Production Location Selecting and Subsequent Production Decision Making for Sport Obermeyer

Research Article
Open access

Production Location Selecting and Subsequent Production Decision Making for Sport Obermeyer

Published on 10 June 2024 | https://doi.org/10.54254/2754-1169/108/20241918
Cheng Cui *,1
  • 1 Art & Science, The University of Washington, Seattle, 98105, America    

* Author to whom correspondence should be addressed.

View PDF
Cui,C. (2024).Production Location Selecting and Subsequent Production Decision Making for Sport Obermeyer.Advances in Economics, Management and Political Sciences,108,44-51.
Export citation
AEMPS Vol.108
ISSN (Print): 2754-1177
ISBN (Print): 978-1-83558-545-0
ISSN (Online): 2754-1169
ISBN (Online): 978-1-83558-546-7
Download Cover Download Volume

Abstract

This paper revolved around two core research issues: production location selection and subsequent production decisions. The aim of the study was to provide a detailed analysis of Sport Obermeyer's data from 1992 to 1995 in a new way, hoping to assist companies facing similar challenges. The paper took Cost, Quality, Lead time and Minimum order quantity as bridges to the four main factors affecting the choice of production location. It detailed the advantages and disadvantages of the two production locations: Hong Kong and China. Then, with the aid of the Newsvendor model, the mismatch cost ratio for the 10 styles was determined. Ultimately, using the coefficient of variance for comparison and confirmation of the mismatch cost ratio results, the paper helped Sport Obermeyer analyze which styles of parkas had low-risk and low-uncertainty demand, and which had high-risk and high-uncertainty. The paper then combined the characteristics of the two production locations and assisted Sport Obermeyer in making subsequent production decisions. Five styles of parkas suitable for production in China and five styles suitable for production in Hong Kong were identified, ensuring stable profits and deliveries for Sport Obermeyer.

Keywords

Production location, mismatch cost ratio, newsvendor model

1 Introduction

The snow-capped peaks of Aspen, Colorado, aren't just home to skiers and snowboarders eager to carve fresh tracks. They were also the birthplace of Sport Obermeyer, a trailblazing skiwear company founded in 1947 by Klaus Obermeyer. During the 1990s, globalization has unlocked doors to a multitude of manufacturing landscapes. As companies grapple with the question of where to produce, quality, cost, and speed become the critical trifecta guiding decisions. As for Sport Obermeyer, the challenge was even greater. The nature of fashion, combined with the unpredictability of weather patterns, meant that demand was incredibly hard to forecast. Produce too much, and you risk unsold inventory. Produce too little, and you miss out on potential sales. The stakes were high, and a misstep could cost the company its reputation and revenue. The way to navigate this intricate dance of supply and demand. The way to choose their production locations amidst a plethora of options, and to make the crucial subsequent production decisions that ensured products were on the shelves just when consumers wanted them.

Having posed these critical research questions, it becomes essential to contextualize them within the broader scholarly discourse. The decision-making process around production location selection and subsequent operational choices has long intrigued researchers, strategists, and business leaders alike. To understand the nuances and underpinnings of these decisions, people must delve into the existing body of literature that has shaped current perspectives and practices. There are many researchers who have analyzed many cases or have adopted new algorithms or decision-making methods to determine the selection of production sites or make subsequent production decisions. Darvish and Coelho analyzed a system that encompasses both production and distribution and suggested a series of step-by-step methods and a metaheuristic to compare solution costs obtained from their two methods [1]. Ketokivi and Turkulainen conducted a thorough analysis of 35 decisions regarding the location of final assembly by investigating the crucial connections among production, supply chain, product development and market. This was done to comprehend the factors influencing the selection of a manufacturing site from both strategic and economic policy viewpoints, particularly in a setting characterized by high GDP per capita [2]. Buciuni and Finotto analyzed through multiple case studies, focusing on the continuity between the development activities of production sites and production and found that the implementation of a specific set of development tasks relies on specialized knowledge in manufacturing, which is central to the judgment of production location selection [3]. Shahabi and Tafreshian investigated the challenge associated with production, inventory, and location with interrelated demand and devised an approach relying on the external estimation of the non-linear components to tackle the issue [4]. Yu and Normasari proposed a comprehensive strategy for designing the supply chain network and developed a mathematical model geared towards minimizing the overall cost of the supply chain, emphasizing the selection of suitable locations for new plants and distribution centers, while determining the production and distribution of the product [5, 6]. Bhatnagar and Lin applied a Markov decision process model to the transshipment issue and defined the desirable strategy for a two-location scenario and lost-sales model [7, 8]. Shafiee-Gol and Kia formulated a mixed-integer nonlinear programming model to deal with the location-distribution and production planning issues, across multiple plants under dynamic conditions [9, 10]. Sharkey and Geunes presented exact branch‐and‐price algorithms for a category of facility location issues with a temporal dimension and several key variations [11].

