Improving the delivery performance: a case study applying statistical approaches for brasilian fast food

作者
Cleginaldo Pereira de Carvalho,Marcela Aparecida Guerreiro Machado,Herlandí de Souza Andrade,Fabrício Maciel Gomes,Nilo Antônio de Souza Sampaio,Ícaro da Silva Vieira
出处
期刊:GeSec [Sindicato das Secretárias do Estado de São Paulo]
卷期号:14 (12): 22069-22099 被引量:1
标识
DOI:10.7769/gesec.v14i12.3298
摘要

Purpose - Brazilian demand for a faster way of food feeding is growing and delays in delivery time impair customer satisfaction. Although food delivery apps are popular, the work aims to find the root causes of delays at a popular pizzeria in the Brazilian State of São Paulo, highlighting that the pizza manufacturing processes are the same as in past years. Design/methodology/approach - An anonymous survey conducted with the customers showed the key points of improvement for pizzerias in the city where the pizzeria was located. Data was collected from the pizzeria in question, so its non-compliances could be identified and further investigated using statistical analysis. Findings - The results showed a strong correlation between delivery time and the hour of order. Deliveries were faster on Monday compared to the other days of the week, possibly due to a reduced number of orders on this day, but it doesn’t explain the fact that Tuesday, Wednesday, and Thursday, which also had low numbers of orders, had high delivery times. This research work intends also to show that the process of the coming order is standard by apps, but the pizza manufacturing process is not. It means the concept of service quality is not reached during a period. Originality/value - This work has shown that applying quality tools and statistical analysis can bring advantages even for small food service companies. Other establishments that suffer from similar problems could also use the strategies for solving the delays.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
null的应助被yalin采纳,获得10
刚刚
敏感凡之完成签到 ,获得积分10
刚刚
呵呵发布了新的文献求助150
刚刚
hkky发布了新的文献求助10
2秒前
asdcsdb完成签到,获得积分10
3秒前
夜夜夜发布了新的文献求助10
3秒前
书意完成签到 ,获得积分10
3秒前
核桃发布了新的文献求助10
3秒前
易翼完成签到,获得积分10
4秒前
汉堡包的应助被seanx采纳,获得10
4秒前
聪明纲完成签到 ,获得积分10
5秒前
5秒前
7秒前
正直凡灵完成签到,获得积分10
7秒前
追寻完成签到,获得积分10
7秒前
年123完成签到 ,获得积分10
8秒前
刘刘完成签到,获得积分20
9秒前
爱听歌的听云完成签到,获得积分10
9秒前
10秒前
YH完成签到,获得积分20
10秒前
正直凡灵发布了新的文献求助10
10秒前
冷酷的松思完成签到,获得积分10
10秒前
hkky完成签到,获得积分10
10秒前
wu完成签到,获得积分10
11秒前
wannnnn完成签到,获得积分10
11秒前
Richin发布了新的文献求助10
12秒前
xiaoxiong完成签到,获得积分20
12秒前
老迟到的巧曼完成签到,获得积分20
12秒前
Jacob完成签到,获得积分10
13秒前
14秒前
369ninja的应助被小跳鹅采纳,获得10
14秒前
null的应助被lyz采纳,获得10
14秒前
14秒前
15秒前
袁咏琳冲冲冲完成签到,获得积分10
16秒前
白沙湾发布了新的文献求助10
17秒前
18秒前
19秒前
丘比特的应助被自然卷卷卷采纳,获得30
19秒前
19秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
拉伸高分子熔体中缠结行为的模拟和小角中子散射研究 1080
Encyclopedia of Modern Artificial Intelligence 1000
Особенности технологий и методов информационно-психологических войн СССР с Великобританией и США в период 1939-1953 гг 1000
Analyses de reconnaissances de dettes passées devant les échevins d'Ypres (1249-1291) 600
Cellulose and Cellulose Derivatives: Synthesis, Modification and Applications 600
Experimentelle und numerische Modellierung des dynamischen Trag- und Verformungsverhaltens von hochflexiblen Schutzsystemen gegen Steinschlag 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8147995
求助须知:如何正确求助?哪些是违规求助? 9545862
关于积分的说明 21671772
捐赠科研通 7734989
什么是DOI,文献DOI怎么找? 3423733
关于科研通互助平台的介绍 2548672
邀请新用户注册赠送积分活动 2466680