Eindhoven University of Technology

MASTER

Evaluation of inventory strategies through simulation in a aviation industry

Markoulakis, V.

Award date:2018

Link to publication

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Evaluation of inventory strategies throughsimulation in an aviation industry

Author:Vasileios Markoulakis 0980059

Supervisors:Prof.Dr.Tom van Woensel, Tu/e

dr. L.P. Veelenturf, TU/eJ. Kuik, DHL

Master of Science inOperations Management and Logistics

Department of Industrial Engineering and Innovation Sciences

Contents

List of Tables v

List of Figures vii

Abstract ix

Executive Summary x

Preface xiii

1 Introduction 1

1.1 Company Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Product types and figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Problem definition 3

2.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Research Objective and Research Questions . . . . . . . . . . . . . . . . . . 6

2.3 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

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2.4 Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Conceptual Model 12

3.1 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2 Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.3 First Hierarchical Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 Second Hierarchical Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.4.1 Output Buffer sub-model . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.4.2 Plant Sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.4.3 Input Buffer Sub-model . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.4.4 Supplier 1 2 Sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.4.5 Supplier 1 1 Sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.4.6 Supplier 2 1 sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.4.7 Transport sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4 Methodology 20

4.1 Inventory Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2 Safety Stock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.3 Reorder point s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.4 Order-up-to level S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.5 Economic Order Quantity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.6 Total relevant cost function . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.6.1 Ordering Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.6.2 Holding Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5 Case study 27

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5.1 Transportation Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.1.1 Road Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.1.2 Air Freight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.1.3 Ship freight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.2 Model Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.3 Simulation design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.4 Inventory Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.4.1 Base case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.4.2 Strategy 1: 2 week review period with fixed quantities . . . . . . . . 36

5.4.3 Strategy 2: 1 month review period with fixed quantities . . . . . . . . 36

5.4.4 Strategy 3: 1 week review period with order-up-to level . . . . . . . . 36

5.4.5 Strategy 4: 2 week review period with order-up-to level . . . . . . . . 36

5.4.6 Strategy 5: 1 month review period with order-up-to level . . . . . . . 37

5.4.7 Production Ramp-up . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6 Results 38

6.1 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.1.1 Current production rate . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.1.2 Production ramp up . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

6.2 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7 Conclusion and Recommendations 46

7.1 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

7.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7.3 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

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References 50

iv

List of Tables

1 Strategy performance under current production rate over 30 replications . . . xi

2 Strategy performance under production ramp up over 30 replications . . . . xii

2.1 Classification of Inventory Policies . . . . . . . . . . . . . . . . . . . . . . . . 10

4.1 Rule of Thumb for Form Selection of the Inventory Policy . . . . . . . . . . 22

5.1 Door to Door Routine rates . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.2 Quantity Discounts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.3 Master Production Schedule (A320 family) . . . . . . . . . . . . . . . . . . . 30

5.4 Demand arrivals in Broughton . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.5 Bill-of-Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.6 Sub-assemblies bill-of-materials . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.7 Estimated Unit price of items . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.8 Available Transport Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.9 Transportation Concept Assignments . . . . . . . . . . . . . . . . . . . . . . 34

6.1 Strategy performance under current production rate over 30 replications . . . 39

6.2 Fulfilment rates under current production rate over 30 replications . . . . . . 41

6.3 Strategy performance under production ramp up over 30 replications . . . . 42

6.4 Fulfilment rates under production ramp up over 30 replications . . . . . . . . 43

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6.5 Sensitivity analysis on fixed order cost . . . . . . . . . . . . . . . . . . . . . 43

6.6 Sensitivity analysis on inventory holding rate . . . . . . . . . . . . . . . . . . 44

A.1 LTL-FTL schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

vi

List of Figures

2.1 Demand forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Demand share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.3 Current Situation: Single echelon model . . . . . . . . . . . . . . . . . . . . 5

2.4 Optimize Logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.5 Regulative Cycle (van Strien, 1997) . . . . . . . . . . . . . . . . . . . . . . . 7

3.1 Broughton, Airbus System Overview . . . . . . . . . . . . . . . . . . . . . . 12

3.2 First Hierarchical Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.1 Supply chain characteristics for controlled flows . . . . . . . . . . . . . . . . 21

5.1 WIP history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6.1 Average Waiting Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

6.2 Total cost composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

A.1 Output Buffer sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

A.2 Plant sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

A.3 Replenishment sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

A.4 Input Buffer - PoU Replenishment . . . . . . . . . . . . . . . . . . . . . . . . 53

A.5 Inventory Evaluator - Input Buffer . . . . . . . . . . . . . . . . . . . . . . . 54

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A.6 Raw Material Arrival . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

A.7 Supplier 1 2 sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

A.8 Sub-assembly delivery- Supplier 1 1 sub-model . . . . . . . . . . . . . . . . . 55

A.9 Inventory Evaluator- Supplier 1 1 sub-model . . . . . . . . . . . . . . . . . . 55

A.10 Material Ordering nested sub-model . . . . . . . . . . . . . . . . . . . . . . . 55

A.11 Material Arrival- Supplier 1 1 sub-model . . . . . . . . . . . . . . . . . . . . 56

A.12 Supplier 2 1 sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

A.13 Transport sub-model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

A.14 Wing construction drawing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

viii

Abstract

This master thesis concerns the simulation and evaluation of different inventory strategiesin the Airbus’ A320 wing production facility in Broughton, UK. Five alternative strategieswere tested and compared to the baseline strategy under the condition of current productionrate and the scheduled production ramp up. The simulation results show that the total costcan be reduced by up to 53.3% for the current production rate and 57.8% under productionramp up.

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Executive Summary

Airbus has decided to increase the production for its single-aisle aircraft, the A320 family,from 46 to 63 a month by 2019 to cover the increasing demand. Airbus’ A320 family is amarket leader in the single-aisle category.

The scheduled production ramp up can increase cost, have an impact on the time of aircraftdelivery and put pressure on the supply chain. The project is focused on the supply networkof the single- aisle aircraft (A320 family) wing sets in Broughton, United Kingdom. Kuik(2016) detected, the lack of a structured operational planning process between procurementoperations, production and logistics. Nevertheless, research from Airbus across all aircraftprograms and production sites revealed high levels of inventory due to large order quantitiesand high values of safety stock. Based on these findings, we formulated the following researchobjective:

What is the suitable inventory policy and transport concept that deliver minimal cost againstset service performance targets, in the wing production facility of an aviation company ?

A systematic literature review was conducted in order to decide which method is appropriateto answer the research question. As a result of this review, we concluded that simulationmodelling is a suitable tool for analyzing supply chains (Erdem & Sancar, 2006; He & Du,2010; Jain, Workman, Collins, Ervin, & Lathrop, 2001; Ravichandran, 2007; Shang, Tadika-malla, Kirsch, & Brown, 2008). Simulation modelling can facilitate ”what-if” analyses to testseveral strategies and scenarios. The simulation model aims to support managerial decisionmaking in order to optimize the supply chain and preserve low cost on inventory man-agement(He & Du, 2010). Different inventory policies were examined, integrating PeriodicReview Policies and Economic Order Quantity (EOQ).

The built simulation model tests different inventory strategies and evaluates them. Eachstrategy serves as an input into the simulation model. The purpose of an inventory controlstrategy is, in general, to reduce holding and ordering costs while still maintaining satisfac-tory customer service. To evaluate the inventory control strategy, indicators have to be usedto measure the performance of the system. In this study, Cycle time and Total cost wereused.

The baseline strategy is the reference inventory policy of the simulation model. As a baseline

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policy review period of 1 week is tested with fixed ordering quantity of 1 month. Safetystock for 1 month is used and orders are sent to suppliers when the stock is below the 2months demand level. Five alternative strategies were tested. Strategies 1 and 2 keep thesame parameters of the baseline except the review period is set at 2 weeks and 1 month,respectively.

Strategies 3, 4, and 5 concern periodic review policies (R,s,S). To better integrate the trade-off among fixed order, transportation and inventory holding costs , the Economic OrderQuantity (EOQ) is used to determine the order-up-to level S. Strategies 4 and 5 implementthe same type of (R,s,S) policy as strategy 3. The review period differs; 2 weeks and 1 monthfor strategy 4 and 5, respectively.

The performance of the different strategies tested is presented below for the current produc-tion rate.

Table 1: Strategy performance under current production rate over 30 replications

Cycle

Time

(Days)

Finished

Wing

sets

Average

Inventory

Cost

Ordering

Cost

Transpor-

tation

Cost

Total Cost Cost

savings

%

Baseline 34.686 555.37 56.764,453 10,920 856,359.3 57,631,732

Strategy 1 34.899 555.37 52,252,530 10,920 856,359.3 53,119,809 -7.8%

Strategy 2 34.660 555.37 60,320,520 10,920 856,359.3 61,187,799 6.2%

Strategy 3 34.624 555.50 26,039,333 36,680 819,987.1 26,896,000 -53.3%

Strategy 4 34.623 555.43 32,773,612 21,560 777,774.4 33,572,946 -41.7%

Strategy 5 34.623 555.43 53,041,647 21,560 777,774.4 53,840,982 -6.6%

Based on the results, the baseline has the second worst performance after strategy 2, inwhich the same quantities are ordered with 1 month review instead of 1 week. As canbe noted, ordering and transportation costs are the same for the baseline and strategies1 and 2 because of the same fixed order quantities in these strategies. However, due tothe different review periods the average inventory levels vary, explaining the deviation inthe average inventory cost. On the other hand, strategies 3,4 and 5 concern the proposed(R,s,S) policies. Especially for strategies 3 and 4 the performance is significantly better thanthe baseline, with cost savings of 53.3% and 41.7% respectively. In strategy 5 the 6.6%improvement in total cost is lower than that achieved with strategies 3 or 4.

The results on production ramp up follow the same pattern as the current production rate.Strategy 2 has the highest total cost, 7.8% higher than the baseline strategy. The rest fourstrategies make improvements compared to the baseline. Strategy 1 has the same ordering

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Table 2: Strategy performance under production ramp up over 30 replications

Cycle

Time

(Days)

Finished

Wing

sets

Average

Inventory

Cost

Ordering

Cost

Transpor-

tation

Cost

Total Cost Cost

savings

%

Baseline 34.628 669.13 72,392,674 10,920 1,031,447.7 73,435,042

Strategy 1 34.625 669.13 66,556,340 10,920 1,031,447.7 67,598,708 -7.9%

Strategy 2 34.630 669.13 77,130,114 10,920 1,031,447.7 78,172,482 6.5%

Strategy 3 34.622 668.80 30,069,851 37,380 846,781.5 30,954,012 -57.8%

Strategy 4 34.616 668.70 36,894,233 21,560 866,013.4 37,781,806 -48.6%

Strategy 5 34.620 669.20 52,278,976 10,920 991,328.1 53,281,224 -27.4%

cost as the baseline, meaning the same number of orders per annum. Therefore, with theincreased review period the inventory levels are better distributed during the year resultingin lower inventory costs. However, the bigger improvements are noticed when implementing(R,s,S) policies, in strategies 3, 4 and 5.

It can be concluded that a structured inventory policy, such as strategy 3 could result insignificant improvements in the supply chain. Therefore, we recommend to formalize andupdate the current safety stock settings. A data based decision structure for setting safetystock levels should be implemented resulting in an efficient supply chain, based upon thelead time. In a similar pattern, structured methodology should be used to determined theorder-up-to levels or the order quantities based on demand, lead time and the characteristicsof the transport concept. Furthermore, coordination with suppliers to agree on productionrates to reduce risk and costs in the supply chain is necessary. It has to be ensured thatsuppliers are able to accommodate the production ramp up.

