Abstract

Introduction. The introduction of modern management methods in food enterprises is a response to the challenges of time and an institutionalized requirement. Statistical methods and information technologies can improve control in food safety management systems. Their combination provides maximum opportunities for leading food industry organizations.
Study objects and methods. The research featured the market of ready-made high-tech solutions in process automation and existing management systems, including those based on the principles of Hazard Analysis and Critical Control Points (HACCP). The study revealed a need for a software product for HACCP in food production.
Results and discussion. The paper describes a case of a meat processing plant and how the management controls the HACCP. The authors developed a universal software product that allows operators to enter information about violations and downtimes into the system. Using this product, team leaders and department heads can easily identify and eliminate the causes of hazards while controlling technological processes and receiving timely reports.
Conclusion. The software was tested in a production workshop to identify the time specialists need respond to comments and solve problems. The obtained data showed a decrease in loss of time resources by 6.77% for operators and 2.4% for line managers. The highly adaptable program can be used by specialists who work with management systems for food enterprises of Kuzbass. The IBM PC-compatible software product (2.2 MB) makes it possible to work in various Microsoft Windows operating systems and use Microsoft SQL Server 2012 to store data. The computer program was successfully registered with the Federal Service for Intellectual Property (State Registration Certificate No. 2018665743 of 12/10/2018).

