Design of the optimal feeding policy in an assembly system

This paper describes an innovative and integrated approach to component management optimization within a production/assembly system. In a mixed-models assembly process the handling of parts and components for each work station represents a substantial variable that can greatly affect job duration and efficiency. This paper is strictly related to Assembly to Order/Manufacturing to Order (ATO and MTO) systems, where lead time has to be very short and flexibility is at its maximum level. In Assembly to Order (ATO) or Make to Order (MTO) systems, the production is increasingly getting more customized in response to the demand, thanks to the progresses reached in both manufacturing and information technologies. It is becoming increasingly possible to assemble or make products specifically in response to the requests of either end customers or retailers. As a consequence of such customization, the design of the whole system must take into direct account several elements: parts warehouses location, feeding policies and feeding systems. In some cases the collection of parts and components required picking activities, in other the movement of entire units load.In several instances experts have analyzed the problems about material centralization/decentralization, storage policies and assembly feeding problem in different and independent ways, while the problem needs an integrated approach. While many researches regarding components allocation problems in ATO and MTO systems, did not consider feeding policies, material picking, packing activities and vehicles optimization, this paper cover focuses on filling such gap using an integrated framework that considers both aspects of the problem: the centralization/decentralization of components in order to minimize the total storage costs and the right feeding policies.Feeding problems in assembly lines are some of the most important aspects to consider during the analysis and design of an assembly system, to allow the maximization of efficiency and flexibility. To reach such goals, a multi-factorial analysis has been carried out during this experiment and will validate the introduced framework. An industrial application of the introduced framework is illustrated to explain its real significant production implication.     

Product and Process Modularity's Effects on Manufacturing Agility and Firm Growth Performance

Modularity in product design has been hailed as a way to speed new product development (NPD), to reduce NPD cost, and to enhance customization possibilities for consumers. Modularity in process design may speed new product manufacturing setup times, reduce costs, and enhance the profitability of the lower volumes that customization often entails. However, empirical evidence is scarce that either product or process modularity—individually, jointly, or sequentially—actually produce these or other proposed benefits (e.g., performance growth). This study builds on general modular systems theory (GMST) by examining the theoretical relationship between product and process modularity and the effects of each on firm growth performance. Using structural equation modeling, partial versus complete mediation by manufacturing agility is also scrutinized. In one pair of models, product modularity and process modularity are separate direct antecedents to manufacturing agility, which is modeled to affect firm growth performance; in a second pair of models, product and process modularity are related antecedents to manufacturing agility, with product modularity preceding process modularity. Results from the best‐fitting model show that product modularity directly and positively affects process modularity, manufacturing agility, and firm growth performance. Process modularity was unrelated to manufacturing agility, and neither process modularity nor manufacturing agility predicted growth performance. Consistent with GMST, the study provides empirical evidence of the power of one element of a modular system to orchestrate a fit between a firm's product and manufacturing strategies and to directly drive system performance. Thus, modularity in product design is revealed as the key to understanding GMST effects concerning how changes in one system generate changes in other systems.     

Strategic safety stock placement in supply chain design with due-date based demand

We present a strategic safety stock placement model in supply chain design for assembly-type product with due-date based demand, where demand data are based on dates when company has to ship to customers rather than order receiving dates. We formulate multi-echelon stock placement by guaranteed-service model with demand propagation equations through backward explosion, where demand can be either stationary or nonstationary. The stock placement model is incorporated into network design problem and its optimization procedure is provided. We show effectiveness of the optimization procedure and other significant features of the model through numerical examples of a machinery product supply chain.     

Integrated safety stock optimization for multiple sourced stockpoints facing variable demand and lead time

The safety stock placement problem of a multi-stage supply chain comprising multiple sourced stockpoints is addressed in this paper. Each stockpoint faces variability in its downstream demand and suppliers' lead time. The maximum among these suppliers' lead time is determined by employing concepts of order statistics. It is required to find the fill rate and safety stocks at each stockpoint that leads to satisfying the end customer service level at minimum safety stock placement cost. Hence, the fill rates and the safety amounts are decided from a global supply chain perspective. Two models are proposed; a decentralized safety stock placement model and a centralized consolidation model. The decentralized model finds the safety amounts at each stockpoint required to face its underlying lead time demand variability. The consolidation model finds the consolidated safety amounts that will be kept in the relevant consolidation center at each stage. A Benders decomposition technique is developed to handle the nonlinearity and binary restrictions involved in the safety stock consolidation model. Strategies proposed by the consolidation model achieve 45.2-62% reduction in safety amounts that results in a cost savings ranging between 22.2-44.2% as compared to the strategies proposed by the decentralized model.     

