Evaluating the performance of aggregate production planning strategies under uncertainty in soft drink industry

Abstract

The present study is to evaluate the performance of different aggregate production planning (APP) strategies in presence of uncertainty. Therefore, the relevant models for APP strategies including the pure chase, the pure level, the modified chase, the modified level and the mixed chase and level strategies are constructed by using both multi-objective programming and simulation methods. The models constructed for these strategies are run with respect to the corresponding objectives/criteria in order to provide business insights to operations managers about the effectiveness and practicality of various APP strategies in presence of uncertainty. The real world operational data are collected from soft drink industry to validate and implement the models.In addition, multiple criteria decision making (MCDM) methods are used besides multi-objective optimisation to assess the overall performance of each APP strategy. A detailed sensitivity analysis is also conducted by changing the criteria weights in MCDM methods to evaluate the impacts that these weight changes can have on the final rank of each APP strategy.The results of the simulation models are compared to those of multi-objective optimisation models.In general, in both mathematical programming and simulation models, the pure chase and the modified chase strategies presented the best performance, followed by the pure level strategy.

An Icon-Based Methodology for the Design of a Prototype of a Multi-Process, Multi-Product, Aggregated Production Planning Software

“…Examples include [34] and Aydin et al (2022) [8], who propose models incorporating sustainability aspects. Darvishi et al (2020) [32] investigated APP in the textile industry under uncertainty conditions, while Jamalnia et al ( 2019) [36] worked on comparing APP strategies under uncertainty conditions. Genetic algorithms have also been a focus of analysis in APP problems for researchers such as Goli et al ( 2019) [1] and Yuliastuti et al (2019) [37].…”

“…The study evaluates different APP strategies in the presence of uncertainty, using multi-objective optimization and simulation models, with validation on real data from the beverage industry [36].…”

Competitiveness and R&D Subsidies: The Case of the Industry 4.0 Program in Portugal

“…The most recent studies on competitiveness (Chabowski & Mena, 2017;Gordeev, 2020;Laureti & Viviani, 2011;Prasetyo, 2016;Wang & Turkina, 2020;Zhang & London, 2013) measure industrial productivity and use variables related to the value added and monthly physical production (relative prices, industrial organization, and quality), variables related to hours worked, hours paid, and occupied work force. The following theoretical currents on productivity stand out: the neoclassical mainstream (total factor productivity), evolutionist, X-efficiency, managerial and behavioral, neo-Marxist, industrial organization, Kaldor-Verdoorn, comparative advantage, and endogenous growth theories (Bulgarelli & Porto, 2011;Cao et al, 2015;Jamalnia et al, 2019;Lee, 2021;Mendonça et al, 2021;Mendonça et al, 2022;Saboia & Carvalho, 1997).…”

Dynamic Programming Approach in Aggregate Production Planning Model under Uncertainty

“…The nature of the data or input parameters in real-world APP issues, such as those involving demand, resources, costs, objective function coefficients, etc., is inherently imprecise due to the fact that some information cannot be retrieved or is unavailable in its whole [11]. In business practice, products usually have an uncertain demand and variable [12], customer preferences change, production capacity is limited [13], labor market conditions are unstable, subcontracting can incur higher costs [14], uncertainty of raw material supply [15], and an increase in backorders caused customer claim and led them to change the source of their purchases [8], [16]. This demonstrates the complex characteristics of APP and an appropriate APP model is needed.…”

“…The forecasts of future demand are the most important input for the creation of the APP strategy. A highly unpredictable demand results in frequent revisions of production planning from one planning period to the next [8], [15], [17]. This not only results in anxiety and nervousness within the production environment [4], but it is also one of the primary drivers of costs due to its adverse effects on labor and supply levels [5].…”