International Journal of Lean Six Sigma Using lean manufacturing as service quality benchmark evaluation measure Abdelhakim Abdelhadi

Article information: To cite this document: Abdelhakim Abdelhadi , (2016),"Using lean manufacturing as service quality benchmark evaluation measure", International Journal of Lean Six Sigma, Vol. 7 Iss 1 pp. Permanent link to this document: http://dx.doi.org/10.1108/IJLSS-02-2015-0003 Downloaded on: 09 February 2016, At: 08:53 (PT) References: this document contains references to 0 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 16 times since 2016*

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Using lean manufacturing as service quality benchmark evaluation measure

Introduction The fast-food business sector is growing dramatically compared with traditional types of restaurant as a result of several factors, such as the changing lifestyle of populations across the globe. Hence, fast-food marketing strategies should have a clear understanding of customers’ preferences and perceptions of the quality of services provided. Bell (1974) suggested 11 attributes that could be used to reveal customers’ perception of fast-food restaurants: price, Downloaded by La Trobe University At 08:53 09 February 2016 (PT)

friendliness of personnel, variety of menu, service speed, calorie content of food, cleanness, convenience, business hours, delivery services, novelties for children and seating facilities. Using an efficiency measure, this research targets the fourth item, namely service speed, as an important criterion for customers’ perception of service quality. The proposed efficiency measure provides guidance to fast-food management so that they can improve their services and performance to meet customers’ demands. Improving service quality will increase a restaurant’s attractiveness to the public. We examine the relative efficiency of three fast-food providers using a lean metric called Takt time. This lean manufacturing measure is applied to a regional shopping mall in the capital city of Riyadh in the Kingdom of Saudi Arabia. Increasing service efficiency is a key component for improving service quality and sustainability. Therefore, the lean manufacturing metric is implemented to identify the bottleneck areas generating the most waste and to improve the flow of customers, thereby increasing the overall service quality. We focus on implementing this lean measure to compare service duration efficiencies at the three mentioned fast-food restaurants.

Efficiency Several efficiency measures have been used in different industries. Data envelope analysis is an analytical procedure developed by Charnes et al. (1978) for measuring relative efficiency in decision-making units (DMUs) that perform similar functions and have identical goals and objectives. Lo and Lu (2006) used two-stage data envelope analysis to measure the efficiency of financial holding companies in Taiwan. Wu et al. (2006) integrated neural networks (NNs) and data envelope analysis to find the relative efficiency of in banking industries bank. Decisionmaking units include departments, sections and organizations belong to the same business sector. 1

Data envelope analysis is not the only method for determining relative efficiency; multiple regression can also be used to do this by modelling output levels as a function of various input levels. Some investigators have combined data envelope analysis and regression analysis to evaluate operating units that have multiple inputs and outputs (Friedman and Sinuany-Stern, 1998). However, in the new approach proposed in this paper, a lean manufacturing metric called Takt is used as a relative efficiency benchmark measure. Benchmarking can be classified as internal or external: internal benchmarking is used to identify best practice within an organization, whereas external benchmarking compares key process best practices between Downloaded by La Trobe University At 08:53 09 February 2016 (PT)

different organizations. The approach proposed here evaluates external benchmarking and compares service durations between the three mentioned fast-food restaurants.

Lean manufacturing The term ‘lean manufacturing’ was first coined by Womack et al. (1990) in The Machine that Changed the World. Lean production is rooted in the Toyota Production System, which turned the Toyota Motor Corporation from a small domestic producer in the 1950s into one of the world’s leading automotive companies (Zokai and Simons, 2006). One aim of lean production is the elimination of waste within the firm and its supply chain (McKone and Schroeder, 2001). Ohno (1988) identifies seven types of waste, due respectively to overproduction

