Unapređenje prognoze prijevozne potražnje kod dopreme prehrambenih proizvoda

master's thesis

• Title (english): Improving of Transport Demand Forecast in Food Products Delivery
• Author: Hrvoje Zelenika
• Mentor: Mario Šafran (mentor)
• Committee member: Kristijan Rogić (predsjednik povjerenstva)
• Committee member: Ratko Stanković (član povjerenstva)
• Committee member: Diana Božić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Transport and Traffic Sciences; (Division of Intelligent Transport Systems and Logistics); (Department of Transport Logistics); Zagreb
• Defense date and country: 2021-09-22, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Traffic and Transport Technology; Intelligent Transport Systems and Logistics

Abstract

Planiranje i kontrola aktivnosti lanca opskrbe ovisi o točnim procjenama količine proizvoda i usluga koje treba obraditi, a procjene dolaze kao prognoze. Jedan od najvećih izazova proizvođača hrane i pića je prilagoditi proizvodnju i zalihe kako bi se smanjili gubici proizvoda zbog kratke kvarljivosti. Analiza vremenskih serija vrlo je važna u širokom rasponu primjena, osobito kada je u pitanju predviđanje, i obuhvaća mnogo različitih modela predviđanja. Međutim, potrebno je utvrditi koji model najbolje odgovara svakoj situaciji. Postoji bezbroj modela za izradu prognoza predstavljenih u bibliografiji. Od istraživanja tržišta do najsloženijih metoda računanja. Referenca prikazuje tri glavne skupine ovih metoda, a to su: kvalitativne, povijesne projekcije i uzročne. Ovisno o seriji i željenom vremenu predviđanja, moguće je odabrati tehniku koja najbolje odgovara. Osim odabira najbolje tehnike, predviđanje koje će generirati odabrani model treba biti što je moguće bliže stvarnom koliko je moguće. Drugim riječima, pogreške u predviđanju trebale bi se minimizirati, pa voditelji proizvodnje planiraju nepažnju proizvodnje na tržištu i minimiziraju troškove. Metode predviđanja potražnje mogu se temeljiti na matematičkim modelima koji koriste povijesne podatke ili na kvalitativnim metodama, planiranim prema administrativnom iskustvu i recenzijama kupaca.

Abstract (english)

The planning and control of supply chain activities depend on accurate estimates of the amount of products and services to be processed, and estimates come as forecasts. One of the biggest challenges for food and beverage manufacturers is to adjust production and stocks to reduce product losses due to short-term perishability. Time series analysis is very important in a wide range of applications, especially when it comes to prediction and encompasses many different prediction models. However, it is necessary to determine which model best suits each situation. There are countless forecasting models presented in the bibliography. From market research to the most complex calculation methods. The reference shows three main groups of these methods, namely: qualitative, historical projections, and causal. Depending on the series and the desired prediction time, it is possible to choose the technique that works best. In addition to selecting the best technique, the prediction that the selected model will generate should be as close to the actual as possible. In other words, forecasting errors need to be minimized, so production managers plan market negligence and minimize costs. Demand forecasting methods can be based on mathematical models that use historical data or on qualitative methods, planned according to administrative experience and customer reviews.

• Keywords: lanac opskrbe; proizvodnja; planiranje i kontrola; predviđanje
• Keywords (english): supply chain; production; planning and control; forecasting
• Language: croatian
• URN:NBN: urn:nbn:hr:119:960175
• Study programme: Title: Intelligent Transport Systems and Logistics; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka prometa (magistar/magistra inženjer/inženjerka prometa)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2021-10-29 11:02:48