Shigeki Umeda
email: shigeki@cc.musashi.ac.jp

Performance analysis of reverse supply chain systems by using simulation

Keywords: Reverse supply chain, Simulation, Performance evaluation, Reverse logistics 1

This paper proposes methodologies of simulation modeling and analysis of supply chain systems with reverse logistics flows. This paper discusses two types of reverse supply chain: PUSH-type reverse logistics and PULL-type reverse logistics. Generic models are introduced and analysis examples of individual features will be provided.

Introduction

A supply chain system is a chain of processes from the initial raw materials to the ultimate consumption of the finished product spanning across multiple suppliercustomer links. It provides functions within and outside a company that enable the value chain to make products and provide services to the customers. There are many discussions on supply chain operations.

In the last decade, due to environmental and ecological responsibility, enterprises are trying to reuse, remanufacture and recycle the used products to reduce the negative impact on environment, especially the manufacturers of the electrical consumer products. Requirements for corporate responsibility and sustainability are getting more urgent.

Reverse supply chain problems have been discussed from various viewpoints. Gupta at al. reviewed sustainable supply chain systems from management view [START_REF] Gupta | Palsule-Desai: Sustainable supply chain management: Review and research opportunities[END_REF]. Chouinarda at al. discussed reverse logistics activities, and proposed information support system, which covers recovery, processing of unused products, and redistribution of reusable materials [START_REF] Chouinarda | Integration of reverse logistics activities within a supply chain information system[END_REF]. Chung and Wee developed an integrated deteriorating inventory model with green-component lifecycle value design and remanufacturing [START_REF] Chung | Green-component life-cycle value on design and reverse manufacturing in semi-closed supply chain[END_REF]. Fuente et al. proposed an integrated model for the forward and reverse supply chain management (IMSCM) and demonstrated an application to a metal-mechanic company. This model covers demand management, order management, manufacturing management Procedures, Procurement Management Procedures, Distribution Management Procedures, Client Management Procedures, etc. [START_REF] De La Fuente | Integrating Forward and Reverse Supply Chains: Application to a metal-mechanic company[END_REF]. Emilianni discussed the use of online reverse auctions to source engineered components in global aerospace supply chains using online reverse auctions [START_REF] Emiliani | Sourcing in the global aerospace supply chain using online reverse auctions[END_REF]. Gou et al. discussed an optimal delivery policy of delivery and showed the formulas for optimal policies of delivery by using modifications of the EOQ model [START_REF] Gou | A joint inventory model for an open-loop reverse supply chain[END_REF]. Minner combined the problem of safety stock planning in a general supply chain with the integration of external and internal product return and reuse [START_REF] Minner | Strategic safety stocks in reverse logistics supply chains[END_REF]. Nagumey and Toyasaki developed an integrated framework for the modeling of reverse supply chain management of electronic waste, which includes recycling [START_REF] Nagurney | Reverse supply chain management and electronic waste recycling: a multitiered network equilibrium framework for e-cycling[END_REF]. They described the behavior of the various decisionmakers, and constructed the multitiered e-cycling network equilibrium models.

Performance analysis of supply chain systems is a critical issue in its design stage. Simulation is such a generic approach that gives solutions of performance analysis of supply chain systems. Chan et al. applied simulation to analysis of impact of collaborative transportations in supply chain systems [START_REF] Chan | The impact of Collaborative Transportation Management on supply chain performance: A simulation approach[END_REF]. Chatfield et al. developed a supply chain simulation system by using an object-oriented modeling method [START_REF] Chatfield | SISCO: An object-oriented supply chain simulation system[END_REF]. Labarthe et al. proposed an agent-based modeling and simulation of supply chain systems [START_REF] Labarthe | Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context[END_REF]. Umeda and Lee developed a general purpose supply chain simulator [START_REF] Umeda | Integrated Supply Chain Simulation -A Design Specification for a Generic Supply Chain Simulation[END_REF].

This paper proposes methodologies of simulation modeling and analysis of supply chain systems with reverse logistics flows, and discusses system performance of two types of reverse supply chains: PUSH-type reverse model and PULL-type reverse model. The characteristics of each model will be analyzed by using discrete-event simulation. Section 2 describes scenarios and models for the reverse supply chain system. Section 3 represents simulation-based performances evaluations. And, the final section summarizes the characteristics in each model.

