APLIKASI ACTIVITY-BASED COSTING PADA UMKM

The fact that there was a challenge for the UMKM sector was the lack of capability in terms of business menegement. One of them was accuracy in calculating the amount of the main price of production on the resulting product. even though that calculation was crucial in determining the selling price of the product. Therefore, it was important to apply a better method to make the right decision. The purpose of this study was to analyze the comparison of the calculation of the price of production using traditional costing (TC) and Activity-Based Costing (ABC) methods, as well as analyzed the advantage in gaining the application of this method. This research was a descriptive qualitative study with interview methods and documentation. The analysis showed that in the calculation of the underlying price between TC production and ABC, there was an undercosted of 4.04%, while for the profit difference there was 1.2%.

A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems

In the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm Optimization (PSO) and an efficient operator of Artificial Bee Colony Optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (Pbest). Thus, the need for external intervention is inevitable once a useful particle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm Optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants; the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of BitABC is 2. In comparison with hybrid techniques, ScPSO obtains the best Total Average Rank (TAR) as 1.375, and TSS of ScPSO ranks first (6) again. The fitness values obtained by ScPSO are generally more satisfactory than the values obtained by other methods. Consequently, ScPSO achieve promising gains over other optimization methods; in parallel with this result, its usage can be extended to different working disciplines. Highlights PSO parameters are ineffective to regenerate the awkward particle that cannot improve its pbest. An external intervention is inevitable once a particle becomes an awkward one. ScPSO is obtained with the addition of scout bee phase into the PSO. So an evolutionary method eliminating the most important handicap of PSO is gained. ScPSO is compared with the variants and with hybrid versions of PSO and ABC methods. According to the experiments, ScPSO results in better optimal solutions. The fitness values of ScPSO are generally more satisfactory than the others. Consequently, ScPSO achieve promising gains over other optimization methods. In parallel with this, its usage can be extended to different working disciplines.

Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

Menu engineering and activity-based costing

Purpose This paper aims to examine whether using menu engineering (ME) together with activity-based costing (ABC) for menu analysis provides new insights into true menu profitability. The traditional ME approach only uses food costs to determine the contribution margin (CM) of individual menu items. This combined approach uses both food and traceable operating costs to estimate CMs more accurately. Design/methodology/approach An improved ME model was developed and tested in an oriental restaurant in Brazil. Direct observation of restaurant activities allowed most costs to be traced (not simply allocated) to individual menu items. Findings The results revealed small differences in the rankings between the traditional approach and ABC/ME, demonstrating that the integration of ABC with ME made it to possible to identify increased food-costs and lower CMs for all groups of menu items. The results also show that ABC methods are applicable to an oriental-style restaurant. Research limitations/implications Just one restaurant and only 80 per cent of the menu were examined in this study. Future research should apply the model used here to other restaurant types located in different geographical areas to validate the approach. Practical implications The results suggest that ME can be improved upon by first assessing variable costs using ABC methods. Originality/value This paper combines two different analytic techniques (ME and ABC) into a new approach that reveals the true picture of profit and loss for a menu from a restaurant in Brazil.