A simple and accurate approximation for the order fill rates in lost-sales Assemble-to-Order systems

In this paper we consider an Assemble-to-Order system with multiple end-products. Demands for an end-product follow a Poisson process and each end-product requires a fixed set of components. We are interested in the order fill rates, i.e., the percentage of demands for which all requested components are available from stock. Requested components that are not in stock are supplied via an emergency shipment and the demand for these components is lost for the stockpoint under consideration. The component lead times are deterministic and may differ per component. The inventory of each component is controlled via a base stock policy. We show that the system decomposes into subsystems which can be analyzed independently. Each subsystem can be approximated by a subsystem with exponentially distributed lead times, for which an exact evaluation exists. For big subsystems, however, this method requires considerable computational effort. Therefore, we formulate a simple and accurate approximation for the order fill rates. Our approximation uses two estimates of which one generally gives an underestimation of the order fill rate and the other one an overestimation. A weighing factor is used to combine these two estimates into an approximate value. The approximation is shown to be accurate and requires little computational effort.     

Determining safety stocks for production planning in uncertain manufacturing

When computing production plans and available-to-promise statements, the randomness of flow times and yield rates are the two major sources of uncertainty. Considering both of these uncertainties, this study employs a simple formula for computing the standard deviation for cumulative good product output of a particular product type at a particular time. Using this standard deviation, the necessary safety stock for that product type at that time can be obtained. This study was motivated by semiconductor wafer manufacturing, which requires many process steps performed by highly unreliable equipment. Such an environment causes the means and the variances in flow times to be generally larger than those for other industries. It also makes yield rates uncertain. Thus, when making order quotations and calculating production plans, safety stocks must be used to guard against the uncertainties of the manufacturing processes to ensure on-time-delivery ratios and to provide better customer service.     

Effects of correlation on intermittent demand forecasting and stock control

This study investigates the effects of three different types of correlation on forecasting and stock control of intermittent demand items. Applying appropriate forecasting and stock control methods to theoretically generated compound Poisson demand data we show that correlation in intermittent demand does play a role in forecast quality and stock control performance. Negative autocorrelation levels lead to higher service levels than positive values, while cost does not significantly change. Our results also show that high intermittency levels intensify these changes in service level. We also show that cross-correlation produces results in the opposite direction of autocorrelation in size or intervals; that is, positive (negative) cross-correlation leads to higher (lower) service levels.     

The role of the forecasting process in improving forecast accuracy and operational performance

Several 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.     

Considering stochastic lead times in a manufacturing/remanufacturing system with deterministic demands and returns

In this paper we study a manufacturing/remanufacturing system with stochastic lead times and a constant demand. We base our approach on previous research in which we have developed models to describe an inventory system with stochastic lead times. In this paper, we first adopt this method to manufacturing/remanufacturing situations, where there are essentially two supply sources for replenishing serviceable inventory. We then provide a solution procedure when a cycle ordering policy is used. Secondly, we investigate the possibility to use a dual sourcing ordering policy in which each order is split between a manufacturing and a remanufacturing process. Finally, we compare the two ordering policies and illustrate how the lead-time patterns influence the economic consequences.     

Inventory control with a modified Croston procedure and Erlang distribution

This paper considers an inventory control system, primarily for a finished goods inventory. The purpose is to create a procedure that can handle both fast-moving items with regular demand and slow-moving items. The suggested procedure should be easy to implement in a modern computerized ERP-system. Essentially, the system is a periodic review system built around a Croston forecasting procedure. An Erlang distribution is fitted to the observed data using the mean and variance of the forecasted demand rate. According to probabilities for stock shortages, derived from the probability distribution, the system decides if it is time to place a new order or not. The Croston forecasting method is theoretically more accurate than ordinary exponential smoothing for slow-moving items. However, it is not evident that a Croston forecasting procedure (with assumed Erlang distribution) outperforms ordinary exponential smoothing (with assumed normal distribution) applied in a “practical” inventory control system with varying demand, automatically generated replenishment, etc. Our simulation study shows that the system in focus will present fewer shortages at lower inventory levels than a system based on exponential smoothing and the normal distribution.     