2

Models of reverse logistics supply chains.

Reverse logistics scenarios

Reverse logistics systems require taking back products from customers and the repairing, remanufacturing (value-added recovery), or recycling (material recovery) the returned products. The reverse logistics in supply chains is strongly related to all stages of a product development and is also a critical problem to all level of the industry.

There are many types of reverse logistics [START_REF] Gupta | Palsule-Desai: Sustainable supply chain management: Review and research opportunities[END_REF]. We, here, consider a virtual supply chain system, which is composed of the following components: Chain manager, Supplier, Manufacturer, Retailer, Customers, Collector, and Remanufacturer (Fig. 1). This model supposes home electric appliances such as PCs, TVs, and refrigerators Chain manager receives demand order from Customer. The manager predicts demand in next ordering duration by using Customer's order. The manager also gives production orders production orders to Manufacturer and Supplier by using the predicted demands. Customer uses products, and sends the used products to Collector. Supplier, Manufacturer, and Retailer are members that form arterial flows (production generation flows) in a chain. Supplier provides parts or materials to Manufacturer according as supply orders from Chain manager. Manufacturer provides products to Retailer according as production orders from Chain manager. Retailer provides products to Customer according as Demand (Purchase) order from Customer. These chain members use Deliverers to carry materials to its downstream. Meanwhile, Collector and Remanufacturer are members that form venous flows (reverse logistic flows) in a chain. The Collector reclaims used products from Customer, when he/she disposes the used product. And, it detaches reusable materials from the disposed product, and sends them to Remanufacturer. Remanufacturer produces remanufactured products by using materials from Collector. It provides the regenerated objects to Manufacturer, such as spare-parts.

Reverse logistics models

These models are based on an analogy between arterial-venous blood flows in a human body and material-flow in a supply chain. Solid lines are production generation flow (arterial-flow), meanwhile, dashed lines are reverse logistics flow (venousflow) in Figure .2 and 3. Arterial-flows and venous-flow should be synchronized with each other. The system synchronizes venous flows with arterial flows.

The flow from Customer to Remanufacturer by way of Collector is a reverse logistics flow. Customer sends "used-products" to Collector, when Customer disposes them. The role of Collector is to distinguish reusable materials from the disposed products, and stores them. How much volume the Collector gathers reusable material from disposed materials depends on "Collection Rate". This is a probability variable that the Collector gathers reusable materials from the disposed material by Customers.

We defined two types of this reverse logistics flow: PUSH-type and PULL-type. The PUSH-type is that Collector and Remanufacturer sends reverse products to Manufacturer in an orderly manner. In PUSH-type, remanufactured products are sequentially pushed into Manufacturer, synchronizing with occurrence of reverse. Remanufactured product would be kept as material inventory in Manufacturer. In PUSH-type, remanufactured products are sequentially pushed into Manufacturer, synchronizing with occurrence of reverse. Remanufactured product would be kept as material inventory in Manufacturer.

Meanwhile, the PULL-type is that Collector and Remanufacturer work according as PULL signals from their downs-streams. In PULL-type, reverse products are stocked at Collector. These products stay at there, during no PULL signal from Remanufacturer. And, Remanufacturer does not work until it receives PULL signal.

In Fig. 3, Collector works as "Stock-driven" mode. Collector continuously observes stock volume at Remanufacturer. It starts to produce products when the stock volume is smaller than the stock-replenishment level, and continues to work until the stock volume is equal to or greater than the stock-volume level. This works according to the following operational sequences:

1. Collector periodically observes stock volume data at Remanufacturer. 2. Collector starts producing while stock volume at Remanufacturer goes down below the stock-replenishment level. 3. Collector stops producing when the stock volume reaches the stock-volume level.

This logic is also applicable to the case of between Remanufacturer and Manufacturer.

Simulation analysis

The variable of Customer's order volume is obedient to a uniform distribution between 6 lots to 10 lots (U(6,10)). And, the order interval of Customer is 5 days. Two levels of "Collection Rate" are defined. This is a probability variable that the Collector gathers reusable materials from the disposed material by Customers. These are 0.6 as high-level reverse and 0.2 as low-level reverse. Simulation duration is set on 100 days.

