Incorporating macroeconomic leading indicators in tactical capacity planning

Sagaert, Yves; Kourentzes, Nikolaos; Vuyst, Stijn De; Aghezzaf, El‐Houssaine; Desmet, Bram

doi:10.1016/j.ijpe.2018.06.016

“…The next step was to classify the retention stock/holding inventory into the active, inactive, and dead inventory through statistical assessment as per the laid down criteria [ 34 ]. This classification ( Table 3 ) helps organizations; (1) to maintain control over the critical items with vast sums of capital invested in them (2) to ensure that the inventory turnover ratio is methodically maintained at a reasonably higher level through systematic control of holding stock (3) to make sure optimum levels of inventory is preserved at all times [ 23 ]. At stage-2, the outcomes of fault trend analysis ( Fig 3 ) are used to determine the frequencies of multiple faults to identify the related spares requirement and inventory trend.…”

Section: Results Discussionmentioning

confidence: 99%

“…Numerous collaborative inventory management models tend to focus on the inventory decisions made by organizations participating in different cooperative programs, such as CRP, ECR, QR, and VMI. However, collaboration is a decision-making process that includes inter-dependent firms [ 23 , 24 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Development of a hybrid framework for inventory leanness in Technical Services Organizations

Rehmani

¹

,

Naseem

²

,

Yaraghi

³

et al. 2021

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Inherent uncertainties in demand and supply make it problematic for supply chains to accomplish optimum inventory replenishment, resulting in loss of sales or keeping excessive inventories. To cope with erratic demands, organizations have to maintain excessive inventory levels, sometimes taking up to one-third of an organization’s annual budget. The two most pressing concerns to handle in inventory management are: how much to order and when to order. Therefore, an organization ought to make the correct and timely decisions based on precise demand information to avoid excessive inventory accumulation resulting in enhanced competitive advantage. Owing to the significance of inventory control and analysis, this paper reports on developing and successfully implementing a hybrid framework for optimum level inventory forecasting in Technical Services Organizations. The proposed framework is based on a case study of one of Pakistan’s leading Technical Services Organization. The paper presents a statistical analysis of historical data and a comprehensive fault trend analysis. Both these analyses set a solid foundation for the formulation of a comparative analysis matrix based upon price and quantity based analysis of inventory. Finally, a decision criterion (Forecasting Model) is proposed using three primary forecasting techniques with minimum error calculations. The study’s finding shows a forecast error of 142.5 million rupees in the last five years, resulting in the accumulation of more than 25 thousand excessive inventory stock. Application of price and quantity based analysis identifies that 65% of the annual budget is significantly dependent upon only 9% (in terms of quantity) of "High Price and Small Quantity" Items (HS). These HS items are forecasted through three different forecasting methods, i.e., Weighted Moving Average, Exponential Smoothing, and Trend Projection, with Minimum Absolute Deviation to significantly reduce the forecasting error while predicting the future required quantity. The research work aims to contribute to the inventory management literature in three ways. First, a new comparative analysis matrix concept for identifying the most critical items is introduced. Second, a Multi-Criteria Forecasting Model is developed to capture a wide range of operations. Third, the paper suggests how these forecasting criteria can be integrated into a single interactive DSS to maintain optimum inventory level stock. Even though the DSS framework is based on data from a single organization, the application is expected to manage inventory stock in a wide range of manufacturing and services industries. This study’s proposed hybrid framework is the first of its kind that encapsulates all four dimensions of inventory classification criteria, forming a multi-criteria hybrid model within a DSS framework.

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“…Nonetheless, in the literature, there are papers that have questioned this assumption, and arguing for directly using stock control metrics in the context of inventory management (for examples see, Gardner, 1990a;Boylan, 2005, 2006;Teunter and Duncan, 2009;Syntetos et al, 2010;Kourentzes, 2013;Syntetos et al, 2015a;Sagaert et al, 2018).…”

Section: Background Researchmentioning

confidence: 99%

“…Indirectly, a reasonable approximation of the data generating process is still required, but one has to note that this is less straightforward than in the conventional case. For example, consider operational production planning, where a consistent forecast across time may be more beneficial than a very accurate, but volatile, forecast (Sagaert et al, 2018;Fildes and Kingsman, 2011). Nonetheless, if the quality of the forecast is very poor, then that impacts negatively on the operations.…”

Section: Optimising Directly On Inventory Performancementioning

confidence: 99%

Optimising forecasting models for inventory planning

Kourentzes

¹

,

Trapero

²

,

Barrow

³

2020

International Journal of Production Economics

Self Cite

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Inaccurate forecasts can be costly for company operations, in terms of stockouts and lost sales, or over-stocking, while not meeting service level targets.The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter.

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“…Nonetheless, in the literature, there are papers that have questioned this assumption, and arguing for directly using stock control metrics in the context of inventory management (for examples see, Gardner Jr, 1990;Boylan, 2005, 2006;Teunter and Duncan, 2009;Syntetos et al, 2010;Kourentzes, 2013;Syntetos et al, 2015;Sagaert et al, 2018). Substantial work has been done in the intermittent demand literature, where forecast evaluation is problematic when using conventional error metrics (Kolassa, 2016).…”

Section: Research Backgroundmentioning

confidence: 99%

Optimising Forecasting Models for Inventory Planning

Kourentzes

¹

,

Trapero

²

,

Barrow

³

2019

Self Cite

View full text Add to dashboard Cite

Inaccurate forecasts can be costly for company operations, in terms of stockouts and lost sales, or over-stocking, while not meeting service level targets.The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify

show abstract

Incorporating macroeconomic leading indicators in tactical capacity planning

Cited by 16 publications

References 36 publications

Development of a hybrid framework for inventory leanness in Technical Services Organizations

Development of a hybrid framework for inventory leanness in Technical Services Organizations

Optimising forecasting models for inventory planning

Optimising Forecasting Models for Inventory Planning

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