Keywords

Information technology, automation, registration, rejection, quality, security

REFERENCES

1. Ermolaeva EO. Identifikatsiya i ustranenie riskov v zhiznennom tsikle produktov pitaniya [Identification and elimination of hazards in the life cycle of food]. Kemerovo: Kemerovo Technological Institute of Food Industry; 2015. 110 p. (In Russ.).
2. Trofimova NB, Rubashanova EA, Poznyakovskiy VM. Using risk-oriented approach when improving the management system at the enterprises of agro-industrial complex. Agro-Industrial Complex of Russia. 2017;24(3):759–763. (In Russ.).
3. Muda I, Afrina E. Influence of human resources to the effect of system quality and information quality on the user satisfaction of accrual-based accounting system. Contaduria y Administracion. 2019;64(2):1–24. DOI: https://doi.org/10.22201/fca.24488410e.2019.1667.
4. Chen H, Liu S, Chen Y, Chen C, Yang H, Chen Y. Food safety management systems based on ISO 22000:2018 methodology of hazard analysis compared to ISO 22000:2005. Accreditation and Quality Assurance. 2019. DOI: https://doi.org/10.1007/s00769-019-01409-4.
5. Rossieva DV, Ermolaeva EO, Trofimova NB, Trofimov IE. Development of software product to support the process of internal audit of a food company. Food Processing: Techniques and Technology. 2017;46(3):135–140. (In Russ.).
6. Surkov IV, Ermolaeva EO, Rossieva DV, Trofimova NB, Trofimov IE. Ehkspertnaya sistema opredeleniya opasnostey i kontrolʹnykh tochek proizvodstvennykh protsessov po printsipam KHASSP [Expert system for determining hazards and control points of production processes according to the principles of HACCP]. Certificate of software registration № 2018666105. 2018.
7. Surkov IV, Ermolaeva EO, Rossieva DV, Trofimova NB, Trofimov IE. Uchet i analiz nesootvetstviy vnutrennego audita integrirovannoy sistemy menedzhmenta (Uchet i analiz nesootvetstviy vnutrennego audita ISM) [Accounting and analysis of hazards of the internal audit of the integrated management system (Accounting and analysis of inconsistencies of the internal audit of the IMS)]. Certificate of software registration № 2017616985. 2017.
8. Prokhorov AA, Ermolaeva EO, Trofimova NB, Trofimov IE. Uchet opasnykh faktorov i nesootvetstviy protsessov proizvodstva pishchevoy produktsii [Hazard analysis and control points in food production processes]. Certificate of software registration № 2018665743. 2018.
9. Angelopoulos CM, Filios G, Nikoletseas S, Raptis TP. Keeping data at the edge of smart irrigation networks: A case study in strawberry greenhouses. Computer Networks. 2020;167. DOI: https://doi.org/10.1016/j.comnet.2019.107039.
10. Prosekov AYu, Ivanova SA. Providing food security in the existing tendencies of population growth and political and economic instability in the world. Foods and Raw Materials. 2016;4(2):201–211. DOI: https://doi.org/10.21179/2308-4057-2016-2-201-211.
11. Villaverde JJ, Sevilla-Moran B, Lopez-Goti C, Alonso-Prados JL, Sandín-Espana P. QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation. SAR and QSAR in Environmental Research. 2020;31(1):49–72. DOI: https://doi.org/10.1080/1062936X.2019.1692368.
12. Drozdov D, Atmojo UD, Pang C, Patil S, Ali MI, Tenhunen A, et al. Utilizing software design patterns in product-driven manufacturing system: A case study. Studies in Computational Intelligence. 2020;853:301–312. DOI: https://doi.org/10.1007/978-3-030-27477-1_23.
13. Assuncao WKG, Vergilio SR, Lopez-Herrejon RE. Automatic extraction of product line architecture and feature models from UML class diagram variants. Information and Software Technology. 2020;117. DOI: https://doi.org/10.1016/j.infsof.2019.106198.
14. Woźniak D, Gohardani B, Majchrzak E, Hoti E, Urikova O. Product lifecycle management service system. Advances in Intelligent Systems and Computing. 2020;1035:525–533. DOI: https://doi.org/10.1007/978-3-030-29035-1_51.
15. Wang L, Liu C. Evolutionary game analysis on government supervision and dairy enterprise in the process of product recall in China. International Journal of Information Systems in the Service Sector. 2020;12(1):44–66. DOI: https://doi.org/10.4018/IJISSS.2020010104.
16. Deǧerli M, Özbudak EK, Asli Aytaç A, Nur Çolakoǧlu F, Demirel OE. Project-level audits as part of an effective quality assurance process: Applied practices and relevant lessons learned. CEUR Workshop Proceedings. 2017;1980:391–402.
17. Salehi V. Development of an agile concept for MBSE for future digital products through the entire life cycle management called Munich agile MBSE concept (MAGIC). Computer-Aided Design and Applications. 2020;17(1):147–166. DOI: https://doi.org/10.14733/cadaps.2020.147-166.
18. Rathi N, Srivathsav R, Chitlangia R, Pachghare VK. Automatic selenium code generation for testing. Advances in Intelligent Systems and Computing. 2020;1039:194–200. DOI: https://doi.org/10.1007/978-3-030-30465-2_22.
19. Ekren BY. A simulation-based experimental design for SBS/RS warehouse design by considering energy related performance metrics. Simulation Modelling Practice and Theory. 2020;98. DOI: https://doi.org/10.1016/j.simpat.2019.101991.
20. Zeller A, Jazdi N, Weyrich M. Functional verification of distributed automation systems: Assisting production line operators by an automated model composition. International Journal of Advanced Manufacturing Technology. 2019;105(9):3991–4004. DOI: https://doi.org/10.1007/s00170-019-03791-2.
21. Liu S, Tan X, Liu C-Y, Zhu C-L, Li W-H, Cui S, et al. Recognition of fusarium head blight wheat grain based on hyperspectral data processing algorithm. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis. 2019;39(11):3540–3546. DOI: https://doi.org/10.3964/j.issn.1000-0593(2019)11-3540-07.
22. Krueger C, Clements P. Feature-based systems and software product line engineering with Gears from BigLever. ACM International Conference Proceeding Series. 2019. DOI: https://doi.org/10.1145/3307630.3342393.
23. Gupta A, Maurya S, Kumar A. Smart control and monitoring of bottle filling system based on SCADA. International Journal of Recent Technology and Engineering. 2019;8(2):2117–2119. DOI: https://doi.org/10.35940/ijrte.B1214.0982S1119.