Table .1 represents the differences between PUSH-type reverse and PULL-type reverse. The PULL system indicates higher utilization of Collector than the PUSH system. In PUSH system, the Collector works only when the materials arrive from it Upstream (Customer). Meanwhile, in PULL system, Collector works to replenish inventories at the downstream (Remanufacturer). This mechanism, accordingly, makes higher resource utilization, when the Collection Rate is at low level. 4, Fig. 6). Meanwhile, the inventories at both Collector and Remanufacturer do not fluctuate so much. PUSH system processes the collected reusable materials in sequence to produce the regenerated parts, as far as its supply continues. The input material inventory in Manufacture, accordingly, keeps on increasing.

Conclusion

In both PUSH system and PULL system, all of the reusable materials generated at Customer (market) are transferred to Collector. In PUSH system, the gathered materials in Collector are sent to Remanufacturer, which is a re-production process. After this regeneration process, materials accumulate on Manufacturer as its input materials. Meanwhile, in PULL system, the reusable materials staying at Collector would be transferred to Remanufacturer, only when the withdrawal signals from its downstream has been occurred. Therefore, reusable materials stocked in Collector demonstrates an upward trend. This reason suppresses increase of the materials in both Remanufacturer and Manufacturer.

The next stage of this simulation analysis will need to consider processes cost factors at both Collector and Remanufacturer. When the regeneration process at both Collector and Remanufacturer is expensive, the PULL system is the better choice. This is because the PULL system avoids overproductions.

Fig. 1 .

 1 Fig. 1. Configuration the proposed reverse supply chain model

RFig. 2 .Fig. 3 .

 23 Fig. 2. PUSH-type reverse logistics model

Fig. 4 .Fig. 5 .Fig. 6 .Fig. 7 .

 4567 Fig. 4. Material inventories transition at each supplier (PUSH model, Collection Rate=0.6)

Table 1 .

 1 Simulation results (Utilizations of each supply chain member) ,and 7 represent material inventories transitions at each supplier in individual simulation run. The material inventory volume at Manufacturer increases as time goes on in PUSH system (Fig.

	Model	Collection	Utilization@	Utilization@	Utilization@
		Rate	Manufacturer	Collector	Re-manufacturer
	push	0.6	0.92	0.32	0.30
	push	0.2	0.92	0.12	0.10
	pull	0.6	0.91	0.36	0.21
	pull	0.2	0.90	0.24	0.15
	Figure. 4,5,6


Takashi Tanizaki
email: tanizaki@hiro.kindai.ac.jp

Optimum Allocation Method of Standby Taxi Vehicles at Taxi Stands

Keywords: optimum allocation, taxi company, linear programming, service productivity 1

There are two ways for a taxi company to get customers. One is to deliver taxi for a telephone request, and the other is to get customers on street. In previous research we researched rearrangement method of the taxi drivers working hours so as to operate many taxis with many telephone requests for the former method. In this paper, we propose optimum allocation method of standby taxi vehicles at taxi stands for the latter method. In order to minimize the total mileage, fulfilling the taxi demand of all the allocation and maximum number restriction of taxi stands, we formulate this problem as Linear Programming problem. As a result of numerical calculation, there are some improving points to change the number of standby taxi vehicles at taxi stands, and it turned out that total mileage may be reducible by 30%.

Introduction

In Japan, the number of customers who use taxis has been decreasing for about fifteen years. But the number of taxi vehicles (we call taxis in this paper) has been increasing from 2002 by regal revision. As a result, income of a taxi per day decreases [START_REF]Ministry of Land, Infrastructure, Transport, and Tourism[END_REF]. It became very severe to get customer between taxi companies. Then taxi companies introduce various methods to gain more customers, for example, customer membership. Some companies introduce new taxi operation system using GPS and smartphone to shorten taxi waiting time. For 90% of taxi companies, the number of possession taxis is less than 50. Moreover, for 70% of taxi companies, the capital is less than 10 million yen. Therefore it is difficult for many taxi companies to introduce above system because of financial deficit. From above reason, we research improvement method of service productivity to gain more customers by little investment for small taxi companies.