Many researchers have also proposed new conditions for investigating production location. Fuchs highlighted the importance of location-specific variations in production attributes and in consumer demand for technological competitiveness [6]. Some researchers have even proposed new production site options and production methods that are responsive to the times. Treber and Moser proposed a methodology that is practical and application-oriented for redistributing production technologies across manufacturing locations in worldwide production networks [8].

Motivated by a real case, this paper will go back to 1992-1995 with the help of Sport Obermeyer's data at the time to use an innovative method with a focus on combining traditional analysis with innovative metrics such as mismatch cost ratio and coefficient of variation to provide a novel, multidimensional approach to problems of production location selection and following production decisions, offering a blueprint for effective supply chain management to unearth invaluable insights on balancing demand-supply dynamics, choosing optimal production locations, and predicting market needs.

2 Methods

2.1 Data Source

The data for this literature are collected from the Sport Obermeyer website, which is provided by actual operations of Sport Obermeyer, and from the classic case study of the Sport Obermeyer. All data are from 1992 to 1995.

2.2 Variable Selection

The data utilized for this paper mainly consists of two parts. The first part (see Table 1 below) includes five variables (Styles, Price, Name of people who participate in forecasts, Average Forecasts, Twice the Standard deviation). The second part (see Table 2 below) includes 12 variables (Production Location, Wage per hour, Exchange Rate, Hours Worked, Weekly Output per worker, Actual Work Effort per parka, Compensated Work Duration per parka, Cost of Labor per parka, Production Line, Training, Repair Rate, Minimum Order Quantity).

Table 1: Committee’s Forecasts.

Style

Price

Laura

Carolyn

Greg

Wendy

Tom

Wally

\( μ \)

Gail

$110

900

1000

900

1300

800

1200

1017

Isis

$99

800

700

1000

1600

950

1200

1042

Entice

$80

1200

1600

1500

1550

950

1350

1358

Assault

$90

2500

1900

2700

2450

2800

2800

2525

Teri

$123

800

900

1000

1100

950

1850

1100

Electra

$173

2500

1900

1900

2800

1800

2000

2150

Stephanie

$133

600

900

1000

1100

950

2125

1113

Seduced

$73

4600

4300

3900

4000

4300

3000

4017

Anita

$93

4400

3300

3500

1500

4200

2875

3296

Daphne

$148

1700

3500

2600

2600

2300

1600

2383

Totals

-

20000

20000

20000

20000

20000

20000

20000

The reason only five people participated in Forecast is that in 1992, Wally Obermeyer, the Vice president of the Sport, modified the company's standard procedure where the committee would make production commitments based on the collective agreement of the group. Instead, in an effort to obtain more comprehensive data, Wally instructed each committee member to independently project the retailer demand for every Sport Obermeyer product, as indicated in Table 1.

Table 2: Comparison of operations between Hong Kong and China.

Topic

Hong Kong

China

Exchange Rate

HK$7.8 = US$1

RMB 5.7 = US$1

Wage per hour

HK$30

RMB 0.91

Hours Worked

48 hours per week

58.5 hours per week

Weekly Output per worker

19 parkas

12 parkas

Actual Work Effort/parka

~2.36 hours

~3.7 hours

Compensated

work duration per parka

~2.54 hours/parka

~4.89 hours/parka

Cost of Labor per parka

HK$75.7

RMB4.46

Training

Trained in multiple areas

Trained for single task

Minimum Order Quantity

600 units

1200 units

Repair Rate

1-2 %

~10 %

Production Line

10-13 people/line

40 people/line

Table 1 showed the 10 styles of Women's Parkas and the six committee members' predictions of the demand for these 10 styles of Women's Parkas. Considering the Balance Between Precision and Reliability, this paper chooses to use Twice the standard deviation, which is 95% confidence interval.

