Replanning timing in hierarchical production planning

Tsubone, Hitoshi; Furuta, Hirohisa

doi:10.1016/0925-5273(95)00092-5

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Bobko and Whybark [4] deem CV a robust measure of demand volatility and it is used in e.g. Tsubone and Furuta [17] and Pujawan and Kingsman [18] to characterize demand behavior. To lower variation and thereby reduce uncertainty of the demand the general approach is to aggregate individual customer orders into demand rates for both individual products and product families [9,19,20] over a sufficient time horizon or postponing allocations [21].…”

Section: Background and Motivationmentioning

confidence: 99%

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

Eriksen¹,

Nielsen²

2016

Management and Production Engineering Review

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Accepted: 1 September 2016In this paper an investigation into the demand, faced by a company in the form of customer orders, is performed both from an explorative numerical and analytical perspective. The aim of the research is to establish the behavior of customer orders in first-come-first-serve (FCFS) systems and the impact of order quantity variation on the planning environment. A discussion of assumptions regarding demand from various planning and control perspectives underlines that most planning methods are based on the assumption that demand in the form of customer orders are independently identically distributed and stem from symmetrical distributions. To investigate and illustrate the need to aggregate demand to live up to these assumptions, a simple methodological framework to investigate the validity of the assumptions and for analyzing the behavior of orders is developed. The paper also presents an analytical approach to identify the aggregation horizon needed to achieve a stable demand. Furthermore, a case study application of the presented framework is presented and concluded on. KeywordsDemand distribution, Poisson processes, order quantities, Coefficient of Variation, Planning&Control. IntroductionTo achieve good customer service, companies aim at delivering products on time at competitive prices. One aspect in achieving competitive performances is companies' abilities to match customer expectations. In many situations resources are dedicated prior to knowing actual customer demand. This makes forecasting customers' ordering behavior of critical importance. Most research on this topic has been dedicated to either forecasting demand [1] or how to react in a quick robust manner to customer demand through e.g. better planning [2]. The research presented in this paper focuses on another aspect that has received limit research attention, namely the fact that demand is composed of individual customer orders. The fact that the demand a company faces is actually composed of a large number of presumed i.i.d. customer orders that can be aggregated is challenged in this research through an investigation of actual behavior and modelling of what to do when customer orders are in fact not i.i.d. This paper expands on the work reported in [3] through adding a framework, extending the complexity of the model, elaborating on the current state and adding more numerical examples. This paper focuses on customer order quantity distributions, analyses their behavior and investigates the consequence of transforming these into demand rates that are used for planning purposes. It also presents an analytical model to calculate the aggregation horizon necessary to ensure a stable distribution of demand for operational planning purposes. In this research main aim is to establish the specific need for aggregation in 39 Unauthenticated Download Date | 5/9/18 8:28 PM Management and Production Engineering Reviewthe form of the number of customer orders needed to aggregate to achieve a system-wide desired stability. In...

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Section: Background and Motivationmentioning

confidence: 99%

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

Eriksen¹,

Nielsen²

2016

Management and Production Engineering Review

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“…To describe the volatility of the order quantities the coefficient of variance (CV) is used (e.g. discussed in Nielsen and Eriksen [13] and used in Tsubone and Furuta [18]). The size Q of an order is assumed to have mean µ and standard deviation , i.e.…”

Section: Analytical Modeling Of the Aggregation Horizonmentioning

confidence: 99%

Order Quantity Distributions in Make-to-Order Manufacturing: At What Level of Aggregation Do They Respect Standard Assumptions?

