Machine learning demand forecasting and supply chain performance

Without a doubt, demand forecasting is an essential part of a company’s supply chain. It predicts future demand and specifies the level of supply-side readiness needed to satisfy the demand. It is imperative that if a company’s forecasting isn’t reasonably reliable, the entire supply chain suffers. Over or under forecasted demand would have a debilitating impact on the operation of the supply chain, along with planning and logistics. Having acknowledged the importance of demand forecasting, one must look into the techniques and algorithms commonly employed to predict demand. Data mining, statistical modeling, and machine learning approaches are used to extract insights from existing datasets and are used to anticipate unobserved or unknown occurrences in statistical forecasting. In this paper, the performance comparison of various forecasting techniques, time series, regression, and machine learning approaches are discussed, and the suitability of algorithms for different data patterns is examined.

Download Full-text

Impacts of Manufacturer Overconfidence on Supply Chain Performance with Demand Forecasting

DEStech Transactions on Computer Science and Engineering ◽

10.12783/dtcse/cisnrc2019/33311 ◽

2019 ◽

Author(s):

XIAOGUANG LIU ◽

XIFU WANG ◽

LUFENG DAI

Keyword(s):

Supply Chain ◽

Demand Forecasting ◽

Supply Chain Performance

Download Full-text

System dynamics modeling with fuzzy logic application to mitigate the bullwhip effect in supply chains

Journal of Modelling in Management ◽

10.1108/jm2-04-2018-0045 ◽

2019 ◽

Vol 14 (3) ◽

pp. 610-627 ◽

Cited By ~ 3

Author(s):

Mehdi Poornikoo ◽

Muhammad Azeem Qureshi

Keyword(s):

Fuzzy Logic ◽

Supply Chain ◽

System Dynamics ◽

Dynamic Models ◽

Demand Forecasting ◽

Bullwhip Effect ◽

Automated System ◽

Supply Chain Performance ◽

Inference System ◽

Content Type

Purpose A plethora of studies focused on the cause and solutions for the bullwhip effect, and consequently many have successfully experimented to dampen the effect. However, the feasibility of such studies and the actual contribution for supply chain performance are yet up for debate. This paper aims to fill this gap by providing a holistic system-based perspective and proposes a fuzzy logic decision-making implementation for a single-product, three-echelon and multi-period supply chain system to mitigate such effect. Design/methodology/approach This study uses system dynamics (SD) as the central modeling method for which Vensim® is used as a tool for hybrid simulation. Further, the authors used MATLAB for undertaking fuzzy logic modeling and constructing a fuzzy inference system that is later on incorporated into SD model for interaction with the main supply chain structure. Findings This research illustrated the usefulness of fuzzy estimations based on experts’ linguistically and logically defined parameters instead of relying merely on the traditional demand forecasting based on time series. Despite the increased complexity of the calculations and structure of the fuzzy model, the bullwhip effect has been considerably decreased resulting in an improved supply chain performance. Practical implications This dynamic modeling approach is not only useful in supply chain management but also the model developed for this study can be integrated into a corporate financial planning model. Further, this model enables optimization for an automated system in a company, where decision-makers can adjust the fuzzy variables according to various situations and inventory policies. Originality/value This study presents a systemic approach to deal with uncertainty and vagueness in dynamic models, which might be a major cause in generating the bullwhip effect. For this purpose, the combination between fuzzy set theory and system dynamics is a significant step forward.

Download Full-text