Application of Analytical Network Process and Conditional Probability Co-occurrences Matrix for Business Modelling of Small-Medium Enterprises

In Indonesia, the scope of agroindustry are related to the food and non-food industry managed by Small-Medium Enterprises (SME). The classical problem of Indonesian Agroindustry were related to logistic, infrastructure, technology, high-cost economy, regulation, and financing constraint. Therefore, an innovative business model is required for competitive and sustainable SME. Importance rate of the model can be defined by determining some criteria in a business model. Analytical Network Process (ANP) is required to determine importance rate of business model. However, ANP could not minimize the subjectivity factor of the respondent in determining the criteria. Application of Conditional Probability Co-occurrences Matrix (CPCM) is required to minimize the subjectivity factor by comparing priority weight of each criterias. The research objectives are: 1) To apply ANP method for representing business model criteria and attribute of SME; 2) To apply CPCM method for criteria pattern extraction. The case study of research is SME Bakpia Tela Ungu and Telopia. CPCM Pattern extraction of Contrast, Energy and Local Homogeneity indicated the significant different of business model criteria between food, non-food agroindustry and local governmental board. The research results indicated that there were different subjectivity to determine criteria priority weight

Development of Green-Affective Work System for Food SMEs

Work system of food Small and Medium-sized Enterprises (SMEs) is influenced by various factors as worker performance, characteristics of raw material, value-added process and workplace environmental ergonomics. Green-affective design analyzes properties of work systems and how these technical attributes could be sensible to the environment (Green) and worker (Affective). The research objectives were: 1) To explore the relationship between green and affective parameters in work systems of Food SMEs; 2) To design a green-affective work system for Food SMEs. Six (6) SMEs of different food products were used for the case studies as Crackers, Nuggets, Fish Chips, Bakpia, Tempe and Herbal Instant Beverages. Air conditioner was suggested to set the temperature set points for controlling environmental ergonomics. Green parameters were analyzed using calculation of air conditioner electricity cost at different workplace temperature set point. Affective parameters were analyzed using heart rate, worker energy consumption and rowan incentive plan. Research findings indicated air conditioner could be used to control environmental ergonomics based on the satisfied temperature set points and efficient electricity cost in work system of food SMEs. Keywords: Air Conditioner; Environmental Ergonomics; Heart Rate; Rowan Incentive Pla

Kansei Engineering for Quantification of Indigenous Knowledges in Agro-industrial Technology

The term of indigenous knowledge refers to specific local knowledge in consumer/user which should be incorporated by agro-industry to compete in a globalized worlds. This research highlighted Kansei Engineering as a potential approach to quantify indigenous knowledge in agro-industrial technology. The research objectives were: 1) To review the quantification tools of indigenous knowledges in agro-industrial technology using Kansei Engineering; 2) To characterize indigenous knowledges in Indonesian agro-industry. Case study was demonstrated in Indonesian food product, services and ergonomic technology. Quantification was characterized using widely developed quantification tools for indigenous knowledges. The research results concluded some indigenous knowledges which could be incorporated in indigenous knowledge-based innovations. Keywords: Agro-industry, Ergonomic technology, Product, Services, Technical parameter

KUSTOMISASI LINGKUNGAN RESTORAN UNTUK MAKAN DI TEMPAT (DINE-IN) DI ERA TATANAN KEHIDUPAN BARU

The Covid-19 pandemic in 2020 caused everything to change, including the pattern of human life. Currently, the activities carried out must comply with health protocols. Despite the restaurant's strict safety protocol measures, customers generally do not feel safe dine-in during a pandemic. This study classified the restaurant environment in the form of premium, deluxe, and standard classes using Kansei engineering. This study aimed to identify the attributes of an ergonomic environment in a restaurant for dine-in in the era of the post-pandemic era also find out the best alternative by Technique for Order Preference by Similiarity to Ideal Solution (TOPSIS). A total of 503 respondents from 3 provinces on the island of Java (East Java, Central Java, DI Yogyakarta) participated in the survey in this study. In detail, 41 respondents were needed for interviews, 418 respondents for attribute determination and 44 expert respondents for the TOPSIS. Kansei results generated 37 attributes in the premium, 39 attributes in the deluxe and 7 attributes in the standard classes. The research concluded that consumers tend to choose premium class facilities to dine-in at restaurants in the era of the new order of life. Keywords :  dine-in, ergonomics, kansei engineering, TOPSI