Because all data are from 1992-1995, before Hong Kong was returned to China, China is used here instead of mainland. Table 2 showed the specific comparison between two production locations (Hong Kong and China) from 1992 to 1995.

2.3 Research Protocol

This paper will use the Newsvendor Model combined with Normal Demand Distribution to find the quantity of maximum profit. Combined with Loss Function, Expected sales and Expected leftover inventory, Expected profit can be obtained, and mismatch cost ratio can be obtained by combining maximum profit. Finally, combined with the coefficient of variance for double check and comparison, the subsequent production decision was obtained.

3 Results and Discussion

3.1 Comparison of Two Production Locations

Figure 1 showed the four main factors affecting the selection of production locations. Because the data were from 1992-1995, China was used here instead of Mainland.

Figure 1: Main factors of comparison.

First, regarding Quality & Skills, at the time of the case, the training for Hong Kong workers and Chinese mainland workers was completely different. Workers in Hong Kong were usually trained in multiple areas, encompassing a wider variety of responsibilities. In contrast, Chinese mainland workers were trained for single operations only. On average, Hong Kong workers operated approximately 50% more efficiently than workers in China and offered greater flexibility in production. Additionally, since Hong Kong workers generally had higher technology proficiency and better repair rate control than Chinese mainland workers (1-2% vs. ~10%), the quality of the products produced was generally superior. In conclusion, Hong Kong was perceived to possess a skilled workforce and superior quality control.

Second, concerning Lead Time, lead time, within a supply chain context, refers to the duration between placing an order (or initiating production) and when the finished goods are ready for shipment or delivery. Hence, lead time and productivity are intrinsically linked (assuming all other external factors remain constant). By comparing the productivity of workers in Hong Kong with those in the Chinese mainland from 1992 to 1995, it's evident that due to the higher skill proficiency of the Hong Kong workers-evidenced by weekly output per worker (19 parkas vs 12 parkas) and actual work effort per parka (~2.36 hours vs ~3.7 hours)-Hong Kong workers held a clear advantage. Furthermore, China had longer production lines (40 people/line vs 10-13 people/line). A longer production line typically translates to a longer duration to complete a product. This increases the overall lead time. The prolonged lead time in China implies that production decisions must be made well in advance, with less demand information available. This situation makes China less suitable for items with unpredictable demand.

Third, concerning cost, Table 2 and Figure 1 reveal that the wages for Hong Kong workers (HK$30) were higher than those of Chinese mainland workers (RMB 0.91). Moreover, the cost of labor for each parka was notably greater for Hong Kong workers (HK $75.7) compared to Chinese workers (RMB 4.46). Since both regions paid workers on a piece-rate basis, Chinese workers generally earned lower wages and incurred lower overtime costs. Thus, in terms of cost, China held an advantage over Hong Kong. The elevated production costs in HK suggested that producing large quantities there wasn't economical. Conversely, lower production costs made China the ideal location for bulk production. For items with high uncertainty, the trade-off between cost and the ability to respond swiftly to changing demand justified production in HK. For predictably demanded items, China offered significant cost savings for Obermeyer.

Fourth, regarding the minimum order quantity, Hong Kong had a lower threshold (600 units of the same style vs 1200 units of the same style). This was advantageous for high-risk items, as Obermeyer might not have wanted to commit to vast quantities without a clearer demand forecast. China, with its higher minimum order quantities, was less suitable for speculative items but was more fitting for items with stable demand.

In conclusion, with its flexibility, shorter lead time, and skilled labor, Hong Kong was ideal for items with uncertain and high-risk demand. In contrast, China, with its cost-efficiency, large-scale production capability, and extended lead time, emerged as the preferred choice for items with predictable and low-risk demand.