There are two customer acquisition methods for taxi companies in Japan. One is to deliver a taxi for a telephone request, and the other is to get customers on street. For the former method, it is required to deliver a taxi immediately by telephone requests. We researched improvement method of rearranging taxi drivers' working hours so as to work many taxis with many telephone requests [START_REF] Tanizaki | Improvement Method of Service Productivity for Taxi Company, Competitive Manufacturing for Innovative Products and Services[END_REF]. For the latter method, it is required to allocate taxis for a place with many customers who need a taxi. We research the optimum allocation method of standby taxis at taxi stands to minimize total millage between taxi stands and the place where customer takes a taxi.

In this paper, we analyze present situation of the Taxi Company which we research (we call Company A in this paper), research the optimum allocation method, and apply our method to Company A.

Subjects of Company A

Company A is typical small taxi company whose business area is less than 30 minutes drive by taxi. Maximum numbers of taxis at each taxi stand, the number of taxis which can be worked in every hour, and the operation records for each place where customer took the taxi in 2011 are as follows.

Maximum numbers of taxis at each taxi stand

Maximum numbers of taxis at each taxi stand are shown in Table1. They are determined by the size of taxi stand, distribution with other taxi companies, etc.

Total numbers of taxis which can be worked in each hour

Total numbers of taxis which can be worked in each hour are shown in Table2. There are many taxis in time zone between 7:00 and 1:00 for large number of taxi demand, and few taxis in time zone between 3:00 and 6:00 for small number of taxi demand.

The operation records

The operation records for each place where customer took the taxi in 2011 are shown in Table 3. These records are counted on the time when customer took the taxi. Area in Table 3 is a group of the near place of distance. "Deliver" in Table 3 is a call from a customer and picking up a customer at the call place. "Getting" in Table 3 is encountering and picking up a customer at taxi stand or on street.

There are 50 areas in the operation records of Company A. It is difficult to solve the problem with 50 areas because size of problem is large. Therefore we pick up 26 areas exceeding 1000 equivalent to 0.5% of about 200,000 annual sum totals in "Deliver" or "Getting". The analyzing results of monthly operation record are as follows.

x Many areas of taxi use increase in December because of year-end party.

x Specific area of taxi use increase in March because of graduation ceremony and farewell party. We analyze also about the operation record in each area according to time zone. The results are as follows.

x In many areas, there is much use of taxi in time zone between 8:00 and 20:00.

In this time zone, many people go out and many supermarkets open. x In A2 area, there is much use of taxi in time zone between 21:00 and 3:00. It seems that there are many bars in A2 area. x In some areas, there is little use of taxi in time zone between 10:00 and 17:00.

In these areas, there is much use of a morning, the evening, and night. Therefore, it seems that the taxi is used at the time of commuting and going home.

As mentioned above, the time zones with much use of taxi are different in areas. Therefore, it is necessary to decide optimum allocation of standby taxis at taxi stands in every hour.

Formulation

We research the optimum allocation method of standby taxis at taxi stands to minimize total millage between taxi stands and the place where customer takes a taxi (we call it demand place). In this company, it is possible to drive taxi to all demand places from any taxi stands. In order to minimize the total mileage, fulfilling the taxi demand of all the allocation, it is considered how many taxis should be driven to a demand place from a taxi stand (see Fig. 1).

Demand place (j)

Maximum numbers of taxis A1 A2 ̖ A26 Total Taxi stand (i) a x ij ҇3 b ҇3 ̖ j ҇2 Total ҇
Taxi demand (= operation records) 7.44 5.98 0.02 Fig. 1. from -to table

Notation

The following notations are used to formulate this problem. i : Taxi stand (i = 1, 2, ̖,10), j : Demand place (j = 1, 2, ̖, 26), t:Time zone(t = 0, 1, ̖,23),

x ijt : The number of taxis which move from i to j in time zone t, c ij : Distance from i to j, Xt : Total number of taxis in time zone t, T i : Maximum numbers of taxis at taxi stand i, D jt : Taxi demand in demand place j in time zone t. There are many places where customer takes a taxi in demand place. We decide to make into a representative point the place allocated mostly, as a result of discussion with Company A. We measure the mileage from a taxi stand to the representative point of demand place on the map for c ij .