Seleksi Vendor Dengan Integrasi Anp – Topsis Dan Optimalisasi Alokasi Order Dengan Pendekatan Goal Programming (Studi Kasus Di PT. Xyz)

Salah satu faktor kesuksesan sebuah Perusahaan adalah pemilihan vendor. Pemilihan vendormerupakan masalah pengambilan keputusan penting agar mendapatkan pemasok yang dapatmeningkatkan daya saing Perusahaan. Dalam penelitian ini akan dikaji mengenai pemilihan vendor untuk dua macam subkomponen untuk produk spare part motor yaitu bolt flange (Part Code 90105060270080) dan washer plain (Part Code 90201208140080) di PT. XYZ. Penyelesaian masalah seleksi vendor dalam penelitian ini dibantu dengan menggunakan metode gabungan ANP-TOPSIS. Setelah didapatkan vendor terpilih kemudian dilakukan perhitungan goal programming untuk pengalokasian order kepada vendor-vendor terpilih tersebut. Berdasarkan hasil pengolahan data dengan metode gabungan ANP-TOPSIS didapatkan perangkingan untuk vendor bolt flange secara berurutan adalah PT. GMS dengan nilai c* =0,91960, PT. DPM dengan nilai c* = 0,49236, PT. GIP dengan nilai c* = 0,43609, dan PT. GNP dengan nilai c* = 0,27989. Sedangkan untuk vendor washer plain secara berurutan adalah PT. NCS dengan nilai c* = 0,79556, PT. IDS dengan nilai c* = 0,37118, PT. PSM dengan nilai c* = 0,34377, dan PT. CKP dengan nilai c* = 0,12120. Dari perangkingan tersebut dipilih dua vendor dengan urutan dua teratas. Dengan demikian vendor yang terpilih untuk memasok bolt flange adalah PT. GMS dan PT. DPM, sedangkan untuk washer plain adalah PT. NCS dan PT. IDS. Penelitian dilanjutkan untuk menentukan alokasi order kepada masing-masing vendor dengan metode goal programming

Artificial Neural Network Model for Affective Environmental Control System in Food SMEs

This paper presents an affective environmental control system for Small and Medium-sized Enterprises (SMEs). The system is proposed as a technology innovation in appropriate information technology. It is defined that workplace environment set points could be controlled using worker workload. The research objectives are: 1) To design an affective environmental control model for SME; 2) To develop an Artificial Neural Network (ANN) model for predicting affective environment set points. The system consisted of 4 sub-systems as measurement, assessment, control and decision. An ANN model is developed for sub-systems of control. Training and validation data are acquired from 4 (four) samples of SME in Yogyakarta Special Region, Indonesia. The model has been developed successfully to predict temperature and light intensity set points using back-propagation supervised learning method. The research results indicated the satisfied performance of ANN with minimum error. ANN model indicated the closeness of R2 value between training and validation data. The research results could be applied to support the worker productivity in food SMEs by providing a comfort workplace environment and optimum worker workload

Planning Occupational Safety and Health Management System (OSHMS) Based on The Covid-19 Pandemic Guidance at So Good Food Dairy Company