or

underproduction , waiting inventories, unnecessary transport, waiting times, unnecessary motion (people), unnecessary processes and defective products (Zokaei and Simons, 2006). Overproduction results from producing more, sooner or faster than is required by the next process downstream, whereas underproduction occurs when the production rate is not high enough (Rother and Shook, 1998). Overproduction means that resources are tied up in stock rather than being directly devoted to production. While many researchers and practitioners have studied and commented on lean manufacturing, it is very difficult to find a concise definition of ‘lean’ on which there is universal agreement. Womack et al. (1990) define lean as a dynamic process of change driven by a systematic set of principles and best practices aimed at continuous improvement, with lean manufacture combining the best features of both mass and craft production. Liker (1996) defines lean as a philosophy that when implemented reduces the time from customer order to 2

delivery by eliminating waste in the production flow. Worley (2004) defines lean as the systematically removal of waste throughout an organization from all areas of the value stream. Hallgren and Olhager (2009) define lean manufacturing as a programme aimed at increasing the efficiency of operations throughout an organization. Alves et al. (2012) define lean production as a model where workers assume the role of thinkers and their involvement promotes continuous improvement. Lean manufacturing is most frequently associated with eliminating the seven important wastes identified by Ohno (1988) to reduce variability in supply, processing time or demand (Abdulmalek and Rajgopal, 2007). Åhlström (1998) define lean manufacturing Downloaded by La Trobe University At 08:53 09 February 2016 (PT)

as a manufacturing philosophy that focuses on delivering the highest-quality product on time and at the lowest cost. Herron and Braident (2007) define it as the systematic elimination of waste associated with all members of product families or services that follow common process paths to the consumer (value stream). Briefly, this approach is called lean because it uses lower amounts, or indeed the minimum amounts, of materials required to produce a product or perform a service (Davis, 1993). Lean manufacturing can best be defined as an approach that delivers the greatest value to the customer by eliminating waste through process and human design elements (Lewis, 2001). Lean manufacturing has become an integrated system that includes highly inter-related elements and wide management practices, including just-in-time (JIT), quality systems, work teams, cellular manufacturing, etc. It aims to increase productivity, reduce lead time and cost, and improve quality; in other words, lean production is more than just tools and techniques. Lean thinking forces an organization to focus on real value from the customer’s viewpoint and aligns all processes towards that end. Lean thinking has been broadly accepted in many manufacturing operations and has been applied successfully across many disciplines, including healthcare (Abdelhadi and Shkoor, 2014; Abdelhadi, 2015). Puvanasvaran et al., (2009) show that investment in the infrastructure will lead to more positive results in the application of lean manufacturing. Arumugam et al. (2012) show that the use of a lean tool called ‘utilization observation’ not only speeds up processes in airports, but also helps in identifying the root causes of variations in these processes. Process improvement and better utilization of resources can lead to higher levels of leanness (Chauhan et al., 2009). Al-Aomar (2010) present three types of metrics characterizing the leanness of any system undergoing lean improvements, namely productivity, cycle time and work in process inventory. However, none of these authors 3

use lean manufacturing as a benchmark evaluation in the food industries sector. The research reported here will show how a lean metric called Takt time can be used as an indicator to determine whether a problem is a bottleneck or a starving issue in a service industry, namely fast food restaurants.

Takt time The German word Takt means precise time cycle, rhythm or interval; it also refers to an Downloaded by La Trobe University At 08:53 09 February 2016 (PT)

orchestral conductor’s baton and the beat of music. In the manufacturing context, the term ‘Takt’ was first introduced in the German aircraft industry, where it was used to synchronize production processes with customer demand to prevent overproduction. Takt is the time elapsed between output units when the production rate is synchronized to meet customer demand. According to Ohno (1988), Takt time is an indicator showing when the next production item is required. For example, if the calculated Takt time for a production system is 10 minutes, this means that each part of the process should take only 10 minutes. If four parts take 40 minutes for processing and two take 14 minutes, then the system in imbalanced and the production flow is not smooth. Thus, the Takt time is central to lean production and is calculated by dividing customer demand by available working time per working period. The Takt time is calculated as follows: Takt time =

       

(1)

         

where operational time = production time – breaks

(2)

required production = production volume 

   

(3)

=       

The Takt time can be used for individual units in the entire value stream to adjust production quantities to meet any variations in product demand.