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Preface

This project has been conducted for DHL on behalf of Airbus. The project is the final stageof the master program Operations Management and Logistics at Eindhoven University ofTechnology. Although the research period was a path with many obstacles, I kept motivateddue to the challenges and many interesting findings which came along during this period.

First of all, I would like to thank my company supervisor Joeri Kuik for the opportunityto perform my master thesis at his department. I would like to thank him for all the timehe spent to help me during this research and the feedback he gave me. Moreover, I wouldalso like to thank all other persons involved, for their valuable time and providing me theinformation and insight I needed.

Second, I would to thank my supervisors at the university. Special thanks to Tom vanWoensel, my first supervisor during this research. You supported me when I had some hardtimes during the data gathering and simulation modelling period and provided me withmultiple options and advices when needed. I would also like to thank my second supervisor,Luuk Veelenturf, for his critical thoughts and feedback.

Furthermore, I would like to thank my friends both in the Netherlands and Greece. Thankyou for your mental support during this project since our gatherings were a happy momentsin a very difficult period.

Last but not least, I would like to thank my family for their contribution. You made itpossible for me to study and have always supported me during these years. I am the personwho I am today because of you.

Vasilis Markoulakis

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Chapter 1

Introduction

1.1 Company Description

Airbus SE is a European multinational corporation that designs, manufactures and sellscivil and military aeronautical products worldwide. It consists of 3 divisions (i) Airbus(Commercial Aircraft), (ii) Airbus Space and Defence and, (iii) Airbus Helicopters. Thisresearch focussed on the Airbus commercial aircraft division.

Airbus produces the most modern and comprehensive aircraft family on the market. Airbus’strategy is to build aircraft with high quality and efficiency standards, pioneering incrementalinnovative solutions and seeking the most efficient sourcing and manufacturing possible.

Airbus is a global enterprise with headquarters in Toulouse, France. Airbus fully ownssubsidiaries in the United States, China, Japan, India and in the Middle East, spare partscentres in Hamburg, Frankfurt, Washington, Beijing, Dubai and Singapore and trainingcentres in Toulouse, Miami, Wichita, Hamburg, Bangalore and Beijing.

Airbus started as a consortium of European aircraft manufacturers in 1970 to compete withAmerican aviation firms, such as Boeing and Lockheed. Today, Airbus employs over 129.000people, over 11.000 aircraft have been delivered, and revenue has exceeded 66 bn$.

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1.2 Product types and figures

The company’s production line of commercial aircraft, is marked by design, efficiency, andversatility. These characteristics allow Airbus to offer a wide range of tailored solutions tomeet the requirements of any airline and their market, while ensuring the best experience.

The Airbus’ A320 family is market leader in the single-aisle category and the most successfulaircraft family ever. The A330 family covers all long-range requirements since it providescomfortable cabin for passengers and crew. The largest commercial aircraft flying today isthe double-deck A380. The A350 eXtra Wide Body (XWB) is the latest aircraft that intendsto provide unique passenger comfort, full operational flexibility and cost efficiency.

On June 2018 Airbus had a backlog of 7,168 aircraft of which 6,058 single-aisle aircraft, 1,007A330/350 and 228 A380 aircraft. The backlog and immediate need by customers of moreefficient aircraft drive Airbus to increase the production of single-aisle from 46 in 2015 to 63aircraft a month in 2019.

1.3 Thesis Outline

In this first chapter, a short introduction to the company is given. The remainder of thisreport is organized as follows; In Chapter 2, the problem is defined, which lead to the formu-lated research objective and research questions. Furthermore, related literature is discussed.In Chapter 3, we introduce the conceptual model built in the simulation tool including themodel assumptions and the performance measures. The methodology used, which includesall the theory and formulas used in the simulation is analysed in Chapter 4. The introductionof the case study, simulation parameters, input data and several strategies are explained inChapter 5. Next, in Chapter 6, we discuss the numerical results of all strategies in comparisonwith the base case. Finally, we present our conclusions, recommendations, and suggestionsfor future research in Chapter 7.

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Chapter 2

Problem definition

2.1 Problem Statement

Airbus has decided to increase the production for its single-aisle aircraft from 46 to 63a month by 2019 to cover the increasing demand. This claim derives from the GlobalMarket Forecast (GMF) 2017 (Leahy, 2017), which projects the demand for the next 20years estimating 1829 more aircrafts than the GMF 2016 (Figure 2.1). Also, due to theincreasing competition it is highly important for Airbus to deliver competing and adequateproducts to its customers, to create shareholder value.

Source:(Leahy, 2017)

Figure 2.1: Demand forecast

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Airbus’ A320 family is a market leader in the single-aisle category. According to the Airbus’Global Market Forecast 2017, the demand of single- aisle aircraft covers 71% of the totalaircraft unit production of the company, as Figure 2.2 shows (Leahy, 2017).

Source:(Leahy, 2017)

Figure 2.2: Demand share

Big production rate increases mean pressure on the supply chain. Production ramp-up canincrease cost and impact the time of aircraft delivery significantly. The strategy of Airbusassumes that shorter lead times from suppliers to the production sites or assembly lines willresult in reduced stock,and logistics cost reduction. Based on these developments, the mainproblem statement could be defined as;

Is the supply chain for single-aisle aircraft, robust enough to accomplish the required pro-duction ramp-up rates and satisfy the desired objectives? How the supply chain could beimproved?

The focus of this research is on the supply network of the single- aisle aircraft (A320 family)wing sets in Broughton, United Kingdom. The main motivation is the fact that some of thewing sets are leaving the factory unfinished. In these cases Airbus Broughton engineers haveto travel to the Final Assembly Line to complete the wing sets.

The supply network of the wing set production facility in Broughton, UK can be modeled asa single echelon system with single items. This assumption, as depicted in Figure 2.3, canbe justified from the flow of goods from suppliers to the Airbus wing facility.

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Source:(Kuik, 2016)

Figure 2.3: Current Situation: Single echelon model

In the current situation, the lack of a structured operational planning process between pro-curement operations, production and logistics has been detected by Kuik (2016). Materialplanning is executed on program- aircraft level and purchase order quantities are fixed SAPparameters against agreed lead-times. Production can be considered stable in 6-month hori-zon, as hardly any changes in demand are noticed.

Furthermore, 75% of the flows are uncontrolled by Airbus since there is limited visibility.These suppliers tend to increase stock levels by early shipping to avoid penalties for latedeliveries and supplying ready material to get paid. On the other hand, the remaining 25%are the controlled flows, hence managed by Airbus. For the oversized materials, for whichcapacity constraints apply, the logistics teams participate in decision making on quantitiesand planning. However, because of missing or changing production data, extra costs andissues such as, too much inventory, wrong items delivered, and emergency transports oftenoccur.

Source:(Kuik, 2016)

Figure 2.4: Optimize Logistics

According to Airbus, research across all aircraft programs and production sites revealed thatthe average order quantity is 1 month of inventory with weekly delivery (Figure 2.4). The lack

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of a structured inventory policy, the high number of safety stock and orders can be held liablefor the high inventory. Moreover, Airbus faces many missing parts due to the visibility gapfrom loading to available in stock, the ordering of wrong items, and the miscommunicationwith the suppliers for small production changes. Therefore, to tackle these problems asimulation model could be built. This simulation model will aid to determine when toreview the stock, when and how much to order, which transport concept to use ensuringcertain performance levels are reached.

2.2 Research Objective and Research Questions

Based on the research area, and scope described in the previous section, the research ques-tions are defined. The scope and research area is summarized into one main question andseven sub questions. The research questions are defined below:

What is the suitable inventory policy and transport concept that deliver minimalcost against set service performance targets, in the wing production facility ofan aviation company ?

To assist a smooth and efficient search process and to carry out the search protocol in astructured manner that will be used to answer the main research question, the below subquestions are defined.

• What is the current inventory policy for the replenishment of the SKUs ?

• What are the current transport concepts in use for the SKUs?

• What are the resulting service performance levels under this policy and what are thetarget levels set by the company ?

• What are the ordering, inventory and transportation costs, associated with this inven-tory policy ?

• What is the suitable inventory policy, derived from a simulation model which replicatesthe system?

• What is the proper transport concept for each SKU under the proposed inventorypolicy?

• What are the cost savings for Broughton A320 wing-production facility of Airbus, ifthe proposed inventory policy from the simulation model is applied?

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2.3 Research Methodology

The framework proposed by van Strien (1997) and Joan Ernst van Aken (2007), describes aregulative cycle, which is a multi-step logical structure for solving practical problems. Thiscycle implements a selected scheme divided into five parts: problem definition, analysis,design, implementation, and evaluation. Since the duration of the project is limited, thefocus is on the first three steps of the problem solving cycle. The steps of Intervention andEvaluation are both out of the scope of this research. The representation of this frameworkis given in Figure 2.5.

Problem Definition

AnalysisEvaluation

Intervention Design

Figure 2.5: Regulative Cycle (van Strien, 1997)

The goal of the first step of this framework, Problem Definition is to clearly describe theproblem to be researched in the master thesis project. The work of Kuik (2016) is used todefine the AS IS state, and identify the weaknesses of the current situation. Analysis step,subsequently, aims to answer the first four research questions to accumulate necessary infor-mation before designing the main deliverable of this project. To answer these questions, dataanalysis is required and match the company’s current strategy with the existing inventorypolicies available in the literature.

Furthermore, the goal of the Design step is to answer the remaining three research questions.This step, concerns the creation of a simulation model, which replicates the system aimingto determine the cost optimal inventory parameters and the appropriate transport concept.

7

2.4 Literature

The situation of Airbus can be linked to the Inventory Routing Problem (IRP). Inventoryrouting problems are among the more important and more challenging extensions of ve-hicle routing problems, in which inventory control and routing decisions have to be madesimultaneously. According to Bertazzi, Savelsbergh, and Speranza (2008), the objective isto determine distribution policies that minimize the total cost, i.e., the sum of inventoryholding and transportation costs, while avoiding stock-outs and respecting storage capacitylimitations. The decisions to be made are: when to deliver to each customer, how much todeliver to each customer each time it is served, and how to route the vehicles so as to min-imize the total cost. The basic version of IRP is highly complex, NP-hard complexity, andis mainly formulated as a Mixed Integer Programming model (Archetti, Bertazzi, Laporte,& Speranza, 2007).

There are several variations of the IRP, depending on the characteristics of the situation.For example, the planning horizon can be finite or infinite, inventory holding costs may becharged at the supplier only, at the supplier and the customers, or at the customers only, theproduction and consumption rates can be deterministic or stochastic, the optimal deliverypolicy can be chosen from among all possible policies or has to be chosen from among aspecific class of policies.

However, in the Airbus case, for each supplier there is a predetermined transport conceptto use. This default option is preferred as is a driver of planning for the transport cost.The transport option can change, for example - from sea to air, to reduce the transit time.However, this comes as a cost and increased risk. Also, the majority of the components aremoved using dedicated equipment on bespoke routes direct from supplier to plant. In othercases, elements of consolidation are used to optimize the route.

Therefore, the focus of the research should be on the inventory policies rather than thedistribution of goods. Erdem and Sancar (2006) focused on the improvement of supply chainperformance in a commodity manufacturer in Turkey. For two different ordering strategies,a tool is built to investigate the performance of the system. Through simulation the tooloutputs performance measures, such as costs and service levels for each stakeholder.

Ravichandran (2007) examined a multi-stage stochastic dynamic programming problem withthe maximization of the expected profit as the objective, subject to specific constraints onworking capital requirement, service level, order fill rate, and end of the season inventory. Dueto computational complexity, it was chosen to examine policy options based on a simulationmodel. Depending on simulation experiments, an ordering policy which optimizes the overallobjective was proposed.