Companies need an Occupational Safety and Health (OSH) culture based on a new life guidance during the Covid-19 pandemic. The application of OSH at So Good Food Dairy Company is moderate, though there are no control measures to minimize this risk. This study further aimed to analyze the application of OSH in the workplace and determine the implementation of the OSH Management System (OSHMS). Furthermore, it focused on identifying the probability of accidents at each workstation and proposed risk mitigation plans and anticipatory steps. Questionnaires were then administered to collect primary data from 62 respondents at the So Good Food Dairy Company’s processing, filling, packing, and storage workstations. Similarly, secondary data were obtained from documented information about the company’s OSH. All the respondents provided valid answers with a Cronbach alpha value of 0.9671, considered very reliable. Moreover, the questionnaire responses showed that So Good Food Dairy Company was rated highly by workers. Failure Mode and Effects Analysis (FMEA) was used to identify the factors causing work accidents and Risk Priority Number (RPN), while recommendations for improvement were made based on the Hazard Identification Risk Assessment & Risk Control (HIRARC) principles. A total of 12 probability accidents were observed in processing, four in filling and packing, and six in storage. Therefore, this study proposes an OSH design that includes an OSHMS planning based on clauses ISO 45001:2018 and ISO/PAS 45005:2020. Additionally, seven OSH programs and a risk mitigation road map using the Pareto Chart principle to set priorities were recommended

Continuous Handling of Uncertainty in Food Chains: Using the House of Risk Model in Ecosystems

The house of risk model represents an approach to mitigate risk through systematically analysing data risk agents based on empirical findings through prioritizing them. Food production is associated with uncertainty both within the production system as well as in environment. Given the state of current technology, including its rapid development impacting on connectivity in supply chains, the house of risk model is considered through this conceptual study applying an ecosystems approach on how to mitigate risk in food chains in their many-faceted environmental setting. Ecosystems thinking is rooted in a normative quest to secure sustainability. It also is at the operations level a complex system. It is pointed out that an ecosystems approach encompasses mixed methods, including both deterministic and complex systems. The nature of this complementarity is discussed. The study provides a list of four issues regarding using the house of risk model within an ecosystem: (1) ethical, (2) development, (3) operations and (4) discourse

An Optimization Model for Environmental Ergonomics Assessment in Bioproduction of Food SMEs

Environmental ergonomics in bioproduction of food Small Medium-sized Enterprises (SMEs) become a concern and need to be optimized. An optimization model was developed using a Genetic Algorithm (GA). The weight of an Artificial Neural Network Model was used as a fitness function for GA. The research objectives were: 1) To design an environmental ergonomic assessment system for bioproduction of Food SMEs, 2) To develop an optimization model for environmental ergonomic assessment using a Genetic Algorithm. GA is utilized to search optimal set points of environmental ergonomics based on the predicted fitness values. Each chromosome of GA represents the environmental ergonomics value. The parameters were heart rate, bioproduction temperature, distribution of bioproduction relative humidity and light intensity. The target of the optimization model was the bioproduction temperature set points. The research result indicated the model generated optimum values of environmental ergonomics parameter in bioproduction of food SMEs. The parameters could be used to provide standard workplace environment for the sustainability of food SMEs

An Intelligent Incentive Model Based on Environmental Ergonomics for Food SMEs

In this study, an intelligent incentive model based on environmental ergonomics in food small and medium-sized enterprises (SMEs) was developed. Environmental ergonomics was defined as the impact of temperature and relative humidity within a certain range on a worker's heart rate during work. Optimum environmental ergonomics are highly required as a basic standard for food SMEs to provide fair incentives. Recommendable parameters from a genetic algorithm and fuzzy inference modeling were used to model customized incentives based on optimum heart rate, workplace temperature and relative humidity before and after working. The research hypothesis stated that industries should optimize their workload and workstation environment prior to customizing incentives. The research objectives were: 1) to recommend optimum environmental ergonomics parameters for customized incentives; 2) to determine the incentives at workstations of SMEs based on optimum environmental ergonomics parameters and fuzzy inference modeling. The optimum values for heart rate, workstation temperature and relative humidity used were based on recommendable values from the genetic algorithm. An inference model was developed to generate decisions whether a worker should receive an incentive based on a calculated index. The results indicated that 84.4% of workers should receive an incentive. The results of this research could be used to promote the concept of ergonomics-based customized incentives