4

Comparing cycle time with Takt time The cycle time is the time taken to produce the final product. It includes value-added and nonvalue-added activities. In the ideal situation, the cycle time is equal to the Takt time. •

If the cycle time equals the Takt time then production is at its intended target.

•

If the cycle time is less than the Takt time then production capacity is underutilized.

•

If the cycle time is more than the Takt time then production bottlenecks lead to poor

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outcomes and an inability to meet demand.

Methodology There are three groups of customers receiving services at fast-food restaurants in this shopping mall; each customer has his or her preferences for buying the type of fast food they wish, using the three designated restaurants. Services are not balanced in the three restaurants; for example, the service time for each order at each restaurant varies, depending on the number of customers in each queue. The numbers of staff in the restaurants are imbalanced and have different training for dealing with order preparation. From the customer’s viewpoint, the overall value is the shortest time he or she can spend in the queue to receive their order. This conclusion emerged from a questionnaire distributed to a random sample of customers during a three-week period, which showed that more than half of customers were unhappy with their length of stay in the queue. Therefore, our main goal is to increase customer satisfaction by studying the lead time, that is, by comparing the relative efficiency between the lengths of stay in the queues at the three fast-food restaurants. To make the comparison, the flow of customers was observed for seven days. Benchmarking using relative efficiency was conducted to measure and compare the length of stay as a key measure.

Data collection and analysis The study was conducted at a shopping mall. Data were collected at three fast-food restaurants, the first belonging to a well-known international chain (labelled as I), the second belonging to a national chain (II), and the third being a locally owned restaurant (III). Designated teams 5

collected the total time each customer spent from arrival time until being served. Data were collected over a period of one week. This duration of data collection was based on advice from the managers of the three restaurants under investigation. According to the managers, the intensity of sales and the pattern of customer arrival showed cyclical behaviour, with a one-week cycle time. The service duration for each customer entering the queue was measured. Table I

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presents the data relating to customer waiting time.

Table I about here Table II shows the duration each restaurant was open to serve customers (only the fast-food restaurant belonging to the international chain (I) opened to serve breakfast). Table II about here

Data analysis Statistical analysis using Minitab statistical software was used to analyse the type of relationship existing between the restaurants with regard to service duration, while the lean metric, Takt time, was used to provide a deeper analysis.

Service duration Table III shows the result of a Kolmogorov–Smirnov test to check if the sample data collected come from a normally distributed population. For 1 – α = 95%, the test indicates that the sample data come from a normally distributed population, because all p values shown are greater than α = 0.05. Analysis of variance (ANOVA) was used to check for significant differences in mean population service time between the three restaurants under consideration. The results indicate that there is no significant difference in population mean among the three restaurants. As shown in Figure I, the three confidence intervals of the population mean overlap within each other’s ranges, 6

which indicates that statistically there is no significant difference in population mean among the average waiting times for customer services. Table III about here

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Figure I about here

Takt time The use of Takt time as a lean metric provides a deeper explanation of the situation. The actual available time/day was observed for each restaurant as shown in Table II. The cycle time and Takt time for each restaurant were calculated. The cycle time was calculated as the total time taken for customers to wait in the queue divided by the total number of customers in the given time period. The Takt time was calculated using equations (1) and (2). The calculations of the cycle and Takt times and the efficiency for the three restaurants are as follows: • Restaurant I: cycle time =

 

= 9.56 min/customer



Takt time =  = 0.155 min/customer   

. / 

EI=    = . /  = 61.6 • Restaurant II: cycle time =

 "#

= 8.94 min/customer



Takt time = "# = 0.344 min/customer   

. / 

EII=    = .# /  = 25.9 • Restaurant III:

7

cycle time = Takt time = EIII=

 

 

= 8.67 min/customer

= 0.69 min/customer

  

." / 

  

. / 

=

= 12.5

Note that the higher the value of E, the lower the quality of service time. The concept of relative efficiency $%& was used to compare the efficiencies between any two '

entities X and Y as follows: $%& = Downloaded by La Trobe University At 08:53 09 February 2016 (PT)

'

(& ('

For example, the relative efficiency between restaurants I and II is (

.