Identification of Lead Users for Consumer Products via Virtual Stock Markets*

Newly launched products in the consumer goods and services markets show high failure rates. To reduce the failure rates, companies can integrate innovative and knowledgeable customers, the so‐called lead users, into the new product development process. However, the detection of such lead users is difficult, especially in consumer product markets with very large customer bases. A new and potentially valuable approach toward the identification of lead users involves the use of virtual stock markets, which have been proposed and applied for political and business forecasting but not for the identification of experts such as lead users. The basic concept of virtual stock markets is bringing a group of participants together via the Internet and allowing them to trade shares of virtual stocks. These stocks represent a bet on the outcome of future market situations, and their value depends on the realization of these market situations. In this process, a virtual stock market elicits and aggregates the assessments of its participants concerning future market developments. Virtual stock markets might also serve as a feasible instrument to filter out lead users, primarily for the following two reasons. First, a self‐selection effect might occur because sophisticated consumers with a higher involvement in the product of interest decide to participate in virtual stock markets. Second, a performance effect is likely to arise because well‐performing participants in virtual stock markets show a better understanding of the market than their (already self‐selected) fellow participants. So far, only limited information exists about these two effects and their relation to lead user characteristics. The goal of this paper is to analyze the feasibility of virtual stock markets for the identification of lead users. The results of this empirical study show that virtual stock markets can be an effective instrument to identify lead users in consumer products markets. Furthermore, the results show that not all lead users perform well in virtual stock markets. Hence, virtual stock markets allow identifying lead users with superior abilities to forecast market success.     

ERP与MRO系统集成过程建模与应用研究

为了实现产品生产阶段到服务阶段业务与数据的有效集成,从业务层和数据层对制造服务系统中的大修维护维修(MRO)与企业资源计划(ERP)的集成过程进行了建模与分析,对ERP与MRO系统之间的业务过程和信息交互过程进行了分析,建立了二者的业务集成框架模型,分析了ERP与MRO系统的数据结构转换过程,提出了二者数据集成模型。应用Web Services完成了ERP与MRO的系统集成开发,包括服务BOM集成、备品备件管理集成等。研究结果实现了制造和服务阶段关键产品数据信息的集成与反馈,打通了从制造到服务阶段的数据传递与集成,对进一步研究复杂产品生命周期管理的信息集成具有一定的借鉴意义。     

Comparison of a new bootstrapping method with parametric approaches for safety stock determination in service parts inventory systems

In this paper, we address the problem of forecasting and managing the inventory of service parts where the demand patterns are highly intermittent. Currently, there are two classes of methods for determining the safety stock for the intermittent item: the parametric and bootstrapping approaches. Viswanathan and Zhou (2008) developed an improved bootstrapping based method and showed through computational experiments that this is superior to the method by Willemain et al. (2004). In this paper, we compare this new bootstrapping method with the parametric methods of Babai and Syntetos (2007). Our computational results show that the bootstrapping method performs better with randomly generated data sets, where there is a large amount of (simulated) historical data to generate the distribution. On the other hand, with real industry data sets, the parametric method seems to perform better than the bootstrapping method.     

The impact of aggregation level on forecasting performance

Most operations decisions are based on some kind of forecast of future demand. Thus, forecasting is definitely a very traditional area in the operations and inventory management literature. While literature concerning forecast explores the adoption of various qualitative and quantitative methods, this paper tries to design new solutions to improve forecasting accuracy by focusing on the forecasting process that uses such algorithms. In particular, when forecasting demand one should always make clear exactly what he/she is trying to forecast, in terms of the time bucket (i.e., the period of time over which demand is aggregated), the forecasting horizon, the set of items the demand refers to (e.g., forecasting demand for a single item can be much harder than forecasting demand for a group of items), the set of locations the demand refers to (e.g., demand at the single store level is much less predictable than the demand for a whole chain of stores). Traditionally, these features of the final output of forecasting also influence the forecasting process. Indeed, when one wants to forecast demand at single store single item single day level it seems natural to analyse demand and causal factors at the same level of aggregation. On the contrary, in this paper we aim at showing that, first of all often aggregating and/or disaggregating data in the forecasting process can lead to substantial improvements; second, the choice of the appropriate level of aggregation depends on the underlying demand generation process.In addition, most forecasting algorithms tend to focus on a single demand variable. On the contrary, we can analyse analogous time series to improve the effectiveness of the forecasting process. Clustering techniques can be used to identify such homologous time series. Such clusters of homologous time series can provide, on the one hand, the sample size required to gain good statistical confidence and, on the other hand, relatively homogeneous data.In the paper, we use sales data from a food retailer at a very detailed level to test our hypotheses. This claims for relevance for both practitioners and researchers.     