Formulation

The objective is to minimize sum total of mileage from taxi stands to demand place for each time zone. The Problem is formulated as follows:

(1) subject to Constraint [START_REF] Tanizaki | Improvement Method of Service Productivity for Taxi Company, Competitive Manufacturing for Innovative Products and Services[END_REF] shows that sum total of taxis which stand by at taxi stand i do not exceed the maximum number of taxis at the taxi stand. Constraint (3) shows that sum total of taxis which move to the demand place j fulfills taxi demand of the place. Constraint (4) shows sum total of taxis which move from i to j is equal to total number of taxis in each time zone.

The application result to Company A

As shown in 2.2, total number of taxis which can be worked changes for every hour. Therefore, it is necessary to decide optimum allocation of standby taxis at taxi stands in every hour. As shown in 2.1, there are maximum numbers of taxis at each taxi stand. First, we solve the problem in 3.2 to decide optimum allocation of standby taxis at taxi stands in each time zone. Secondly, we analyze the influence on taxi operation caused by the maximum number restrictions of standby taxi at each taxi stand. Therefore we solve the problem in 3.2 without maximum number restrictions at each taxi stand, and solve the ideal optimum allocation. Thirdly, we compare two results with present allocation. For these case studies, we use optimization solution software package using the operation records in 2011/1 to 2011/12 from Company A.

Calculation procedure with maximum number restrictions

We solve the optimal solution of the problem in 3.2 under the following conditions.

x T i and X t are given in Table1 and Table2.

x D jt is annual average of the operation records in time zone t. If sum of D jt exceeds X t , D jt is distributed proportionally so that sum of D jt may become equal to X t (See Table4). x Since the calculation result becomes the real number, sum total of calculating result at each taxi stand is rounded off (See Table5).

Calculation procedure without maximum number restrictions

We solve the optimal solution of the problem in 3.2 without maximum number restrictions under the following conditions.

x Ti is equal to п, X t is given in Table2.

x D jt is annual average of the operation records in time zone t. If sum of D jt exceeds X t , D jt is distributed proportionally so that sum of D jt may become equal to X t (See Fig. 2). x Since the calculation result becomes the real number, sum total of calculating result at each taxi stand is rounded off (See Fig. 3)

Monday

Calculation results

The present allocation of taxis at taxi stands, the calculation result on Monday by procedure 4.1 and 4.2 are shown in Fig. 4, Fig. 5 and Fig. 6. We calculate day by day about one week. We show the result of Monday for example. Sum total of mileage on Monday in expression (1) is shown Table 4. From Table 4, sum total of millage is shorter in order of Procedure 4.2, Procedure 4.1, and present allocation. Therefore solving Linear Programming (we call LP in this paper) problem described in 3.2 is effective method to decide optimum allocation of standby taxis.

From Fig. 4 and Fig. 5, it is effective for mileage reduction to increase taxi at taxi stand d, f, i and to decrease taxi at taxi stand g, h. Since there is hospital near taxi stand d and supermarket near taxi stand f, there is taxi demand. Although there is a university near taxi stands i, there is no transportation from university to Shinkansen express station, therefore there is taxi demand. From Fig. 4 and Fig. 6, it is effective for mileage reduction to increase taxi at a, c and to reduce taxi at b, g, h. Since there is railroad station near taxi stand a and bar near taxi stand c, there is much taxi demand.

From Table 4, sum total of millage without restrictions is 62% of the best result in present situation (= Procedure 4.1). The maximum number restrictions of standby taxi at taxi stand a have bad influence on the management of company A.

Conclusions

We research the optimum allocation method of standby taxis at taxi stands to minimize total millage between taxi stands and the place where customer takes a taxi. In order to minimize the total mileage, fulfilling the taxi demand of all the allocation and maximum number restriction of taxi stands, we formulate this problem as LP problem. Using the operation records in 2011/1 to 2011/12 from Company A, we analyze present situation of Company A, and apply our method to Company A. As a result of numerical calculation, it turns out that solving LP problem is effective method to minimize the total mileage. There are some improving points to change the number of standby taxis at taxi stands, and it turned out that total mileage may be reducible by 30%.