He and Du (2010) built a Decision Support System, which aimed to support managerialdecision making in order to optimize the supply chain and preserve low cost on inventorymanagement. A few popular inventory policies were examined, such as, Economic OrderQuantity (EOQ), Continuous Review Model, and Periodic Review Model. For each of these

8

policies, the DSS determines the appropriate Safety Stock and Weeks Forward Coverage,while satisfying the required Service Level.

As discussed by several studies in the literature(Erdem & Sancar, 2006; He & Du, 2010; Jainet al., 2001; Ravichandran, 2007; Shang et al., 2008), simulation modelling is a suitable toolfor analyzing supply chains. Its attractiveness relies on its capability of capturing complexityand uncertainty. Simulation modelling provides a virtual representation of real systems,which enables users to understand the supply chain processes (Erdem & Sancar, 2006).Simulation is, mainly, used before an existing system is altered or a new system built, in orderto reduce the chances of failure to meet specifications and to optimize system performance.(Uddin, Nipa, & Rume, 2015). Furthermore, simulation modelling can facilitate ”what-if”analyses to test several strategies and scenarios, compressing time and comparing alternativeoptions leading to better future decision making. For all these reasons, simulation approachis selected to replicate the supply chain in the Airbus case.

To better explain the main inventory policies mentioned above, some key definitions forstock levels have to be provided. The stock that is physically in the warehouse is calledOn-hand (OH) stock. This quantity determines if a demand of this item can be satisfieddirectly from the warehouse. The Inventory Position is defined as the sum of the On handand the On order stock minus the Backorders. The On order stock is that stock which hasbeen requested but not yet received.

Inventory Position = On hand + On order - Backorders (2.1)

The Safety stock(SS) is defined as the average level of the net stock just before a replenish-ment arrives. A positive SS provides buffer against larger demand during the replenishmentlead time.

Replenishment decisions are mostly based on the Inventory Position instead of the inventoryOn Hand. If replenishment decisions are based on the on-hand stock and the replenishmentorders have delivery lead time, the inventory is not updated when an order has been placed.Therefore, a second order will be generated again if the order has not been arrived yet. Onthe other hand, the inventory position takes into account this replenishment order.

According to Silver, Pyke, and Thomas (2016), the main goal of a replenishment controlsystem is to resolve the following three issues :

1. How often the inventory status should be determined

2. When a replenishment order should be placed

3. How large the replenishment order should be

Under deterministic conditions, the inventory status is know almost at all times and an ordercan be placed to arrive precisely when the inventory level reaches a predetermined level.

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The use of the basic Economic Order Quantity(EOQ) could be considered as a method todetermine the replenishment order quantity.

On the other hand, under stochastic conditions the answers of these three key questionsare more difficult to obtain. Inventory status is difficult to be known, and practically ischecked less frequent. However, on this case the system must be protected for longer periodfor unanticipated demand or lead time variations. The answer to the second problem restson a trade-off between the costs of ordering and carrying extra inventory and the costsof providing low customer service level (Silver et al., 2016). Regarding the replenishmentquantity, a relaxed variation of the EOQ could be still provide a solution for the problem.

Silver et al. (2016) presented a classification of the possible inventory polices based on the re-view interval and the order quantity. The four more common inventory policies are presentedin Table 2.1.

Table 2.1: Classification of Inventory Policies

Continuous Review Periodic Review

Fixed replenishment quantity (s,S) (R,s,S)

Variable replenishment quantity (s,Q) (R,s,Q)

Continuous review indicates that the inventory level is known constantly, whereas in thecase of a periodic review system (R), the inventory is only observed at certain moments intime. In practice in most situations a periodic review is applied, for example if deliveryschedules are fixed (Van Donselaar & Broekmeulen, 2014). Concerning the replenishmentquantity, it can be a fixed quantity, for example if items are delivered in full pallets. Inother systems the replenishment quantity is variable, depending on the inventory positionat the moment of ordering, since it is replenished up to a fixed order-up-to level (S). Inboth systems with a fixed and a variable replenishment quantity the decision to replenishthe inventory depends on whether the inventory position has dropped below a critical levelcalled the reorder level(s) (Van Donselaar & Broekmeulen, 2014). The reorder level is equalto the safety stock plus the demand during lead time.

As previously discussed, the Economic Order Quantity(EOQ) could be a useful method todetermine the replenishment order quantity. Precisely, EOQ is the ideal order quantity acompany should purchase for its inventory given a set cost of production, demand rate andother variables. EOQ applies only when demand for a product is constant over the year andeach new order is delivered in full when inventory reaches zero. There is a fixed cost for eachorder placed, regardless of the number of units ordered. There is also a cost for each unitheld in storage, commonly known as holding cost, sometimes expressed as a percentage ofthe purchase cost of the item. The optimal order quantity us derived minimizing the totalcost associated with the purchase, delivery and storage of the product. The basic version of

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EOQ follows this equation:

TC = PD +DK

Q+hQ

2(2.2)

Where:

• PD is the variable cost of goods: purchase unit price(P) × annual demand quantity(D)

• DKQ

is the cost of placing orders: each order has a fixed cost K, and we need to orderDQ

times per year.

• hQ2

is the holding cost:the average quantity in stock is Q2

, and h is the annual holdingcost per unit

To determine the minimum point of the total cost curve, calculate the derivative of the totalcost with respect to Q and set it equal to 0:

0 = −DKQ2

+h

2(2.3)

Q∗ =

√2DK

h(2.4)

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Chapter 3

Conceptual Model

The supply chain consists of the following stages: Manufacturer, and Suppliers. The twostages are connected through the Transport Concept module, which represents the selectionone of the available transportation modes. The Supply Chain general modelling structureis presented in Figure 3.1. The Manufacturer is divided into Output Buffer, Plant, andInput Buffer modules. Output Buffer represents the demand arrivals and a virtual buffer forfinished products and the Input Buffer models the warehouse of the facility and componentordering from suppliers. Plant module replicate the production process of wings.

Since the Supply Chain in the aviation industry is very complex, with hundreds of parts,components and materials flowing from hundreds of suppliers, located in different countries,to the wing manufacturer only a subset of the Supply Chain was selected and analysed.Hence, critical 1st tier suppliers were identified, since a delay in the components deliveredby these suppliers causes a disruption in the production. In turn, 2nd tier suppliers wereidentified, which are the direct suppliers of these 1st tier suppliers. These two sets of suppliersconstitute the Suppliers stage.

2nd

tier Supplier 1st

tier Supplier Transport concept Plant P

Input

Buffer

IB

Output

Buffer

OB

Demand

Finished

Product

Raw

Material

(QIB , RIB )

Figure 3.1: Broughton, Airbus System Overview

The structure for modelling and performance evaluation for this type of supply chain through

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computer simulation is composed of hierarchical levels. The first level, the most general, iscomposed by the above-mentioned elements and by their integration made by orders andmaterial/products flows. At the second hierarchical level, one performs the intermediatemodelling of each supply chain member.

3.1 Performance Measures

For this supply chain model, different inventory policies are tested through computer simu-lation and evaluated. The purpose of an inventory control system is, in general, to reduceholding and ordering costs while still maintaining satisfactory customer service. To evaluatethe inventory control system, indicators have to be used to measure the performance of thesystem. In this study we considered the following performance measures:

• Cycle time between customer and manufacturer

• Total Cost

The Cycle Time measures the time, in days, between an order creation and the arrival of afinished wing set at the Output Buffer, sent by the manufacturer. Since Airbus operates ina MTO environment forwarding wing sets to the Final Assembly Line, delays in deliverieswould pressure the next stages. The Total Cost performance measure gives an evaluation ofrelevant costs associated with the used inventory policy on a time period. The annual TotalCost is defined as the sum of Inventory Holding Cost plus Ordering and TransportationCosts per year, and will be described in the next chapters.

3.2 Model Assumptions

This section presents the assumptions made in this simulation study. The assumptionsindicate which aspects are taken into account and which aspects are beyond the scope of thesimulation model.

• The model considers single items and single-echelon.

• Manufacturer operates into a MTO environment.

• Incoming demand is deterministic with constant interarrival times.

• The size of an order into the Output Buffer is one wing set.

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• Inventory or transport of finished wing set is out of scope.

• Manufacturing process follow a Triangular distribution.

• Resource capacity of manufacturing stations are non-restricting.

• Shortages in stock freezes the production and restarts when the inventory is available.

• Order quantities are integer values.

• Minimum order quantities are not considered.

• No partial deliveries are considered.

• First tier sub-assembly suppliers operate in a MTS environment.

• No assembly delay is considered for first tier sub-assembly suppliers.

• First tier sub-assembly suppliers follow an (R,s,S) replenishment policy for the sub-components.

• First and second tier component suppliers have an infinite stock of raw material, andthey do not perform sourcing activities.

• Delivery lead times are stochastic and follow the Uniform distribution.

Due to the geographical dispersion among the suppliers, the stock keeping units are notcoordinated and thus were considered as single items. Furthermore, in this model the man-ufacturer makes inventory decisions at a single-location. The Point-of-Use (PoU) is notconsidered a warehouse as it is replenished only when the production of the order starts.Between the manufacturer’s warehouse and the PoU there is no replenishment lead time.Regarding the 1st tier suppliers, the inventory is not managed by the manufacturer, hencefor all these reasons we considered a single-echelon model.

Features of the final product, such as volume and value dictate a production approach whereproducts are not built until a confirmed order for products is received, named Make-to-Order(MTO). However, since the backlog can guarantee 9 years of production with the currentproduction rates, the incoming demand can be considered as deterministic. Each order hassize of 1 pair of wings, hereinafter one wing set. The scope of this study is limited until thewing set is produced. After the production is finished the order leaves the system. Inventoryof finished wing sets and transport to the final assembly line are out of scope.

We assumed the manufacturing process follows the triangular districution since Kuik (2016)mentions the most likely estimate is 34 days and in the worst case 36 days. The lowerlimit was arbitrary set to 32 days. Since the manufacturer claims that is able to facilitatethe production ramp-up, then the capacity of manufacturing stations is assumed as non-restricting. Also, the production freezes when stock shortages occur and restarts wheninventory is available again.

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The manufacturer makes inventory decisions. The order quantities are integer values sincethey are components of wing sets. To reduce the complexity of the model, no limitations onthe order quantities were considered. Hence, minimum order quantities and partial deliveriesare out of scope.

According to Kuik (2016), first tier sub-assembly suppliers operate in a Make-To-Stock(MTS)environment. They keep stock for the sub-components and we assumed the same type ofinventory policy (R,s,S) as the manufacturer is used, hence a periodic review policy withreorder point and order up to inventory level. In the model the suppliers have no inventoryfor finished sub-assemblies and since the assembly is MTS, then we decided not to includeassembly delay.

Furthermore, to reduce the complexity of the model, first and second tier component sup-pliers have an infinite stock of raw material, and they do not perform sourcing activities.Moreover, delivery lead times are stochastic. Data from the transport provider indicate thelower and upper limit in days for each of the available transport modes. For this reason weassumed that delivery lead times follow the Uniform distribution.

Finally, due to the lack data from Airbus several parameters such as, Bill-of-Materials(BoM),sub-assemblies’ BoM, unit price per component, weight and volume for each component wereassumed.