Eriksen

Nielsen

2012

Advances in Production Management Systems. Value Networks: Innovation, Technologies, and Management

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Abstract. This paper presents both an analytical and a numerical investigation into the order quantities received by a company in the form of customer orders. A discussion of assumptions regarding the behavior of demand in the form of customer orders from various perspectives within manufacturing planning and control with a special emphasis on the make-to-order environment is presented. A methodological framework for analyzing the behavior of orders and investigate the validity of the assumptions is given. Furthermore, an analytical approach to identify the horizon needed for aggregating orders to achieve a stable demand is developed and tested on data from a real case. Keywords:Poison process, aggregate demand, order sizes, planning horizon. IntroductionCompanies are in the business of satisfying customer demands in the form of customer orders. This paper focuses on the operational characteristics of ordering behavior in the form of demand and discusses the underlying assumptions within a number of fields. These characteristics include volume per planning period (demand rate -often disaggregated to several time horizons and described as a distribution of stochastic variables), accumulation of demand over the planning period, the number of orders per product per planning period and the standard order quantities for each product. The characteristics of the make-to-order manufacturing are depicted by Hendry and Kingsman [9] and Marucheck and McClelland [10]:• resources are multi-task machinery and flexible workforce -a large number of possible end items within a product line, • product lead time is vital for customer satisfaction and agreed with customer before production commences,

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“…In manufacturing planning and control literature, much attention is given to the terms volume and product mix [4,25]. These are often seen as the critical parameters (together with the competitive priorities; quality, flexibility, reliability, and speed) when designing and evaluating supply chain or manufacturing planning and control systems.…”

Section: Introductionmentioning

confidence: 99%

“…The importance of this increases if the manufacturer is placed early in a supply chain. The lateness of the delivery of an upstream supplier chain affects the plans of all downstream companies and the total cost of supply chain increases.In manufacturing planning and control literature, much attention is given to the terms volume and product mix [4,25]. These are often seen as the critical parameters (together with the competitive priorities; quality, flexibility, reliability, and speed) when designing and evaluating supply chain or manufacturing planning and control systems.…”

mentioning

confidence: 99%

Towards an Analysis Methodology for Identifying Root Causes of Poor Delivery Performance

Nielsen

Dung

Eriksen³

et al. 2014

Foundations of Management

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This paper presents an analysis methodology for establishing the demand stability of the planning environment faced by a company and the impact on the stability from changes to sales order. The methodology focuses on three critical planning parameters derived from customer orders: product mix, volume, and order sizes. Furthermore, the methodology links the delivery performance of a company to the changes made to sales orders. Based on a test case application of the methodology, it is concluded that by accepting changes, the demand faced by the case company has become more unstable on product mix, volume, and the number of order lines per period, while some added stability has been achieved with regards to the order size distributions. Ultimately, by applying the methodology to the case company, it is found that by changing the sales orders, the company does not improve the delivery performance.Key words: Business Intelligence, diagnostics, Decision Support Systems, Performance Improvement Systems. IntroductionThe main challenge in today's businesses is not getting enough information to make qualified decisions, but rather structuring the information already available in a manner that facilitates pro-and reactive actions to improve the performance of the business. Nowadays, manufacturers compete not only on the prices of supplied products but also on the services supported to the customers. On-time delivery is one of the most important services that a manufacturer has to provide and thus on-time delivery becomes a critical performance measure. The importance of this increases if the manufacturer is placed early in a supply chain. The lateness of the delivery of an upstream supplier chain affects the plans of all downstream companies and the total cost of supply chain increases.In manufacturing planning and control literature, much attention is given to the terms volume and product mix [4,25]. These are often seen as the critical parameters (together with the competitive priorities; quality, flexibility, reliability, and speed) when designing and evaluating supply chain or manufacturing planning and control systems. Furthermore, it is known that many companies focus in some manner on On-Timein-Full (OTIF) delivery of customer orders as their main performance goal [16]. Despite extensive literature on this topic, very little attention has been given to the connection between delivery performance and the stability of the planning environment in the form of the demand faced by the manufacturer. In order to establish this connection, this research investigates the impact of changes to sales order lines on the stability of the planning environment and links these changes to the delivery performance of a company.It is known that the delivery performance is much affected by the frequency and type of changes to the production schedule, especially so in Make-To-Order (MTO) manufacturers, where each customer order is directly linked to the production schedule and it is difficult to get high delivery reliability [4...

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Replanning timing in hierarchical production planning

Cited by 6 publications

References 11 publications

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

Order Quantity Distributions in Make-to-Order Manufacturing: At What Level of Aggregation Do They Respect Standard Assumptions?

Towards an Analysis Methodology for Identifying Root Causes of Poor Delivery Performance

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