3.2 Making Following Production Decision

Due to uncertain demand, a single period, and other conditions, the paper initially used the Newsvendor Model to determine the probability that demand would be less than or equal to a specific quantity. This was done because profit is maximized in this scenario, leading to the identification of the critical ratio.

\( Critical Ratio=\frac{Cu}{Cu+Co}=\frac{$27}{$27+$9 } =0.75, \) (1)

where \( Cu \) is underage cost, \( Co \) is the overage cost According to the Central Limit Theorem, this paper assumed a normal distribution and used the inverse normal to determine the z-score (0.6745) corresponding to the percentile of the critical ratio. Using the z-score formula, the paper calculated the corresponding x, which yielded the quantity for maximum profit.

\( z=\frac{x-μ}{σ} \) (2)

Table 3 showed the calculation process of maximum profit of these 10 styles of parkas. The next step is to calculate the mismatch cost ratio to determine which products are high uncertainty (high risk) and which products are low uncertainty (low risk). In order to find the mismatch cost ratio, this paper need to first find the Expected sales and Expected leftover to find the Expected profit.

Table 3: Max-profit quantity.

Style

Average Forecasts

Standard Deviation

Max-profit Quantity

Gail

1017

388

1278.702

Isis

1042

646

1477.72

Entice

1358

496

1692.547

Assault

2525

680

2983.653

Teri

1100

762

1613.961

Electra

2150

807

2694.313

Stephanie

1113

1048

1819.865

Seduced

4017

1113

4767.707

Anita

3296

2094

4708.382

Daphne

2383

1394

3323.239

Totals

-

-

26360.089

\( Expected profit=(Price-Cost)×sales-(Cost-Salvage value)×leftover \) (3)

\( Expected sales=Expected (Mean) demand-Expected shortage \) (4)

\( Expected shortage=L(z) ×Standard deviation \) (5)

\( Expected leftover=Quantity-Expected sales \) (6)

Table 4 showed the calculation process of the Expected profit of these 10 styles of parkas by calculating Expected sales, Expected shortage and Expected leftover.

Table 4: Expected profit.

Style

Expected shortage

Expected sales

Expected leftover

Expected profit

Gail

57.8896

959.1104

319.5916

244.08

Isis

96.3832

945.6168

532.1036

250.08

Entice

74.0032

1283.997

408.5501

325.92

Assault

101.456

2423.544

560.1090

606

Teri

113.6904

986.3096

627.6516

264

Electra

120.4044

2029.596

664.7176

516

Stephanie

156.3616

956.6384

863.2269

267.12

Seduced

166.0596

3850.940

916.7667

964.08

Anita

312.4248

2983.575

1724.806

791.04

Daphne

207.9848

2715.015

1148.224

571.92

To obtain the Expected Shortage, L(z), the loss function, is needed. This paper obtained L(z) (0.1492) using the following function:

\( NORMDIST(z,0,1,0)-z×(1-NORMDIST(z,0,1,1) ) \) (7)

Then, this paper will find the maximum profit, and combine with quantity to find the mismatch cost ratio corresponding to these 10 styles.

\( Maximum profit=(Price-Cost)× μ \) (8)

\( Mismatch cost=Maximum Profit-Expected Profit \) (9)

\( Mismatch ratio=Mismatch cost ÷Quantity \) (10)

Table 5: Mismatch cost ratio.

Style

Expected demand

Maximum profit

Mismatch Cost

Mismatch Cost Ratio

Gail

1017

244.08

39.4608

0.03880121

Isis

1042

250.08

65.7003

0.06305207

Entice

1358

325.92

50.4448

0.03714638

Assault

2525

606

69.1582

0.02738937

Teri

1100

264

77.4978

0.07045257

Electra

2150

516

82.0745

0.03817417

Stephanie

1113

267.12

106.5849

0.09576364

Seduced

4017

964.08

113.1956

0.02817915

Anita

3296

791.04

212.9665

0.06461361

Daphne

2383

571.92

141.7742

0.05949401

Table 5 showed the calculation process of mismatch cost ratio of these 10 styles of parkas. According to the Table 5, this paper took mismatch cost ratio = 0.05 as the boundary. Mismatch cost ratios higher than 0.05 were considered high risk and high uncertainty, while those lower than 0.05 were deemed low risk and low uncertainty. This paper then performed the alignment using the coefficient of variance. The coefficient of variance provided a relative measure of variability with respect to the mean. A higher coefficient of variance indicated greater variability, which could be interpreted as higher uncertainty in demand. In the context of cloth production, it offered an understanding of how stable or predictable the demand was for a particular product. By combining the two, products with a high coefficient of variance and high mismatch cost ratio were the riskiest. They had uncertain demand, and any forecasting error could have been costly.