3.3 First Hierarchical Level

This section shows the implementation of the Airbus supply chain. Figure 3.2 illustrates howthe first hierarchical level was implemented, using sub-models in the Rockwell Arena soft-ware. The First Hierarchical Level consists of 7 sub-models, one for each element mentionedabove. As can be noticed, 1st tier suppliers are distinguished into two groups; Suppliers1 1,which provide sub-assemblies to the plant and critical 2nd tier suppliers have been identifiedfor them (Suppliers 2 1) and the group of Suppliers 1 2, which send components to the wingfacility according to the Bill of Materials (BoM).

Figure 3.2: First Hierarchical Level

Furthermore, in Figure 3.2 green right-to-left arrows indicate the information flow among

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the sub-models, such as component ordering. On the other hand, orange left-to-right arrowsindicate material flow, for example components or products delivery.

It must be noted that information flow from Input Buffer is sent directly to the 1st tier suppli-ers, skipping the Transport sub-model. This happens because orders from the manufacturerare sent to the suppliers and transport is only used for the delivery of these orders to themanufacturer. The second hierarchical level implements the manufacturer, transportationconcept and the 1st and 2nd tier suppliers’ conceptual models, as explained below.

3.4 Second Hierarchical Level

The second hierarchical level models each supply chain member, hence the manufacturer,1st and 2nd tier suppliers and the logistics provider. First, the Manufacturer’s ConceptualModel includes demand arrivals from customers, production of the final product utilizingcomponents from the warehouse and component ordering from the suppliers. The model isdivided into three sub-models named, Output Buffer, Plant and Input Buffer.

3.4.1 Output Buffer sub-model

When an order arrives at the Output Buffer, time of arrival and number of factory ordersare assigned. Then the order and its information are forwarded to the plant for production.It should be noted that an order means a wing set arrival.

Regarding the material flow in the Output Buffer, it concerns the delivery of a completed setof wings from the production plant to the output buffer. Statistics on the total time of theorder in the system are being kept, and then it is disposed from the Output Buffer. FigureA.1 shows the Output Buffer sub-model.

3.4.2 Plant Sub-model

The order from Output Buffer is forwarded into the Wing-set Production stage. Before theorder is processed, a check for available component inventory in the Point of Use (PoU) mustbe executed and this action is modelled in the nested PoU Replenishment sub-model. Thisprocedure is illustrated in Figure A.3.

The orders are waiting in a queue to be checked whether there is available inventory at thePoU to proceed for production. If there is a shortage of a component’s inventory at the PoU,a replenishment signal is sent to the Input Buffer. The order remains at a queue until the

16

PoU stock is replenished; after the replenishment the order exits the PoU Replenishment sub-model. This check is repeated for all components of the product’s bill-of-materials (BoM).

After exiting the PoU Replenishment sub-model the order returns at the Plant sub-model,which is depicted in Figure A.2. In the Plant sub-model the required quantity of componentsfor the order is decreased from the PoU inventory levels according to the bill-of-materials(BoM). After all the components are taken from the PoU inventory levels, the order proceedsfor production. There are manufacturing areas, which are called jigs and each wing setrequires one of them for its manufacturing process. According to Airbus, the capacity of theplant is adequate to produce 60 wing sets/ month and facilitate the production ramp up. Ifthere are idle jigs, a jig is seized and the manufacturing process starts. Otherwise, the orderwaits in a queue until a jig is released. As previously mentioned, the manufacturing processtime for a wing set follows the triangular distribution with mean 34 days, lower and upperlimit at 32 and 36 days, respectively. After the production has completed, finished wing setsare forwarded to the Output Buffer and then out of the system, as explained in the OutputBuffer Sub-model section.

The Plant sub-model also concerns the Raw Material Arrival to the PoU, as shown in Fig-ure A.2. Oversized components are sent directly to the Point-of-Use (PoU) and the PoUinventory levels are updated. The decision of which components are sent directly to the PoUtakes place in the Input Buffer sub-model (see Figure A.6), which is explained below.

3.4.3 Input Buffer Sub-model

This sub-model concerns the inventory management of raw components, used in the produc-tion of wing sets. Included actions in this sub-model are Point-of-Use replenishment fromthe Input Buffer when needed, ordering from suppliers following the inventory policy andraw component receipt from suppliers.

Every time a signal is given from the plant to replenish the Point-of-Use, as described inthe previous section and illustrated in Figures A.2 and A.3, components from Input Bufferare transferred to PoU conditioned there is enough inventory in the Input Buffer for thistransfer. Figure A.4 shows this transfer request.

An Inventory Evaluator is created every Review Period; it can be weekly, every two weeks,or monthly depending of the followed inventory policy. Then, the two other necessary pa-rameters of the inventory policy are set at the ”Set policy” module; the Reorder point s andthe Order-up-to level S. The reason of selecting a periodic (R,s,S) inventory policy and howits parameters are determined, are explained in Chapter 4.

The model searches which component or sub-assembly requires ordering. Ordering is neededwhen the inventory position is lower than the Reorder point s, as mentioned in section 2.4.Provided ordering is needed, order quantity is calculated as the variable quantity needed to

17

reach the inventory level equal to the Order-up-to level S. The order is placed and forwardedto the appropriate type of supplier.

If a sub-assembly is ordered, the request is sent to the set of Suppliers 1 1, while the requestis sent to Suppliers 1 2 if a raw component is needed. The Inventory Evaluator works ina loop searching for all items of the bill-of-materials, and leaves when no additional or noordering is required. The inventory evaluation model is presented in Figure A.5.

Finally, when a delivery arrives from a supplier the dimensions of the component are checkedto determine if it will be sent directly to the PoU as described in Figure A.2. Otherwise, asFigure A.6 depicts, if it is not an oversized component there is an 8- hour delay for unloadingand then the Input Buffer inventory is updated.

3.4.4 Supplier 1 2 Sub-model

Whenever a raw component is required, a supplier order is sent to the Supplier 1 2 sub-model. As illustrated in Figure A.7, the sub-model is straightforward; a signal is sent thatthe order has been received, statistics on the quantity are kept and is forwarded to theTransport sub-model to arrange the delivery of the components to the Input Buffer. Anassumption for this type of suppliers is they can always satisfy the requested demand.

3.4.5 Supplier 1 1 Sub-model

As it was previously mentioned sub-assemblies are included in the bill-of-materials of thewing set. Supplier 1 1 sub-model concerns the suppliers who provide these sub-assembliesto the manufacturer. For each sub-assembly, bill-of-materials were assumed as presented inchapter 5.

Suppliers 1 1 operate as a manufacturer; orders from the plant are received and processed.Raw material stock is kept satisfying the demand. When an order arrives at a Supplier1 1, the required raw materials are identified and their quantity. As figure A.8 shows, ifthere is available raw material stock then the assembly is completed and sent to the InputBuffer. Otherwise, the order waits in a queue the assembly is delayed until raw materialis replenished. Ordering is not triggered at that time, but only when is suggested by theinventory evaluator.

Suppliers 1 1 operate in Make-to-Stock (MTS) environment and follow the same inventorypolicy as described in Input Buffer; a Periodic Review policy with Reorder point s andOrder-up-to inventory level S. This model is described in Figures A.9 and A.10. EveryReview Period, an Inventory Evaluator is created and searches for every sub-assembly if rawmaterial must be ordered. Ordering is triggered when the raw material inventory in the

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supplier is below the reorder point s. Then, an order is placed to the Supplier 2 1 such thatthe raw material inventory level to reach the Order-up-to level S.

As shown in Figure A.9, the search loop concerns all the Suppliers 1 1, hence they all followthe same inventory policy. If a raw material is needed, an order is sent the their 2nd tiersuppliers. In the nested Material Ordering sub-model, every material of the sub-assemblyis checked whether an order is necessary. Notice this sub-model has two exit points; one isfollowed in case an order has been set and the other when the search has to be reset for thenext supplier. When material’s shipments arrive from 2nd tier suppliers, raw material stockis updated as Figure A.11 suggests.

3.4.6 Supplier 2 1 sub-model

To overcome the complexity of the studied supply chain, it was assumed that the 2nd-tier suppliers have an infinite stock of raw material, therefore they do not need to performsourcing activities. For this reason, the sub-model of Supplier 2 1 contains only a delaymodule for the delivery lead time to Supplier 1 1, as Figure A.12 shows.

3.4.7 Transport sub-model

The last part of the model is components’ and sub-assemblies’ delivery from the 1st tiersuppliers to the manufacturer. When an order is ready for delivery, the sub-model reads thepredefined Transport Concept for the specific supplier. Then, appropriate option is selectedand the transportation costs are calculated based on this selection. Delivery lead time isadded based on the transportation mode.

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Chapter 4

Methodology

This study examined the supply chain of Airbus in Broughton, UK. This site assembles wingsfor the entire family of aircraft commercial aircraft, producing over 1,000 wings per year. Itsactivities include wing skin milling, stringer manufacture, full wing equipping and wing boxassembly. However, in this study we focused on the A320 family since it represents the 71%of the total aircraft unit production of the company (Leahy, 2017).

As of 30 June 2018, Airbus’ backlog of A320 family jetliners remaining to be delivered stoodat 6,058 aircraft, representing approximately nine years of production at current rates. Eventhough Airbus operates in a Make-To-Order(MTO) environment, the high level of backordersensures a continuous flow of orders into the plant. Demand variation is low and thereforedemand arrivals can be considered deterministic.

The A320 family includes the A318, A319, A320 and A321 jets. For the three latter NewEngine Options (NEO) were introduced increasing the variants of the family. However, allsingle aisle wing variants have a very high level of similarity with respect to the componentsneeded, even the neo and ceo (Current Engine Option) variants. The differences are often inthe later stages of completion where for example the airline requires a certain paint schemeor an additional fuel filler hole cutting, so essentially no part changes. Furthermore, theprocessing times for all variant are virtually identical. For these reasons, we decided to treatthe wing variants as one type of product.

According to Kuik (2016), a structured operational planning process between procurementoperations, production and logistics could not be identified. Material planning is executedon program- aircraft level and purchase order quantities are fixed SAP parameters againstagreed lead-times. In case production output is slower than customer demand, SAP demandand purchase orders remain at the same level providing storage challenges.

The absence of a structured planning in place resulted in several non-routine transports,

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shipping wrong parts and having too much stock on others. As previously mentioned, Airbuscontrols 25% of the incoming flows; Data from the financial year 2015 for these flows revealedthat non-routing cost is 1.45% of the total cost, as Figure 4.1 depicts.

Figure 4.1: Supply chain characteristics for controlled flows

Research from Airbus in 2016 revealed high order quantities with already high deliveryfrequency. With these facts the use of an inventory policy as described in section 2.4,becomes evident.

Therefore, in this study we focused on examining the implementation of structured inventorypolicies in the Airbus case taking into account the transportation costs and the inventoryfulfilment rate. A simulation model, which replicates the system is built and different strate-gies of inventory policies are tested and evaluated. In the following sections, the selection ofinventory policies is discussed and how the trade-off with transportation costs is identified.

4.1 Inventory Policy

Airbus inventory could be modelled as a single echelon, single inventory location systemwith single items. Silver et al. (2016) provided guidelines on how to select a systematicinventory policy. First, the criticality of the parts to the firm, has to be established. Thestudy focused on Strategic and Bottleneck suppliers, identified by Kuik (2016), using thesupply management and purchasing portfolio proposed by Kraljic (1983). Therefore, theexamined suppliers are connected to critical parts for the production that could cause theassembly to stop.

The second feature that needs to be determined is how often the inventory status should bechecked. The time that elapses between two consecutive moments at which the stock levelis checked, specifies the review interval (R). Silver et al. (2016) mentions two cases of review

21

interval; continuous and periodic. In the first case, inventory levels are always known andon the latter the stock status is determined only every R units.

