2026-09-21T10:47:25Zhttps://dora.dmu.ac.uk/server/oai/request

oai:dora.dmu.ac.uk:2086/122642019-03-20T04:14:03Zcom_2086_2388col_2086_2389

Investigating Different Types of Variability in Food Production System Noorwali, Ammar A high level of competition in the food industry, specifically in the Middle East and the UK has forced companies to improve their processes by reducing lead time, waste, and costs and increasing production efficiency. The main challenge to the achievement of the process improvement objectives is the high level of process variability. Therefore, this research investigates the different types of variability in food production system and proposes a methodology to reduce the effect variability in food production system. The variability can be caused by several factors, for instance, in biscuit production lines variability can be induced due to short breakdown and long breakdown, variable processing times, variable temperature, etc. The proposed approach addresses process time variability issues associated with both make-to-stock (MTS) and make-to-order (MTO) manufacturing environments using an iterated approach. The proposed methodology integrates process mapping, (which is a lean tool for identifying value added and non-value added activities), discrete event simulation (to mirror the real production line), Taguchi orthogonal arrays (to generate different scenarios in order to investigate the effect of variability on the simulation model), correlation analysis (to identify the highest variability factors), and the rule based system (to improve food production system performance based on identified key performance indicators (KPIs)). The research uses a biscuit production line as a case study to validate the proposed methodology. The application of the proposed approach determines that the highest effected KPI is %working. The results showed that after implementation of the rule-based system, key performance improved in high variable areas. Results analysis based on before scenario shows that %working performance indicator is highly effected by variable temperature, speed, and breakdown factors for high variable areas such as baking, cooling, aligning, and packing. Based on identified factors and high variable areas, rules are developed by applying standardisation setting (SOP, WI, PP) in high variable areas and the results shows %working improved in baking by 4.78%, in cooling by 16.06%, in aligning by 0.35%, in packing machine1 by 2.5%, in packing machine2 by 2.37%, in packaging1 by 3.35%, and in packaging2 by 3.16%. The integrated method allow quick response , control the environment without production interruption, reduce number of experiments , and reducing variability in high variable areas, which narrowed the improvement in the required areas and increased its effectiveness. De Montfort University 2016-03 Thesis or dissertation Doctoral PhD en http://hdl.handle.net/2086/12264 https://dora.dmu.ac.uk/bitstreams/daa0d57a-2557-434f-b965-7941e78015f7/download 8481405ffa7ec3ef568f5c7082865d46 https://dora.dmu.ac.uk/bitstreams/d10a98fc-37d2-434e-85e5-b8c8823fecf0/download 78074e3b8a5534add636297b434f123a https://dora.dmu.ac.uk/bitstreams/50dc64b9-9dde-48cf-9622-2810942ee854/download ccd3d88790a16ec8b64fbd9011a1a712 Production variability Make-to-order(MTO) Make-to-stock (MTS) Lean Taguchi Rule-based food waste efficiency process improvement Performance KPIs six sigma Faculty of Technology