\( Coefficient of Variance=(\frac{Standard deviation}{Mean}*100)% \) (11)

Table 6: Coefficient of variance and mismatch cost ratio.

Style

Standard deviation

Coefficient

of Variance

Mismatch

Cost Ratio

Gail

388

0.3815

0.0388

Isis

646

0.6199

0.0630

Entice

496

0.3652

0.0371

Assault

680

0.2693

0.0273

Teri

762

0.6927

0.0704

Electra

807

0.3753

0.0381

Stephanie

1048

0.9415

0.0957

Seduced

1113

0.2770

0.0281

Anita

2094

0.6353

0.0646

Daphne

1394

0.5849

0.0594

Table 6 compared and confirmed the coefficient of the variance and the mismatch cost ratio of the demand of these 10 styles of parkas. As could be seen from the Table 6, the five styles with a Mismatch cost ratio lower than 0.05 were also the five styles with a lower Coefficient of Variance. Therefore, when combined with the characteristics of the two production locations of Hong Kong and China, this paper placed these five styles with low Mismatch cost ratio and Coefficient of variance (Gail, Entice, Assault, Electra, Seduced) in China for production. This paper assigned the other five styles (Isis, Teri, Stephanie, Anita, Daphne) with high Mismatch cost ratio and Coefficient of variance to Hong Kong for production.

4 Conclusion

In conclusion, by analyzing and comparing Hong Kong and China from the perspectives of cost, lead time, quality, and minimum order quantity, this paper concluded that Hong Kong, with its better flexibility and shorter lead time, was more suitable for production with high risk and high uncertainty demand. On the other hand, China, benefiting from lower costs and larger minimum order quantities, was more suitable for production with low risk and low uncertainty demand. Using the Newsvendor model, the mismatch cost ratio and coefficient of variance were compared and verified, with a mismatch cost ratio of 0.05 set as the boundary. Ultimately, 5 styles suitable for production in China and 5 styles suitable for production in Hong Kong were identified. As algorithms continue to progress, there will be increasingly efficient ways to assist enterprises in making production decisions in the future.


References

[1]. Darvish, M. and Coelho, L. C. (2018). Sequential versus integrated optimization: Production, location, inventory control, and distribution. European Journal of Operational Research, 268(1), 203–214.

[2]. Ketokivi, M., Turkulainen, V., Seppälä, T., Rouvinen, P. and Ali-Yrkkö, J. (2017). Why locate manufacturing in a high-cost country? A case study of 35 production location decisions. Journal of Operations Management, 49-51(1), 20–30.

[3]. Buciuni, G. and Finotto, V. (2016). Innovation in Global Value Chains: Co-location of Production and Development in Italian Low-Tech Industries. Regional Studies, 50(12), 2010–2023.

[4]. Shahabi, M., Tafreshian, A., Unnikrishnan, A. and Boyles, S. D. (2018). Joint production–inventory–location problem with multi-variate normal demand. Transportation Research Part B: Methodological, 110(April 2018), 60–78.

[5]. Yu, V. F., Normasari, N. M. E. and Luong, H. T. (2015). Integrated Location-Production-Distribution Planning in a Multiproducts Supply Chain Network Design Model. Mathematical Problems in Engineering, 2015(16 Mar 2015), 1–13.

[6]. Fuchs, E. R. H., Field, F. R., Roth, R. and Kirchain, R. E. (2011). Plastic cars in China? The significance of production location over markets for technology competitiveness in the United States versus the People’s Republic of China. International Journal of Production Economics, 132(1), 79–92.