The main advantage of a continuous review model is that the period over which safetyprotection is required is shorter than the periodic review. Therefore, continuous review canprovide high service levels with less safety stock. However, since a replenishment order canbe set at any time, continuous review entails high reviewing costs.

On the other hand, periodic review allows a forecast of the staff workload since all items ina supplier group can have the same review interval. Also, pattern on item movements canbe easily identified with a periodic review policy.

Table 4.1: Rule of Thumb for Form Selection of the Inventory Policy

Continuous Review Periodic Review

Critical Items (s,S) (R,s,S)

Non Critical Items (s,Q) (R,S)

Silver et al. (2016) proposed a rule of thumb for choosing the form of the inventory policy,which is depicted in Table . Taking into consideration the characteristics of each policy, thefeatures of Broughton supply chain and the rule of thumb of Silver we concluded that themost applicable inventory control form for Airbus is the (R,s,S) policy.

4.2 Safety Stock

When demand or delivery lead time is probabilistic, the risk of being unable to satisfy thedemand directly out of stock is high. A common practice to avoid stock shortages is the useof Safety Stock (SS) based on customer service. The service level becomes a constraint indetermining the SS of an item; for example, it might be possible to minimize the holdingcosts of an item subject to satisfying, routinely from stock, 95% of the demand (Silver etal., 2016). Since Airbus has a huge backlog and is pressured on delivering aircraft on agreeddue dates, an inventory shortage would mean delays on aircraft deliveries. Hence, Airbushas set high target of inventory fulfilment rate with levels of 99% or above.

According to Silver et al. (2016) the relationship which provides the SS is the following:

SS = kσx (4.1)

Where k is called the safety factor and σx is the standard deviation of demand over a reviewinterval plus a replenishment lead time. The safety factor k reflects the service level and isthe number of standard deviations corresponding to service level probability. It is calculated

22

through the inverse distribution function of a standard normal distribution with cumulativeprobability equal to the service level; for example, k = 1.65 for 95% service level.

When the lead time (L), with mean E(L) and variance var(L), and the demand (D), withmean E(D) and variance var(D), in each unit time period are independent random variables,then the total demand in a replenishment lead time, in units is a random variable x, withmean E(x) and standard deviation σx, then

E(x) = E(L)E(D) (4.2)

and

σx =√E(L)var(D) + [E(D)]2var(L) (4.3)

However, the review period with integer value R has to be included in the lead time.Van Donselaar and Broekmeulen (2014) calculated the mean and variance of DR+L as:

E(DR+L) = E(L)E(D) +R · E(D) (4.4)

and

var(DR+L) = E(L)var(D) + [E(D)]2var(L) +R · var(D) (4.5)

In our case since demand is constant then E(D) = D and var(D) = 0. Therefore,from(4.2),(4.3),(4.4),and (4.5) the following formulas are derived:

E(x) = (E(L) +R)D (4.6)

and

σx = σR+L = D√var(L) = D · σL (4.7)

4.3 Reorder point s

The reorder point s is the answer to the when-to-order decision, since is the inventory positionat which a new order is placed. The inventory position is used as a trigger, because it includesthe on-order stock and takes account of the material requested but not yet received fromthe supplier. Order must be arranged while there is enough on-hand stock to cover demandduring lead time. Additionally,the conditions in the Airbus case are probabilistic due to thevarying lead time; thus, the reorder point has to include safety stock as described in theprevious section. Silver et al. (2016) suggests the following approach for determining thereorder point:

Reorder point s = Expected demand during lead time + Safety stock (4.8)

s = E(x) + SS (4.9)

23

then with (4.1),(4.6), and (4.7)

s = (E(L) +R)D + kD · σL (4.10)

4.4 Order-up-to level S

When an order is placed a variable replenishment quantity is used, ordering enough to raisethe inventory position to the order-up-to-level S. It has been showed that under certainconditions, the best (R,s,S) policy produces a lower total of replenishment, holding, andshortage costs than any other policy. However, the computational effort to obtain the optimalvalues of the three control parameters is high. To mitigate this obstacle, the order-up-to-levelS could be determined finding the trade-off between fixed order costs and inventory holdingcosts. Therefore, Economic Order Quantity can be considered. Hence,

Order-up-to-level S = Reorder point s + EOQ (4.11)

If the fixed order and transportation costs are relative low compared to the inventory holdingcosts of the item, it would be beneficial to order relative small quantities in many replenish-ment shipments.

4.5 Economic Order Quantity

In determining the appropriate order quantity, the criterion of minimization of total relevantcosts is used. The widely used approach of Economic Order Quantity (EOQ) offers thiscapability, since most of the key assumptions of the model apply in the Airbus case. TheEOQ assumes the demand rate is constant and deterministic, as well as the order quantityhas no size restrictions. Furthermore, items are single and are treated independently of otheritems and the entire order quantity is delivered at the same time.

However, two of the assumptions of the basic EOQ have to be relaxed in order to followthe Airbus situation. EOQ assumes zero replenishment lead time, but in our case the leadtime varies. Thus, we will use the expected value of lead time since the standard deviationhas been included in the SS, as described in section 4.2. Moreover, EOQ assumes the unitvariable cost is independent of the order quantity. Nevertheless, transport providers canoffer discounts in the unit transportation cost, as we will describe in the following sections.

EOQ is the order quantity, which minimizes the total relevant costs associated with thepurchase, delivery and storage of the product. The total cost function and each term of theformula will be analysed in the next section.

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4.6 Total relevant cost function

This section defines the total cost function incorporating all relevant costs related to thedetermination of the cost optimal order quantity. Axsater (2015) and Silver et al. (2016)described the fundamental categories of costs. The possible relevant cost can be categorizedinto the following categories: ordering costs, holding costs, and shortage costs. All costsvariable with the inventory levels should be included (Axsater, 2015). Because shortage costsare difficult to estimate, it is very common to replace them by a suitable service constraint.Since the concept of service level is used for determining the safety stock, then shortage costsare omitted the total relevant cost function. Therefore, the total costs function consists ofthree different components, each one of them are discussed in the following sections:

TRC = Cordering + Cholding (4.12)

These components are discussed one-by-one and are expressed per year.

4.6.1 Ordering Cost

The ordering costs are the costs incurred each time when an order is placed. These costs arecomposed of: a fixed cost, which is independent of the size of the order and the transportationcosts, which are dependent on the order size. Hence, the ordering costs function consists oftwo components.

Cordering = Cfixed + Ctransportation (4.13)

Fixed ordering costs are usually administrative costs associated with the handling of ordersor other fixed costs for an order such as costs of order forms, authorization, receiving, andhandling of invoices from the supplier (Axsater, 2015). Common fixed cost values are e30,e70, and e100; hence these values are tested in the Airbus simulation model.

Transportation costs concern the shipping from suppliers to Airbus plant facility. The twomain factors that affect the final cost of a shipment are the weight and the volume occupied.In the field of transport and logistics the parameter to be used to calculate the rates isthe weight / volume ratio, different for each type of transport (air, sea, land). In the nextchapter the calculation of transportation costs are described separately per type.

Transport rates are calculated based on the greater of the total weight or actual weight of thetotal volume (called also ”volumetric”) of all the packages part of a shipment. The volumetricweight is calculated by multiplying the volume of the package with the weight-volume ratioconsidered by the type of transportation.

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4.6.2 Holding Cost

The capital cost is usually regarded to be the dominating part of the holding cost. Otherparts can be material handling, storage, damage and obsolescence, insurance, and taxes. Allcosts that are variable with the inventory level should be included (Axsater, 2015). Theinventory holding costs per year can therefore be obtained by multiplying the percentage(r)per year with the value of the average net stock i.e. the purchasing price cv times the averagenet stock. For the EOQ, the average quantity in stock is Q/2. As a result, Equation 4.14represents the calculation for the financial inventory holding costs.

Cholding = rcv ·Q

2(4.14)

Conventional estimate of inventory holding costs average 25% of the annual value of inven-tories held (Stock & Lambert, 1987).

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Chapter 5

Case study

In this chapter the case study is presented. In section 5.1, the calculation of transportationcosts for each available transport mode, road transport, air freight, and ship freight are de-scribed. The model settings, namely global input variables in the model such as, interarrivaltimes, Bill-of-Materials(BoM), unit price for each component, and delivery lead times arepresented in 5.2. Simulation parameters regarding the length and number of replications,and the different strategies tested in the simulation are discussed in sections 5.3 and 5.4,respectively.

5.1 Transportation Cost

As previously discussed, the total relevant costs consists of inventory holding costs, fixedordering and transportation costs. As mentioned in the previous chapter, the calculationof transportation costs differs per type. In the following sections, the calculation for eachtransport mode hence, road transport ,air freight and ship freight are described.

5.1.1 Road Transport

According to DHL experts, trucks with dimensions 13.2m× 2.44m× 2.44m and capacity upto 65m3 and 20, 000kg are used for Airbus. However, taking into consideration the maximumutilization percentage which is around 70%, then the actual capacity is 45m3. To calculatethe cost, the number of trucks used has to be determined; the highest percentage of truckutilization between the real weight/20,000kg and volume/45m3 is relevant. For example if a

27

transport request has 250kg real weight and 5m3 volume, then 250/20000 = 1.3% and 5/45= 11.1% , selecting the highest.

This percentage is compared with the LTL-FTL schedule, presented in Table A.1. TheLTL-FTL schedule can be approximated with the following linear regression line.

[price used] = 0.87093 ∗ [capacity used] + 0.20204 (5.1)

If the calculated ratio is greater than 1, then more than 1 trucks are necessary. The numberof full trucks are equal with the integer part of the ratio. The decimal part is used in theabove regression line to determine the capacity used in a LTL truck.

For the above example, the capacity used percentage is 11.1%, hence 0.299 price used isselected. In general, the transport cost is given by:

TCtransportation = 1.25e ∗ distance ∗ [Number of full trucks + price used] (5.2)

The total number of trucks is relevant with EOQ, because it is connected with the followingratios:

Number of full trucks + price used→ max[Q ∗ (weight/item)

20000,Q ∗ (volume/item)

45] (5.3)

5.1.2 Air Freight

For air freight the weight/volume ratio is 1 : 167; hence, 1 cubic meter of goods weighs167kg. For example, to ship a package with total weight 450kg and volume 3.12m3, thevolumetric weight is then 3.12m3 × 167 = 521kg > 450kg. Hence, the shipment cost will becalculated on the volumetric weight of 521kg.

Air freight rates are based on area of origin and destination. In Table 5.1, the door todoor routine air rates from Area 1 (USA) to area Domestic Countries (France, Germany,UK, Spain, & Italy) are presented. There is a variable cost connected with the volumetricweight, compared with a minimum rate. For example, if the volumetric weight is over 35 kg,the shipment is charged with 2.12 e/kg but the minimum rate applies. On the other hand,if the volumetric weight exceeds 3000 kg, charge rate becomes 1.83 e/kg.

Considering the EOQ and the above rates, this drives us to relax the assumption that theunit variable cost do not depend on the replenishment quantity and introduce the EOQ withQuantity Discounts.

Thus the cost of air freight is the maximum value between the minimum rate and the costbased on the volumetric weight W and discount d.

