就建筑施工企业质量管理与控制问题做了一些分析。     

Adaptive exponential smoothing versus conventional approaches for lumpy demand forecasting: case of production planning for a manufacturing line

Production planning in a lumpy demand environment can be tenuous, with potentially costly forecasting errors. This paper addresses the issue of selecting the smoothing factor used in lumpy demand forecasting models. We propose a simple adaptive smoothing approach to replace the conventional industrial practice of choosing a smoothing factor largely based on the analyst or engineer's experience and subjective judgment. The Kalman filter approach developed in this study processes measurements to estimate the state of a linear system and utilises knowledge from states of measurements and system dynamics. Performances of an array of forecasting models that have been shown to work well in lumpy demand environments are compared with respect to the proposed adaptive smoothing factor and the conventional smoothing constant across a spectrum of lumpy demand scenarios. All models using the adaptive smoothing factor based on Kalman filter weighting function generate smaller errors than their conventional counterparts, especially under high lumpiness demand environments. Our proposed approach is particularly useful when production management is concerned about simplicity and transferability of knowledge due to constant personnel turnaround and low retention rate of expertise.     

Combined particle swarm optimization and heuristic fuzzy inference systems for a smart home one-step-ahead load forecasting

This article presents a hybrid approach that combines particle swarm optimization (PSO) and heuristic fuzzy inference system (HFIS) for smart home one-step-ahead load forecasting. Smart home load forecasting is an important issue in the development of smart grids. Generally, the electricity consumption of a household is inherently nonlinear and dynamic and heavily dependent on the habitual nature of power demand, activities of daily living and on holidays or weekends, so it is often difficult to construct an adequate forecasting model for this type of load. To address this problem, a hybrid model, consisting of two phases, is proposed in this article. In the first phase, the popular PSO algorithm is used to determine the locations of fuzzy membership functions. Then, the proposed HFIS technique is used to develop the one-step-ahead load forecasting model in the second phase. Because of the robust nature of the proposed HFIS technique, which does not need to retrain or re-estimate model parameters, it is very suitable for smart home load forecasting. The proposed method was verified using two different households’ load data. Simulation results indicate that the proposed method produces better forecasting accuracy than existing methods.     

最优组合预测方法在家用汽车需求预测中的应用

为了提高预测的准确性,引入了组合预测模型,将几个单一预测模型有机地结合起来,综合各个预测模型的优点,对未来几年内家用轿车需求进行预测.通过使组合预测误差平方和最小,确定各个单一预测方法的权重系数,得出更为准确的预测结果.计算结果表明该方法具有较好的实用性.     

Mitigating forecast errors by lot-sizing rules in ERP-controlled manufacturing systems

As enterprise resource planning (ERP) becomes the dominant management software in manufacturing and distribution systems in various industries, some problems associated with its origin, material requirements planning (MRP), still need to be resolved. We examine the effect of forecasting errors, one of the common operational problems in any business operation, in the context of an ERP-controlled manufacturing system. We consider a mitigating remedy, the use of a lot-sizing rule, to cope with the consequences of forecasting inaccuracy without resorting to costly inventory-oriented buffers. An ERP-controlled manufacturing system is simulated to see how these lot-sizing rules mitigate the forecast errors and subsequently generate acceptable system performance. The simulation results should help ease ERP users’ fear of committing another fatal error in demand forecasts, instead encouraging them to consider proper lot-sizing rules to cope with forecast errors.     

Demand forecasting and smoothing capacity planning for products with high random demand volatility

For those products that are heavily competitive in the marketplace, demand volatility and unpredictability have been growing. This has resulted in a sizeable deviation in demand forecasts when using the traditional forecasting methods. Accordingly, this study aims to develop a real option approach-based forecasting model for predicting demand during the upcoming planning horizon for products with high random volatility on demand. The real option approach can effectively deal with the long-term trends and random variation involved in a given demand stochastic diffusion process. Additionally, this study proposes taking Monte Carlo simulation as a numerical method to solve the demand-forecasting model. Monte Carlo simulation not only can accurately approximate almost any type of stochastic processes, but also can competently handle the path-dependant relationship existing between successive demands. Subsequently, these demand forecasts are used to determine the provisioned smoothing capacity during the upcoming planning horizon. To this end, this study also proposes several effective and practical smoothing capacity-planning approaches in accordance with the specified production strategy. Based on a numerical example, the integrated planning approach can obtain a plausible result.     

An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing

As high-speed computing is crucial to empower intelligent manufacturing for Industry 4.0, non-volatile memory (NVM) is critical semiconductor component of the cloud and data centre for the infrastructures. The NVM manufacturing is capital intensive, in which capacity utilisation significantly affects the capital effectiveness and profitability of semiconductor companies. Since capacity migration and expansion involve long lead times, demand forecasting plays a critical role for smart production of NVM manufacturers for revenue management. However, the shortening product life cycles of integrated circuits (IC), the fluctuations of semiconductor supply chains, and uncertainty involved in demand forecasting make the present problem increasingly difficult in the consumer electronics era. Focusing on the realistic needs of NVM demand forecasting, this study aims to develop a decision framework that integrates an improved technology diffusion model and a proposed adjustment mechanism to incorporate domain insights. An empirical study was conducted in a leading semiconductor company for validation. A comparison of alternative approaches is also provided. The results have shown the practical viability of the proposed approach.     

Operational tactics and tenets of a new manufacturing paradigm ‘instant customerisation’

An increasing trend towards a new manufacturing paradigm of ‘instant customerisation’ is occurring due to customers demanding their orders for customised products or services be fulfilled under zero customer lead time and at a price near the price of mass production. By means of the manufacturing paradigm innovation model (MPIM), the operational tactics, operational tenets, and an operational framework of instant customerisation are elicited and described. An important new tactic within all the operational tactics is finalise-to-individual demand forecasting, meaning end products are finalised according to the forecasting of individual customer's needs. The knowledge and information, which is the primary input of individual demand forecasting, is gleaned and understood by total customer participation and value fusion. Total customer participation and value fusion are developed through customer participation and customer relationship management, which have critical distinctions. The operational framework, which is termed manufacturing paradigm tree of instant customerisation, can characterise the operational tenets, the relationships between different operational tactics and the objectives, and the relationships among different operational tactics. With the proposed framework, instant customerisation can be understood more effectively and holistically.     

A combined model predictive control and time series forecasting framework for production-inventory systems

Model Predictive Control (MPC) has been previously applied to supply chain problems with promising results; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing insight in the future. In this paper this possibility is explored, by proposing a complete management framework for production-inventory systems that is based on MPC and on a neural network time series forecasting model. The proposed framework is tested on industrial data in order to assess the efficiency of the method and the impact of forecast accuracy on the overall control performance. To this end, the proposed method is compared with several alternative forecasting approaches that are implemented on the same industrial dataset. The results show that the proposed scheme can improve significantly the performance of the production-inventory system, due to the fact that more accurate predictions are provided to the formulation of the MPC optimization problem that is solved in real time.     

基于Petri网的供应链协同需求预测流程模型

提出了一个集成化的供应链协同需求预测模型,该模型包括数据、组织、环境与例外、决策与方法、运作与计划以及协作与调整等6个部分,其特点是将不同成员的意见根据不同的重要性融合到预测中,且突出了多源信息的应用.通过基于有色Petri网对本模型的