[7]. Bhatnagar, R. and Lin, B. (2019). The joint transshipment and production control policies for multi-location production/inventory systems. European Journal of Operational Research, 275(3), 957–970.

[8]. Treber, S., Moser, E., Helming, S., Haefner, B. and Lanza, G. (2019). Practice-oriented methodology for reallocating production technologies to production locations in global production networks. Production Engineering, 13(3-4), 283–291.

[9]. Shafiee-Gol, S., Kia, R., Kazemi, M., Tavakkoli-Moghaddam, R. and Mostafayi Darmian, S. (2020). A mathematical model to design dynamic cellular manufacturing systems in multiple plants with production planning and location–allocation decisions. Soft Computing, 25(5), 3931–3954.

[10]. Bruch, J., Wiktorsson, M. and Bellgran, M. (2014). On the production location decision: a case study on process and criteria. International Journal of Manufacturing Research, 9(1), 74.

[11]. Sharkey, T. C., Geunes, J., Edwin Romeijn, H. and Shen, Z.-J. M. (2011). Exact algorithms for integrated facility location and production planning problems. Naval Research Logistics (NRL), 58(5), 419–436.


Cite this article

Cui,C. (2024).Production Location Selecting and Subsequent Production Decision Making for Sport Obermeyer.Advances in Economics, Management and Political Sciences,108,44-51.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content

About volume

Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis

ISBN:978-1-83558-545-0(Print) / 978-1-83558-546-7(Online)
Editor:Ursula Faura-Martínez
Conference website: https://2024.icftba.org/
Conference date: 4 December 2024
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.108
ISSN:2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Darvish, M. and Coelho, L. C. (2018). Sequential versus integrated optimization: Production, location, inventory control, and distribution. European Journal of Operational Research, 268(1), 203–214.

[2]. Ketokivi, M., Turkulainen, V., Seppälä, T., Rouvinen, P. and Ali-Yrkkö, J. (2017). Why locate manufacturing in a high-cost country? A case study of 35 production location decisions. Journal of Operations Management, 49-51(1), 20–30.

[3]. Buciuni, G. and Finotto, V. (2016). Innovation in Global Value Chains: Co-location of Production and Development in Italian Low-Tech Industries. Regional Studies, 50(12), 2010–2023.

[4]. Shahabi, M., Tafreshian, A., Unnikrishnan, A. and Boyles, S. D. (2018). Joint production–inventory–location problem with multi-variate normal demand. Transportation Research Part B: Methodological, 110(April 2018), 60–78.

[5]. Yu, V. F., Normasari, N. M. E. and Luong, H. T. (2015). Integrated Location-Production-Distribution Planning in a Multiproducts Supply Chain Network Design Model. Mathematical Problems in Engineering, 2015(16 Mar 2015), 1–13.

[6]. Fuchs, E. R. H., Field, F. R., Roth, R. and Kirchain, R. E. (2011). Plastic cars in China? The significance of production location over markets for technology competitiveness in the United States versus the People’s Republic of China. International Journal of Production Economics, 132(1), 79–92.

[7]. Bhatnagar, R. and Lin, B. (2019). The joint transshipment and production control policies for multi-location production/inventory systems. European Journal of Operational Research, 275(3), 957–970.

[8]. Treber, S., Moser, E., Helming, S., Haefner, B. and Lanza, G. (2019). Practice-oriented methodology for reallocating production technologies to production locations in global production networks. Production Engineering, 13(3-4), 283–291.

[9]. Shafiee-Gol, S., Kia, R., Kazemi, M., Tavakkoli-Moghaddam, R. and Mostafayi Darmian, S. (2020). A mathematical model to design dynamic cellular manufacturing systems in multiple plants with production planning and location–allocation decisions. Soft Computing, 25(5), 3931–3954.

[10]. Bruch, J., Wiktorsson, M. and Bellgran, M. (2014). On the production location decision: a case study on process and criteria. International Journal of Manufacturing Research, 9(1), 74.

[11]. Sharkey, T. C., Geunes, J., Edwin Romeijn, H. and Shen, Z.-J. M. (2011). Exact algorithms for integrated facility location and production planning problems. Naval Research Logistics (NRL), 58(5), 419–436.