TCair = max[113.11, 2.12(1− d) ·W ] (5.4)

where

W = max[Q ∗ (weight/item), Q ∗ (167 ∗ volume/item)] (5.5)

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Table 5.1: Door to Door Routine rates

Area 1

Domestic Countries e/kg

Minimum 113.11

+35 kg 2.12

+100 kg 2.12

+300 kg 1.99

+500 kg 1.93

+1000 kg 1.83

+3000 kg 1.83

Table 5.2: Quantity Discounts

Unit variable cost Weight Order Quantity Discount

2.12 0 ≤ W < 300

1.99 300 ≤ W < 500 6%

1.93 500 ≤ W < 1000 9%

1.83 1000 ≤ W 13.5%

5.1.3 Ship freight

For ship freight, containers are used to transport the goods. Specifically, for a Korean supplier60ft containers are used, due to the oversized wing panels transferred. Each container cantransport 6 wing sets, with total gross weight around 11,534 kg and costs 28,120.8 e.

TCship = 28120.8× round[W

11534] (5.6)

where

W = Q ∗ (weight/item) (5.7)

The ratio W/11534 determines the number of containers needed, therefore is round up if acontainer is not full.

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5.2 Model Settings

As discussed before, Airbus plans to increase the production of single-aisle from 46 in 2015to 63 aircraft a month in 2019. Demand for the simulation model was generated from thefollowing Master Production Schedule, presented by Kuik (2016).

Source:(Kuik, 2016)

Table 5.3: Master Production Schedule (A320 family)

It has to be noted that Broughton facility provides A320 family wings to all the FinalAssembly Lines except China. Hence, deliveries from China have to be disregarded andbecause of two holiday closures, the total delivery per year is divided by 11.2 months. We

Table 5.4: Demand arrivals in Broughton

2017 2019

Wing sets/month 49 59

Interarrival time(day) 0.6122 0.5085

consider the same demand for each month. Thus, for the current production rate (year 2017)the time between order arrivals is 0.6122 days, and 0.5085 days for the production ramp up,as Table 5.4 shows.

According to Kuik (2016) the average MTO lead time for a wing set is 34 days, but there were,also, mentions for 36 days. Therefore, we decided to use Triangular distribution T(32,34,36)for the manufacturing process with lower limit 32 days, upper limit 36 days and mean 34days.

As previously mentioned, Airbus did not provide Bill-of-Materials for the wing sets or thesub-assemblies. Therefore, we had to build one. DHL provided the Transfer Requests of 2017

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for all programs from suppliers to the Airbus wing facility in Broughton. These TransferRequests belong to the controlled flows, as described in section 2.1. After comparing thesupplier list of A320 family, identified by Kuik (2016), with the suppliers from the TransferRequests file, the relevant with the study shipments were determined. Out of this poolof suppliers, only Strategic and Bottleneck suppliers, as mentioned in 4.1, were selected inthe used BoM. Furthermore, adding the gross weight and gross volume of each shipmentwe calculated the total ordered gross weight and volume of the year 2017. Dividing thisnumbers with the number of planes delivered, we estimated the weight and volume of eachitem of the BoM. Out of these items, Top and bottom panels, and wing-skins are consideredoversized and are sent directed to PoU when they arrive at Broughton, as explained in 3.4.2.

Table 5.5: Bill-of-Materials

No Country Supplier Name Item # items kg/item m3/item

1 Belgium Supplier 1 Flap/Slat track 2 2 170.56 0.77

2 Belgium Supplier 2 Slats 4 270.90 3.19

3 Germany Supplier 3 Outboard Flap 2 166.92 5.71

4 France Supplier 4 Detailed parts- connectors 80 0.05 0.0003

5 France Supplier 5 Detailed parts-tubes 80 0.10 0.0005

6 Sweden Supplier 6 Ailerons 2 27.55 0.27

7 S.Korea Supplier 7 Top panels 1∗ 821.61 59.23

8 S.Korea Supplier 7 Bottom panels 2 821.61 59.23

9 USA Supplier 8 Detailed parts- flanges 10 0.58 0.0005

10 USA Supplier 9 Alu wing-skin pallets 1∗ 561.91 0.29

11 USA Supplier 10 Ata28-fuel 2 3.26 0.03

12 USA Supplier 11 Wing Pneumatic Ducting 2 1.13 0.01

The number of items in the above table are referred to one wing set, and were derived throughwing construction drawings (see A.14) and intuition. Special focus should be drawn on partswith asterisk in the table, Top panels of Supplier 7 and Wing-skin pallets from Supplier 9.Only one Top panel was considered in the BoM, because 50% of the top panels are producedin Airbus Broughton and the rest are sourced by Supplier 7. Also, top and bottom panelsare covered with wing-skins. Supplier 9 ships wing-skins to Broughton for the 50% of toppanels that Airbus produces, thus one item in the BoM. The wing-skins for the rest of thepanels, are sent from Supplier 9 directly to Supplier 7 and assembled there; therefore, thepanels from Supplier 7 are sub-assemblies and Supplier 9 is a 2nd tier supplier.

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Table 5.6: Sub-assemblies bill-of-materials

1st tier Sub-assemblies 2nd tier Items # items

Supplier 7 Top panel Supplier 9 Alu wing-skins 1

Supplier 7 Bottom panel Supplier 9 Alu wing-skins 1

Each top and bottom panel require one item of wing-skin of Supplier 9, as Table 5.6 shows.Nevertheless, Supplier 7 has to establish sourcing activities with Supplier 9 and for thisreason we made the assumption that Supplier 7 uses the same form of inventory policy asAirbus, hence periodic review with reorder point (R,s,S). The reorder point is calculatedin the same way as for Airbus and described in section 4.3. Service level of satisfying thedemand routinely from stock is set to 95%, since it is a typical value for high priority items.No costing is considered for the sourcing activities of the 1st tier suppliers.

As previously discussed in section 4.6.2, the inventory holding cost in the Airbus plant can becalculated by multiplying the percentage of the unit value (r) per year with the value of theaverage net stock. We consider 25% as r for the annual inventory carried (Stock & Lambert,1987). However, there were no data available from Airbus for the unit price, therefore weneeded to establish an approximation.

In 2014, Airbus spent about e13 billion on parts for its A320 family of jets (Hepher, 2015).Considering 500 aircraft deliveries on that year, then purchasing materials accounts to e26million per plane. Out of theses costs, approximated 30% are accounted for the aircraftengines, which they weigh 7822kg (Pester, 2010). Since an A320 plane weighs 36230kg, theaircraft excluding the engines weighs 28408kg and costs e18.2 millions. According to Pester(2010), the weight of a wing set is estimated at 12558kg ( 6279kg each wing), representing44% of the total aircraft weight excluding the engines, estimating the cost of a wing set ataround e8.01 millions. However, considering the assumed BoM (Table 5.5) the items of theselected supplies represent the 39% of the wing set’s weight, corresponding to e3.1 millions.With this cost we can approximate a unit price for each item of the BoM, shown in Table5.7.

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Table 5.7: Estimated Unit price of items

No Country Supplier Name Item Unit price e

1 Belgium Supplier 1 Flap/Slat track 2 109,271.82

2 Belgium Supplier 2 Slats 173,553.59

3 Germany Supplier 3 Outboard Flap 106,940.81

4 France Supplier 4 Detailed parts- connectors 32.80

5 France Supplier 5 Detailed parts-tubes 63.38

6 Sweden Supplier 6 Ailerons 17,650.37

7 S.Korea Supplier 7 Top panels 526,377.35

8 S.Korea Supplier 7 Bottom panels 526,377.35

9 USA Supplier 8 Detailed parts- flanges 371.49

10 USA Supplier 9 Alu wing-skin pallets 359,995.16

11 USA Supplier 10 Ata28-fuel 2,089.29

12 USA Supplier 11 Wing Pneumatic Ducting 724.13

DHL is the Lead Transport Partner (LTP) and is responsible for transporting the itemsto Broughton. The available transport concepts and their respective lead times are pre-sented in Table 5.8. In the simulation model, the Transportation Lead times follow Uniformdistribution.

Table 5.8: Available Transport Concepts

Geographic Availability Transport Concept Lead time transport

Direct Transport 1-2 days

Europe Break Bulk 2-3 days

Integrator EU 1-2 days

Integrator Overseas 2-3 days

Overseas Air Freight 4-7 days (incl. customs)

Ship Freight 25-50 days

Every supplier has been assigned a default transport option; After consulting with DHLexperts the following assignments were decided.

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Table 5.9: Transportation Concept Assignments

No Country Supplier Name Item Transport Concept Lead Time

1 Belgium Supplier 1 Flap/Slat track 2 Break Bulk 2-3 days

2 Belgium Supplier 2 Slats Break Bulk 2-3 days

3 Germany Supplier 3 Outboard Flap Break Bulk 2-3 days

4 France Supplier 4 Detailed parts- connectors Break Bulk 2-3 days

5 France Supplier 5 Detailed parts-tubes Break Bulk 2-3 days

6 Sweden Supplier 6 Ailerons Break Bulk 2-3 days

7 S.Korea Supplier 7 Top panels Ship Freight 25-50 days

8 S.Korea Supplier 7 Bottom panels Ship Freight 25-50 days

9 USA Supplier 8 Detailed parts- flanges Integrator 2-3 days

10 USA Supplier 9 Alu wing-skin pallets Ship Freight 25-50 days

11 USA Supplier 10 Ata28-fuel Air Freight 4-7 days

12 USA Supplier 11 Wing Pneumatic Ducting Air Freight 4-7 days

For the shipments of 2nd tier supplier Supplier 9 to Supplier 7, South Korea, ship freightwas also selected.

5.3 Simulation design

There are three simulation parameters that need to be determined before a simulation canbe started. These parameters include the simulation length, the warm-up period and thenumber of replications for each strategy.

The objective of the simulation model is to collect statistics and determine annual costs.Airbus has two holiday closures, hence the operating year is considered with 11.2 months,which results in 340 days. Therefore, the replication length was 340 days plus a 40-daywarm-up period, thus 380 days in total.

The warm-up period is an amount of time that a simulation model is running before anystatistical data is recorded. A warm-up period is always required in a simulation model toprevent interference in your data from the start-up of the model, where there are no itemsin WIP yet. By running a warm-up period the model starts data collection in a steady-stateof the supply chain. In our simulation WIP becomes steady just before 40 days, as Figure5.1 depicts. In this figure, the WIP is plotted against time in days. Therefore, the warm-upperiod in the replications has 40 days duration.

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Figure 5.1: WIP history

Since the models in this project all contain stochastic variables, simulating the same modelmultiple times will give slightly different results. To reduce the influence of this variation,each model strategy should be simulated multiple times using the average values as theoutput data. Thus, 30 replications of each strategy have been made in this study.

5.4 Inventory Policies

In this section the inventory policies that were tested are described. For a better comparisonamong the strategies, we assumed the same inventory policy on 1st tier supplier in all policies.The used strategy is the (R,s,S) with weekly reviews, safety stock corresponding to 95%inventory fulfilment rate and order-up-to level S was calculated with demand of 1 month.The used demand corresponds to the wing set demand of Airbus.

5.4.1 Base case

The baseline strategy is the reference inventory policy of the simulation model. It definesthe ’as-is’ situation in Airbus, given the available information. Currently, Airbus is not usinga structured inventory policy. According to Kuik (2016), fixed quantities (Q) are orderedwithout structural period reviews (R). Research by Airbus, across all aircraft programs andproduction sites, outlined the average order quantity is 1 month and for some suppliersweekly deliveries. Therefore, as a baseline policy review period of 1 week is tested withfixed ordering quantity of 1 month. Safety stock for 1 month is used and orders are sent tosuppliers when the stock is below the 2 months demand level. Furthermore, the initial stockat the start of the simulation is set at the Reorder point level.

35

5.4.2 Strategy 1: 2 week review period with fixed quantities

In this strategy the same concept as the base case is used, except the length of the reviewperiod. Hence, every two weeks the stock levels are reviewed and a replenishment order istriggered if they are below the reorder point. As in the base case the Reorder point is set at2 months, the safety stock covers 1 month of demand and the fixed order quantity is set for1 month.