Managing demand uncertainty through order overplanning

Most techniques for managing demand uncertainty require a certain degree of stability in the environment, since they are completely or partially based on the observation of historical data. When applied to a context characterized by irregular and sporadic demand these techniques show poor performances. In fact, in such a case uncertainty management calls for the gathering of information that directly anticipates future requirements. Although contexts with irregular and sporadic demand have received only minor attention in the past, they are currently gaining ever more importance and extending their occurrence. This paper illustrates and discusses a method, called order overplanning, specifically designed to cope with uncertainty in these environments. It consists of an articulate and coherent set of forecasting procedures, planning principles and slack control techniques. From a Master Production Scheduling (MPS) perspective, order overplanning is similar to hedging and option overplanning: gross requirements are larger than expected demand. The major difference is that order overplanning uses two distinct units in the MPS and forecasting procedures: while the MPS unit is an end item or a module, the forecasting unit is a customer order. This makes order overplanning able to exploit early information generated by each customer during its purchasing process, information that otherwise would be lost. This marked advantage comes to the detriment of an increased effort of integration between Sales and Manufacturing, especially for controlling the slack created to handle uncertainty.The paper first infers the principles and procedures of order overplanning by analysing the case study of an Italian telecommunications manufacturer. Then, it discusses the main advantages and disadvantages of this method, in order to identify the main factors affecting its performances and to determine the planning environments where it fits coherently.     

Approximation approach for the performance analysis of production lines under a kanban discipline

In a JIT production environment, pull-type control systems are usually implemented to reduce the lead time and to synchronize the production rate with the demand rate. The design of an effective production control system requires easy and reliable approaches for evaluating the effect of the major system parameters on its performance.In this paper we model the kanban control system of a manufacturing line as a Markov process. In addition, we develop an approximation approach to the model that permits reliable evaluation of manufacturing system performance in terms of throughput time and work-in-process. The validation of the approach is implemented by comparing the exact results (based on discrete event simulation) and the approximated results.A good approximation is observed for a large range of conditions.     

An inventory control model with consideration of remanufacturing and product life cycle

This paper investigates inventory control policies in a manufacturing/remanufacturing system during the product life cycle, which consists of four phases: introduction, growth, maturity, and decline. Both demand rate and return rate of products are random variables with normal distribution; the mean of the distribution varies according to the time in the product life cycle. Closed-form formulas of optimal production lot size, reorder point, and safety stock in each phase of the product life cycle are derived. A numerical example is presented with sensitivity analysis. The result shows that different inventory control policies should be adopted in different phases of the product life cycle. It is also found that the optimal production lot size and reorder point are not sensitive to the phase length and the demand changing rate.     

基于DAF的煤矿风险预警模型研究及应用

为降低煤矿事故率,改变传统煤矿风险预警模式,使用DAF(德尔菲法、层次分析法、模糊综合评价法)对煤矿安全生产状态进行评价。通过德尔菲法调查分析影响煤矿安全生产的指标因素,选取其中关键性的34项指标来构建预警指标体系,利用层次分析法计算各指标权重,结合模糊数学理论,运用模糊综合评价法进行综合评价,判断出煤矿的安全状态,建立煤矿风险预警模型,并结合实际案例验证其可行性。结果表明,通过模型的数值计算,可以直观判断煤矿的安全生产状态,达到提前预测预警,减少煤矿生产事故发生的目的。     

Effectiveness of sales forecasting methods

Effective sales forecasting has become a prerequisite for successful management. Unfortunately, recognition of the need for more effective forecasts has not produced better results. It appears that forecast error is increasing rather than decreasing. This paper reports the findings of a study of forecasting systems in manufacturing firms. The importance of forecasting to firms, users and preparers of forecasts, forecasting techniques employed, use of macroeconomic data, treatment of inflation, forecasting error, and management of forecasting systems are covered in the paper. Conclusions and recommendations for improvement of forecasting systems are developed from the findings of the study.     

Direct intervention or indirect support? The effects of cooperative control measures on farmers’ implementation of quality and safety standards

In recent years, labor-intensive agricultural products from developing countries have exhibited inferior performance in international trade due to quality and safety incidents, among which pesticide residue is a major issue. Aiming to improve food quality and safety in the context of cooperatives, we introduced three categories of control measures: outcome control, process control and social control. Based on the Pre-Harvest Interval Standard (PHIS), we selected three indices, farmers’ implementation rate of PHIS, absolute distance to PHIS and relative distance to PHIS, to evaluate appropriateness of pesticides use, reduction of pesticide residue and safety improvements of agricultural products. By using random sampling survey data, we empirically analyzed the marginal effects of control measures and their combinations on food quality and safety standards. The empirical results show that implementing process control, namely, unified production standards or supply of unified agricultural inputs, can comprehensively improve farmers’ implementation rate of PHIS, absolute distance and relative distance to PHIS respectively by 34.9%, 3.2 days and 46.0% on average. While the effects of outcome control (safety inspection) and social control (bonus-penalty incentive or training) are restricted to other measures. Therefore, we suggest cooperatives should take farmers’ features, implementation conditions and the effects of control measures into consideration in order to make a sustainable management plan for improving food quality and safety and enhancing competitiveness in international markets.