The impact of demand parameter uncertainty on the bullwhip effect

The bullwhip effect is a very important issue for supply chains, impacting on costs and effectiveness. Academic researchers have studied this phenomenon and modelled it analytically, showing that it affects many real world industries. The analytical models generally assume that the final demand process and its parameters are known. This paper studies a two-echelon single-product supply chain with final demand distributed according to a known AR(1) process but with unknown parameters. The results show that the bullwhip effect is affected by unknown parameters and is influenced by the frequency with which parameter estimates are updated. For unknown parameters, the strength of the bullwhip effect is also influenced by the number of demand observations available to estimate the parameters. Furthermore, a negative autoregressive parameter does not always imply an anti-bullwhip effect when the parameters are unknown. An analytical approximation is proposed to mitigate the poor accuracy of existing models when the parameters of an AR(1) process are unknown, forecasts are updated but parameter estimates remain unchanged

Evaluation of bottom-up and top-down strategies for aggregated forecasts: state space models and arima applications

Abstract. In this research, we consider monthly series from the M4 competition to study the relative performance of top-down and bottom-up strategies by means of implementing forecast automation of state space and ARIMA models. For the bottomup strategy, the forecast for each series is developed individually and then these are combined to produce a cumulative forecast of the aggregated series. For the top-down strategy, the series or components values are first combined and then a single forecast is determined for the aggregated series. Based on our implementation, state space models showed a higher forecast performance when a top-down strategy is applied. ARIMA models had a higher forecast performance for the bottom-up strategy. For state space models the top-down strategy reduced the overall error significantly. ARIMA models showed to be more accurate when forecasts are first determined individually. As part of the development we also proposed an approach to improve the forecasting procedure of aggregation strategies

A model of co-design relationships: definitions and contingencies

In the ever more turbulent business environment firms concentrate on their core capabilities and resort to suppliers as sources of complementary know-how. In other words they tend to co-design their products. This paper shows that co-design may occur in different forms and that success of supplier involvement in product development mainly depends on the proper choice of the type of relationship according to the contingencies to be dealt with. In particular, by adopting a problem solving perspective and a case study approach, we have identified four different approaches to co-design, depending on the type of knowledge transferred from the supplier to the customer (product knowledge or process knowledge) and the degree of interaction between the partners. In this latter regard, a co-design relationship may occur with a loose interaction (when the customer defines the component specifications and the supplier designs the solution that better fits those specifications) or a tight interaction (when the problem solving process is not split between the partners). The paper shows that the choice between a joint or split co-design approach depends on two context factors: the uncertainty of the design endeavour (i.e., the novelty of the component to be developed and the turbulence of the environment) and the relational capabilities (i.e., the capabilities to manage the information flows occurring between the two patterns)

The implementation process of customer-suppliers partnership: lessons from a clinical perspective

Explores the implementation process of the customer-supplier partnership in the machinery industry. Aims to assess the dynamic impacts on performance of various practices that are generally regarded as typical of a customer-supplier partnership. Also, investigates internal consistency of various practices and possible path dependencies. Shows, in the case of single sourcing, the complementarity and the synergistic effects of co-design, sharing production plans, dedicating up-stream capacity and assuring down-stream demand. Finds the key role of a complete performance control system to monitor delivery, quality and cost performance, even when tight control over suppliers might be regarded as less crucial due to trust and long-termism. Discusses the issue of dedicated EDI assets and shows that, at least in some cases, delaying investments might significantly reduce risks of sunk cost for both partners. Shows that, while some players on both sides of the relationship perceive the relationship as a "win-win" situation, others play a "zero sum game"

Multi-level approaches to demand management in complex environments: An analytical model

Recent studies have shown that as demand becomes irregular and complex (i.e., lumpy), a possible approach for managing such uncertainty is to collect information directly from customers. This implies that the sales units have to move closer to customers, analyse their likely requirements, and collect quantitative and structured data as well as qualitative and subjective insights. However, as integration with individual customers increases and data collection capabilities improve the organisational configuration of most companies becomes ever more complex and the aggregation of forecasts more difficult. This paper discusses two approaches to managing demand uncertainty in complex environments. In the first (termed decentralised order overplanning), sales units are responsible for forecasting the demand of each customer and defining requirements. In the second (termed centralised order overplanning), forecasts provided by sales units are aggregated and further elaborated by manufacturing to define item requirements. By means of an analytical model (which describes the forecasting and planning process as a Bayesian-Markovian process), we show that the centralised method out-performs the decentralised approach by virtue of the ability to exploit the additional information provided by commonalities between customers requests. However, this advantage has to be balanced against organisational costs. Since the centralised method splits responsibilities for forecasting and slack control between sales and manufacturing units, major conflicts are likely to arise, the focus and commitment on forecasting accuracy may be compromised, and information may be lost when individual forecasts are sent to the manufacturing unit