5.4.3 Strategy 2: 1 month review period with fixed quantities

In this strategy the review period is increased to 1 month. The rest parameters, safety stock,reorder point, and fixed quantities are kept the same. Strategies 1 and 2 investigate if animprovement in cycle time and total cost is feasible, implementing the same ordering policyin terms of quantities but alternating the frequency of ordering.

5.4.4 Strategy 3: 1 week review period with order-up-to level

In this strategy, a different inventory policy is implemented. As previously discussed insection 4.1 , the suggested policy is a (R,s,S). The safety stock is calculated from the equations(4.1) and (4.7), considering service factor that corresponds to 99% inventory fulfilment rate,which is the target set by Airbus (Kuik, 2016). Using the relation (4.10), the reorder pointis the demand during the delivery lead time and the review period plus the safety stock. Tobetter integrate the trade-off among fixed order, transportation and inventory holding costs, the Economic Order Quantity (EOQ) is used to determine the order-up-to level S. Hence,S is the sum of the Reorder point s and the EOQ as described in section 4.4. The EOQ iscalculated with the minimization of the total relevant costs, as explained in sections 4.5 and4.6.

5.4.5 Strategy 4: 2 week review period with order-up-to level

In this strategy the same (R,s,S) policy is tested, except the review period is, now, increasedto two weeks. The safety stock is calculated with 99% inventory fulfilment rate and thereorder point takes into account the new review period. Finally, the order-up-to level Sadopts the same EOQ values as they are independent of the review period.

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5.4.6 Strategy 5: 1 month review period with order-up-to level

Finally, the review period is increased to 1 month. The rest parameters are calculated withthe same equations as for strategies 3 and 4 and described in sections 4.1 until 4.4.

5.4.7 Production Ramp-up

As previously discussed, Airbus intends to ramp-up the aircraft production to 63 per monthin total for 2019. That translates into 59 wing sets per month for the Broughton facility.Therefore, the base case and the strategies explained above are simulated again for the newdemand. The interarrival time is changed from 0.6122 to 0.5085 days in the simulationmodel, to replicate the production ramp-up (Table 5.4). In the baseline and strategies 1 and2, the order quantities and safety stock are still for 1 month, but the absolute values areincreased due to the higher demand. On the other hand, in strategies 3,4, and 5 except thereorder point, the order quantities as well, are affected by the increased demand since theEOQ takes into account the demand to calculate the order quantities4.5.

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Chapter 6

Results

In this chapter, the numerical results of the strategies proposed in the previous chapter arediscussed. This chapter is structured as follows. In section 6.1 we discuss the results of allstrategies and compare these with the results in the base case. Furthermore, in section 6.2,we perform a sensitivity analysis to measure the impact of other parameter values.

6.1 Numerical Results

The numerical results of the strategies are discussed in this section. We compare the resultsof all strategies with the results in the base case. By applying a paired samples t-test inOutput Analyzer, it could be evaluated whether one strategy works significantly better thananother strategy. This test compares the means between two related groups on the samedependent variable. The dependent variable is the total cost and the two related groupswould be the total cost in the baseline strategy and the total cost after applying a newstrategy. The structure of this section is based on the production rate. In section 6.1.1 theresults of the simulation model are discussed based on the current production rate. Then,in section 6.1.2 the same strategies are tested and compared to the baseline for the intendedproduction ramp-up by 2019.

6.1.1 Current production rate

In this section the performance of each strategy is compared with the base line. In Table6.1 the cycle time, the average number of finished wing sets during replication length, and

38

all the relevant costs are presented. Furthermore, the savings percentage of the total costis calculated. The baseline has the second worst performance after strategy 2, in which

Table 6.1: Strategy performance under current production rate over 30 replications

Cycle

Time

(Days)

Finished

Wing

sets

Average

Inventory

Cost

Ordering

Cost

Transpor-

tation

Cost

Total Cost Cost

savings

%

Baseline 34.686 555.37 56.764,453 10,920 856,359.3 57,631,732

Strategy 1 34.899 555.37 52,252,530 10,920 856,359.3 53,119,809 -7.8%

Strategy 2 34.660 555.37 60,320,520 10,920 856,359.3 61,187,799 6.2%

Strategy 3 34.624 555.50 26,039,333 36,680 819,987.1 26,896,000 -53.3%

Strategy 4 34.623 555.43 32,773,612 21,560 777,774.4 33,572,946 -41.7%

Strategy 5 34.623 555.43 53,041,647 21,560 777,774.4 53,840,982 -6.6%

the same quantities are ordered with 1 month review instead of 1 week. As can be noted,ordering and transportation costs are the same for the baseline and strategies 1 and 2 becauseof the same fixed order quantities in these strategies. However, due to the different reviewperiods the average inventory levels vary, explaining the deviation in the average inventorycost. To test whether these differences are statistically significant, paired samples t-testswere conducted. The tests compare the total cost of each strategy to the baseline. Forboth strategy 1 and 2 the differences are statistically significant; t(29) = 20.59, p < 0.05and t(29) = −15.12, p < 0.05, respectively. These results suggest that the total cost is lowerwhen strategy 1 is implemented and higher for strategy 2, compared to the baseline strategy.The average number of finished wing sets is exactly the same in these strategies.

On the other hand, strategies 3,4 and 5 concern the proposed (R,s,S) policies. Especially forstrategies 3 and 4 the performance is significantly better than the baseline, with cost savingsof 53.3% and 41.7% respectively. To test whether these results are significant, paired samplest-tests have been conducted. This tests compare the total cost of the baseline strategy andstrategy 3 and 4;t(29) = 114.39, p < 0.05 and t(29) = 92.96, p < 0.05, respectively. Thesevalues suggest that the improvements in the total cost when implementing strategy 3 and4 are statistically significant, compared with the baseline strategy. The same applies instrategy 5, based on the the conducted t-test (t(29) = 14.62, p < 0.05). However, the 6.6%improvement in total cost is lower than that achieved with strategies 3 or 4.

In these two strategies the transportation costs are lower because of the smaller order quanti-ties, and the ordering costs are in higher figures as a result of the increased ordering frequency.However, the total cost of the policy is significantly lower than the baseline because of thehigh impact of the inventory holding cost on the total cost. Safety stock and Reorder levelsare aligned with the demand during lead time contributing to decreased average inventory

39

levels by more than 50%. Furthermore, the cost savings do not hinder the average numberof finished wing sets and the cycle time. In fact these values are slightly better than thebaseline strategy, with increased number of wing sets from 555.37 to 555.5 and cycle timedecreased by 1.5 hours. It has to be noted that even though the cycle time in strategy 4is smaller than strategy 3, the difference in time (1.44 mins) is negligible compared to theadditional cost savings (11.6%).

The deviation in cycle time between the baseline and strategy 3 can be justified with theaverage waiting time, presented in Figure 6.1.Wait for Replenishment and Wait to be checkedare two queues in which orders wait if the available stock is insufficient to produce. Strategy 3provides a smoother flow of production without disruptions, instead in the baseline strategya small delay is observed even though it could be considered negligible compared to themanufacturing time.

Figure 6.1: Average Waiting Time

On the other hand, the Wait for Material concerns the 1st tier supplier, which performsourcing activities (Supplier 7 orders wing skins from Supplier 9). In the baseline we observethat 1st tier supplier do not have adequate stock to deliver the sub-assemblies, hence theaverage delay is 11.69 days. To mitigate this delay the 1st tier suppliers have to increasetheir order quantities to the 2nd tier suppliers, as well. The high order quantities of Airbusput pressure deeper in the supply chain, creating bottlenecks in the process. However, instrategy 3 no delays are observed for the 1st tier suppliers leading to conclude that thealignment between the demand and the ordering policy is evident.

Regarding the inventory fulfilment rate, Airbus has a target of above 99%. In the followingtable the fulfilment rate for each strategy is presented. For each strategy the average numberof orders in the Replenishment Queue and in the Wait to be checked Queue are summed and

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Table 6.2: Fulfilment rates under current production rate over 30 replications

Finished

Wing sets

Number of orders in

Replenishment Queue

Number of orders in Wait

to be checked Queue

Fulfilment

rate %

Baseline 555.37 0.062370733 0.086040092 99.973%

Strategy 1 555.37 0.170063693 0.423979731 99.893%

Strategy 2 555.37 0.042065225 0.061118781 99.981%

Strategy 3 555.50 0 0 100%

Strategy 4 555.43 0 0 100%

Strategy 5 555.43 0 0 100%

then divided by the number of finished wing sets in the first column. Subtracting this ratiofrom one, then the fulfilment rate results. The target set by Airbus is reached in all thestrategies; however, in the baseline and strategies 1 and 2 orders are delayed because ofstock outs. On the other hand, after implementing a periodic review policy, as strategies 3,4, and 5 describe behaves smoother with no delays.

6.1.2 Production ramp up

Similar to the previous section, the performance of the strategies described in 5.4 againstthe baseline is evaluated, taking into account the scheduled production ramp-up from Airbusfor 2019. The performance of each strategy is presented in Table 6.3. In absolute valuesthe costs in general and especially the holding costs are increased compared to the previousproduction rate, since the ramp up requires more stock to produce at these levels. The samepattern is drawn as before, since strategy 2 is the worst in terms of total cost. The totalcost of strategy 2 is 7.8% higher than the baseline, and this value is statistically significantbased on the conducted t-test; t(29) = −15.83, p < 0.05.

The rest four strategies make improvements compared to the baseline. Strategy 1 has thesame ordering cost as the baseline, meaning the same number of orders per annum. There-fore, with the increased review period the inventory levels are better distributed duringthe year resulting in lower inventory costs. The total cost of strategy 1 is 6.4% lowerthan the baseline, and this value is statistically significant based on the conducted t-test;t(29) = 25.59, p < 0.05. The bigger improvements are noticed when implementing (R,s,S)policies, in strategies 3, and 4.

In strategy 4, a two week review period is tested. This results in higher number of orders, thusthe higher ordering cost. Even though the orders are more frequent, the transportation costs

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Table 6.3: Strategy performance under production ramp up over 30 replications

Cycle

Time

(Days)

Finished

Wing

sets

Average

Inventory

Cost

Ordering

Cost

Transpor-

tation

Cost

Total Cost Cost

savings

%

Baseline 34.628 669.13 72,392,674 10,920 1,031,447.7 73,435,042

Strategy 1 34.625 669.13 66,556,340 10,920 1,031,447.7 67,598,708 -7.9%

Strategy 2 34.630 669.13 77,130,114 10,920 1,031,447.7 78,172,482 6.5%

Strategy 3 34.622 668.80 30,069,851 37,380 846,781.5 30,954,012 -57.8%

Strategy 4 34.616 668.70 36,894,233 21,560 866,013.4 37,781,806 -48.6%

Strategy 5 34.620 669.20 52,278,976 10,920 991,328.1 53,281,224 -27.4%

are lower because of the smaller order quantities in these kind of strategies. The inventorycost has the bigger impact on savings with almost half the cost compared to the baseline.In terms of total cost this strategy results in 48.6% cost savings, statistically significant(t(29) = 142.73, p < 0.05), and also in lower cycle time.

