Technical Note—On the Feasibility of Aggregate Production Plans

Axsäter, Sven

doi:10.1287/opre.34.5.796

Cited by 49 publications

(6 citation statements)

References 11 publications

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“…In practice only the ideal situation is considered. This coincides with the assumption that if HDP is to work, the aggregate demand plan must successfully be disaggregated to a number of feasible plans [6]. The assumption is a one-toone relationship in aggregation and disaggregation [1,2].…”

Section: The Demand Planning Connectionmentioning

confidence: 54%

Evaluating the Standard Assumptions of Demand Planning and Control

Nielsen

Steger-Jensen

Advances in Production Management Systems

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This paper investigates customer ordering behavior and compares a particular instance to the standard assumptions of demand planning and control. Experience shows that the performance of these planning processes often do not match what could be expected. Based on a presented case, implications for demand planning performance are inferred. This leads to an analysis tool for diagnosing potential problems in the demand planning approach.

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Section: The Demand Planning Connectionmentioning

confidence: 54%

Evaluating the Standard Assumptions of Demand Planning and Control

Nielsen

Steger-Jensen

Advances in Production Management Systems

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“…So in practice the less need to aggregate information, the better planning can be performed. To facilitate the aggregation and disaggregation of information a number of assumptions must be made [24,25]. These assumptions are simple in nature and can basically be reduced to assuming a one-to-one relationship exists when aggregating and disaggregating [19,26] and complete independence of demand.…”

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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“…1. First, most prior research uses the same planning periods at each level of the decision-making hierarchy [e.g., [10][11][12][13][14][15]. This is illustrated in Fig.…”

Section: Temporal Decompositionmentioning

confidence: 99%

A hierarchical production planning system simulator

White¹

2012

Int. j. simul. model.

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This research develops and demonstrates a Hierarchical Production Planning System Simulator (HPPSS) for the analysis of feedback-and-control mechanisms between linear programming models at two levels within a Hierarchical Production Planning (HPP) system. This work focuses on planning levels dealing with tactical decisions. Three areas distinguish it from prior research. First, the HPPSS allows explicit examination of the effects of different information exchanges between the levels of a hierarchical model. Second, the HPPSS allows examination of the problems for which a given feedback-and-control mechanism performs well. Finally, the HPPSS allows the effects of a rolling horizon implementation on the hierarchical models of the production planning problem to be investigated. The significance is that a more thorough understanding of the costs and benefits of various mechanisms for information exchange between the levels of hierarchical models of planning problems over time will lead to improved hierarchical decision-making techniques that may influence organizational design.

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Technical Note—On the Feasibility of Aggregate Production Plans

Cited by 49 publications

References 11 publications

Evaluating the Standard Assumptions of Demand Planning and Control

Evaluating the Standard Assumptions of Demand Planning and Control

Order Quantity Distributions: Estimating an Adequate Aggregation Horizon

A hierarchical production planning system simulator

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