$%)/)) = ( * = . = 2.38 **

This figure implies that restaurant II is 2.38 times more efficient than restaurant I. The relative efficiencies between the restaurants are shown in Table IV. Table IV about here

Research implications and recommendations Using a traditional approach to evaluate the average time a customer spends ordering their food, it is noticed that the average time customers spend at the three facilities are almost equal, but from the Takt time, it is obvious that restaurant I is the least efficient in providing services to customers. It can be concluded from the results of this approach (which has not previously been used in a service sector) that even though the results of a service time comparison among three different entities are close, this does not mean that they have the same capability or same efficiency in providing the service to customers. Customer satisfaction was measured by qualitative criteria according to searches done within the food industry based on the time 8

customers are willing to wait to receive their orders, while it was also measured quantitatively using the Takt time. This research has focused on using lean manufacturing as a tool to assess service quality in a dynamic and challenging area. Even though the reasons why service targets were not met have not been investigated, the results of the study show how Takt time can be used to identify and measure inefficiency. Educating personnel about lean manufacturing will create a culture that allows changes to be made to the way in which services are provided and that will result in continuous improvements in quality.

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Conclusion The aim of this study was to deal with an important issue in facility/resource management within the food industry by identifying the existence of problems and assessing their implications for the quality of services. It has gone beyond the traditional way of comparing lead service time between several systems by taking into consideration a system as a single unit and comparing it with others, rather than by dealing with only part of the system when measuring quality assurance. The root cause of the existing problem has not been investigated, nor have any benchmarking practices to improve service performances been described. Rather, this work has attempted to show the severity of the bottleneck situation that exists, although the research described here could be expanded to reveal the reasons for this. Future work could address the need to apply lean manufacturing rules, such as the use of visual concepts and the redesign of floor settings as a way to reduce wasted time, as part of an approach to continuous improvement. The assessment was carried out using a lean manufacturing metric, Takt time. This metric could be of great value for any type of service provider, since it can reveal customers’ perceptions of service and allows areas with potential for improvement to be highlighted, since customers’ experience is clearly improved if the desired services are provided within a reasonable time.

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Figure I Analysis of variance

Table I, Number of customers and time they spent during the order

Day

Restaurant I

Restaurant II

Number of

Total time

Average

Number of

Total time

Average

Number of

Total time

Average

customers

(min)

time/custom

customers

(min)

time/customer

customers

(min)

time/customer

er (min)

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Restaurant III

(min)

(min)

Sunday

3209

16125

5.02

1309

7200

5.5

420

1298

3.09

Monday

2461

24680

10.03

890

6141

6.9

313

1972

6.3

Tuesday

3247

19256

5.93

1019

9069

8.9

598

4365

7.3

Wednesday

4389

33234

7.57

769

6537

8.5

260

2080

8

Thursday

4231

33785

7.99

848

5936

7

189

1531

8.1

Friday

5720

74278

12.99

921

8381

9.1

358

6193

17.3

Saturday

6239

80483

12.90

1562

22180

14.2

264

3379

12.8

Table II, Total working times for each restaurant during a one-week period (minutes)

Restaurant

Restaurant

Restaurant

II

III

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I

Sunday

660

360

240

Monday

660

360

240

Tuesday

660

360

240

Wednesday

660

360

240

Thursday

660

360

240

Friday

600

360

240

Saturday

660

360

240

Total

4560

2520

1680

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Table III, Kolmogorov–Smirnov normality test Restaurant

Average service duration (min)

Standard deviation

p value

I

8.98

4.66

0.076

II

8.58

2.79

0.086

III

8.91

3.17

More than 0.15

Table IV, Relative efficiencies of designated restaurants

I

II

III

I

X

2.38

4.93

II

X

X

2.07

III

X

X

X