Strategy 3 has, as in the previous case, the optimal performance among the strategies testedin this study. The stock levels are tested every week and as expected results in the biggernumber of orders and the higher ordering costs; almost 3.5 times larger than the baseline.The more frequent deliveries lead to smaller order quantities. This fact explains the low valueof transportation costs in strategy 3. This policy results is total cost savings of value 57.8%,statistically significant( t(29) = 165.09, p < 0.05), without impeding the cycle time. In fact,the cycle time is smaller, 34.622 days compared to 34.628 days in the baseline. However, theaverage number of finished wing sets is decreased to 668.8 compared to 669.13 in the baselinestrategy. Since, the waiting time in strategies 3 and baseline is zero in both cases, then thedeviation can be explained by the variance on each replication of the manufacturing timeand the delivery lead time.

Regarding the fulfilment rate, all the strategies except strategy 1 have 100% rate, as Table6.4 depicts. Over 30 replications in these strategies no orders are delayed because of stockunavailability. In strategy 1, a small number of orders are waiting in the queues resulting ina 99.99% fulfilment rate.

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Table 6.4: Fulfilment rates under production ramp up over 30 replications

Finished

Wing sets

Number of orders in

Replenishment Queue

Number of orders in Wait

to be checked Queue

Fulfilment

rate %

Baseline 669.13 0 0 100%

Strategy 1 669.13 0.003713364 0.000180854 99.999%

Strategy 2 669.13 0 0 100%

Strategy 3 668.80 0 0 100%

Strategy 4 668.80 0 0 100%

Strategy 5 669.20 0 0 100%

6.2 Sensitivity analysis

In this research we make use of several important parameters. Most of these parametersare based on expert opinions. A sensitivity analysis is performed, to measure the impact ofchanges in important parameter values of two strategies. The sensitivity analysis is givenin Table 6.5, where we examine the impact of the fixed cost on the performance of thestrategies. We test for the production ramp up case, for strategy 3. Regarding the baseline,since the fixed cost is not influencing the policy an analysis on this strategy does not providemeaningful information. We test for fixed order cost e30 and e100, compared to e70.

Table 6.5: Sensitivity analysis on fixed order cost

Cycle

Time

(Days)

Finished

Wing

sets

Average

Inventory

Cost

Ordering

Cost

Transpor-

tation

Cost

Total Cost Cost

savings

%

Baseline 34.628 669.13 72,392,674 10,920 1,031,447.7 73,435,042

Strategy 3-30 34.619 688.83 29,590,770 16,830 846,433.7 30,454,034 -58.5%

Strategy 3-70 34.622 668.80 30,069,851 37,380 846,781.5 30,954,012 -57.8%

Strategy 3-100 34.622 668.80 30,128,193 53,400 846,866.9 31,028,460 -57.7%

The strategies seem insensitive with the fixed cost parameter. Cycle time and averagenumber of finished wing sets remained almost the same. As expected the ordering cost itselfhas lower value for fixed price e30 and higher for e70 but with small effect on the total cost.However, varying values are observed for the inventory cost since the order-up-to level haschanged in each strategy because EOQ is used for its calculation. As described in sections

43

4.5 and 4.6, to determine the EOQ fixed order costs are taken into consideration; this factexplains the improvement in cost for strategy 3-30 and the decline for strategy 3-100.

The small effect fixed order cost has on the total cost was expected since it is only a smallpart of the total cost. The lion’s share belongs to the average inventory cost, as presented inFigure 6.2. This pie chart drives us to make a sensitivity analysis on the inventory holding

Figure 6.2: Total cost composition

costs per year. This can be accomplished by adjusting the value of the percentage of theannual cost of the inventory items, which was explained in section 4.6.2. In the simulationmodel we used an estimate of 25% of the annual value of inventories held based on expertsopinion and literature (Stock & Lambert, 1987). For this sensitivity analysis, we tested for15% of annual value of inventories.

Table 6.6: Sensitivity analysis on inventory holding rate

Cycle

Time

(Days)

Finished

Wing

sets

Average

Inventory

Cost

Ordering

Cost

Transpor-

tation

Cost

Total Cost Cost

savings

%

Baseline-15% 34.628 669.13 43,435,606 10,920 1,031,447.7 44,477,972

Baseline-25% 34.628 669.13 72,392,674 10,920 1,031,447.7 73,435,042

Strategy3-15% 34.619 668.73 17,985,592 34,580 847,300.0 18,867,472 -57.6%

Strategy3-25% 34.622 668.80 30,069,851 37,380 846,781.5 30,954,012 -57.8%

We can remark that total cost is extremely sensitive on the inventory holding rate, if the

44

same policies are compared. As Table 6.6 shows, between Baseline-25% and Baseline-15%there is 39% difference in the total cost. Besides, the same applies for Strategy 3-25% andStrategy 3-15%. The cost savings from the baseline remained almost the same, with 57.8%for 25% holding rate and 57.6% for 15% respectively. It should be noted that betweenStrategy 3-25% and Strategy 3-15% except the variation in average inventory cost, there aredifferences in ordering and transportation costs too. This fact is explained by the change inthe order-up-to level S. EOQ is recalculated for the new value of holding rate, hence newEOQs are obtained. Since the holding rate is lower, it is preferable to decrease the numberof orders by increasing the order quantities. This is the reason of the decline in ordering costand the increase in transportation cost for the Strategy 3-15%.

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Chapter 7

Conclusion and Recommendations

In this chapter, we present the main findings of this research. In Section 7.1, we drawour conclusions based on the formulated research questions. In Section 7.2, we present ourrecommendations for the aircraft manufacturer. Finally, in Section 7.3 we will discuss somerecommendations for future research.

7.1 Research questions

First we answer the seven supportive research questions before the main question.

1. What is the current inventory policy for the replenishment of the SKUs?

According to Kuik (2016) Airbus does not use a structured inventory model. However,research from Airbus concluding that for some suppliers fixed quantities with average sizeof 1 month are ordered with weekly deliveries. Therefore, we assumed for the baseline ofour simulation model an inventory policy with weekly reviews, and fixed order quantitiesof 1 month. Furthermore, we assumed safety stock of 1 month and an order was triggeredwhenever the available stock was below a 2-month demand threshold.

2. What are the current transport concepts in use for the SKUs?

DHL is the Lead Transport Partner of Airbus and for transporting SKUs from suppliers to theAirbus plant in Brought, offers six different transport concepts. For European suppliers directtransport , break bulk and Europe integrator (DHL Express) are offered. Regarding overseassuppliers, air freight, ship freight and overseas integrator (DHL Express) are provided. Forevery supplier there is a predetermined method of transport based on geographical proximity,

46

weight and volume of shipments. The available transport concepts were presented in Table5.8.

3. What are the resulting service performance levels under this policy and what are the targetlevels set by the company?

Airbus has set the performance target of inventory fulfilment rate to 99% or above, and inthe current situation this target is reached.

4. What are the ordering, inventory and transportation costs, associated with this inventorypolicy?

Since data of costing in the current situation were not available, we can answer this questionbased on the results of the simulation model for the base case when the production rampup is implemented. Therefore, for the baseline the total costs reach e73.4M, of which e1Mare transportation costs, e10.9k are ordering costs and the rest are accounted for inventoryholding costs.

5. What is the suitable inventory policy, derived from a simulation model which replicatesthe system?

With the developed simulation model, several strategies were tested. The optimal strategysuggested that suitable for Airbus would be the implementation of a (R,s,S) inventory pol-icy. In this strategy, weekly reviews(R) were scheduled with safety stock corresponding to99% inventory fulfilment rate, and order-up-to level S which integrates the Economic OrderQuantity(EOQ).

6. What is the proper transport concept for each SKU under the proposed inventory policy?

Regarding the transportation concept, the method of Airbus for predetermined options wasadopted in the simulation model. Based on intuition and validated by DHL experts, fourout of the six available modes were selected; break bulk, Europe integrator(DHL Express),air freight, and ship freight. The assignment for each supplier was presented in Table 5.9.

7. What are the cost savings for Broughton A320 wing production facility of Airbus, if theproposed inventory policy from the simulation model is applied?

The suggested (R,s,S) inventory policy for Airbus A320 wing production in Broughton whenthe production ramp up is implemented, results in total cost of e30.9M, of which e843.7kare transportation costs, e37.3k are ordering costs and the rest are accounted for inventoryholding costs. Compared to the baseline strategy, in total e42.4M per year can be savedif the suggested policy is adopted. This is equal to 57.8% of the total relevant costs of theexamined system.

What is the suitable inventory policy and transport concept that deliver mini-mal cost against set performance targets, in the wing production facility in anaviation company?

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Based on the answers to the supportive research questions, we notice some large improvementpossibilities in the case of Airbus. The implementation of a structured inventory policy tobe aligned with the incoming demand becomes evident with the results of the simulationmodel. The suggested (R,s,S) policy with weekly reviews and integrating the EOQ is asuitable option for this industry, achieving smooth production with minimal cost againstthe targeted inventory fulfilment rate. The selection of the transport concept is part of theplanning and the cost savings of the proposed policy against the baseline reach 57.8% of thetotal relevant costs.

7.2 Recommendations

This study resulted in the following recommendations for the actors is aviation industry.

• Formalize and update the current safety stock settings. A data based decision structurefor setting safety stock levels should be implemented resulting in an efficient supplychain. Target safety stock levels should be set if necessary based upon the lead time.

• Similar to the safety stock, structured methodology should be used to determinedthe order-up-to levels or the order quantities based on demand, lead time and thecharacteristics of the transport concept.

• Coordinate with suppliers to agree on production rates to reduce risk and costs in thesupply chain. Ensure suppliers are able to accommodate the production ramp up.

7.3 Future research

In this section we discuss suggestions for future research.

• In this research the data availability was limited, especially on item characteristics, Bill-of-Materials, costing and the description of the current situation hence assumptionswere made. Therefore, in future research is necessary to incorporate real data to getmore accurate insights on the real life situation.

• Minimum order size for transportation purposes and capacity constraints at the plantshould be added, to better estimate the order quantities.

• The manufacturing process could be divided into stages and assign the needed itemsbased on the BoM to each stage. Hence, better ordering planning could be attainedand more accurate approach of the real life situation.

48

• Incorporate supply risk and disruptions. Variability in supply might be affect theperformance of the production.

49

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Appendix

Table A.1: LTL-FTL schedule

Capacity used 3.0% 6.1% 9.1% 12.1% 15.2% 18.2% 21.2% 24.2% 27.3%

Price used 0.195 0.231 0.263 0.304 0.327 0.358 0.387 0.417 0.444

Capacity used 30.3% 33.3% 36.4% 39.4% 42.4% 45.5% 48.5% 51.5% 54.5%

Price used 0.469 0.496 0.524 0.552 0.577 0.602 0.631 0.660 0.689

Capacity used 57.6% 60.6% 63.6% 66.7% 69.7% 72.7% 75.8% 78.8% 81.8%

Price used 0.718 0.745 0.773 0.800 0.828 0.855 0.881 0.904 0.930

Capacity used 84.8% 87.9% 90.9% 93.9% 97.0% 100.0%

Price used 0.954 0.973 0.986 1.000 1.000 1.000

Figure A.1: Output Buffer sub-model

52

Figure A.2: Plant sub-model

Figure A.3: Replenishment sub-model

Figure A.4: Input Buffer - PoU Replenishment

53

Figure A.5: Inventory Evaluator - Input Buffer

Figure A.6: Raw Material Arrival

Figure A.7: Supplier 1 2 sub-model

54

Figure A.8: Sub-assembly delivery- Supplier 1 1 sub-model

Figure A.9: Inventory Evaluator- Supplier 1 1 sub-model

Figure A.10: Material Ordering nested sub-model

55

Figure A.11: Material Arrival- Supplier 1 1 sub-model

Figure A.12: Supplier 2 1 sub-model

Figure A.13: Transport sub-model

56

Figure A.14: Wing construction drawing

57