We will research taxi drivers' working hours so as to work many taxis with many taxi delivery demands and incomes using optimum solution method.

Fig. 4 .Fig. 5 . 1 Fig. 6 .

 4516 Fig. 4. Present allocation of taxis at taxi stands

Table 1 .

 1 Maximum numbers of taxis at each taxi stand

	Taxi stand	a	b	c	d	e	f	g	h	i	j
	Maximum numbers 3	3	4	1	2	1	4	5	5	2

Table 2 .

 2 Total numbers of taxis in each hour

	Time zone	0	1	2	3	4	5	6	7	8	9 10 11
	Total numbers	21 16 11 7	5	5	5 16 21 21 21 21
	Time zone	12 13 14 15 16 17 18 19 20 21 22 23
	Total numbers	26 26 25 26 26 26 26 21 21 21 21 21

Table 3 .

 3 The operation records , it is better to consider different taxi allocation in March and December from an all year.

	Area	A1	A2	A3	̖	A48	A49	A50	Total
	Deliver	2085 11476 4633	̖	115	24	103	109036
	Getting 47076 3935 10897	̖	137	5	8	83220
	Total	49161 15411 15530	̖	252	29	111	192256
	From this result

Table 4 .

 4 Sum total of millage on Monday

		Present	Proce-	Proce-	Present	Proce-	Proce-
		allocation	dure 4.1	dure 4.2	allocation	dure 4.1	dure 4.2
	0 30648.4	18872.6	3349.0	12 35390.5	28832.3	14021.5
	1	2438.5	1963.3	1504.7	13 22346.3	18507.0	10757.1
	2	1966.7	1487.0	1288.0	14 17343.0	14270.8	10620.5


Krittiya Saksrisathaporn
email: krittiya.saksrisathaporn@univ-lyon2.fr

Aurelie Charles
email: a.charles@univ-lyon2.fr

Abdelaziz Bouras
email: abdelaziz.bouras@univ-lyon2.fr

Development of a decision support system to facilitate multi-criteria decision making during humanitarian operation within a project life cycle

Keywords: Decision support system, supply chain management, humanitarian logistics, multi-criteria decision making

The use of decision support systems is an important part of supply chain management. Quick and adequate decision making is sometimes difficult to achieve. Three issues arise: how to gather relevant data and use past experiences, how to make the decision when many criteria have to be taken into account, and how can we ensure that the decision making process is quick. Those three issues are currently faced by many companies, and some solutions have already been proposed in the literature. Yet, in some cases, it is difficult, if not impossible to apply those solutions. Humanitarian organizations, for example, have difficulties to build on past experiences. Quick decision making in this sector is vital. The purpose of this paper is to design and develop decision-making tool to support the performance of humanitarian logistics. A case study at the French Red Cross will validate this proposal.

Introduction

Crises happen. Earthquakes, floods, conflicts, chemical leaks are but a few examples of disasters, which affect organizations. We are unable to know for certain when a disaster will occur or how violent it will be. To assist the population affected by disasters, to send relief items and supplies, but also doctors and other human resources, a supply chain is set up. Moreover, the decision need to be quick. Resources need to be sent within five days after the disaster to enable an effective disaster relief. They are usually pre-positioned in strategic places to reduce the lead time during those crucial days. Usually, the choice regarding the usage of one warehouse instead of another and the choice regarding the mean of transportation used are overlooked because quickness has precedence over any other indicator such as costs or environmental impact. Yet, nowadays, humanitarian organizations undergo an increased pressure to improve their processes.

Humanitarian organizations differ from private companies in many ways. Among others, this sector is historically focused only on its currents action, on the day to day work needed to attend affected people. The funding of operations comes before any long term project, such as the development of Computer Based Information System (CBIS). In addition, the high turnover of staff reduces the possibilities to build knowledge on past experience, especially since few or no data are recorded from past operations. The inconsistent and uncertain environments in which organizations operate also complicate the situation, as data is difficult to collect and analyze. Last, but not least, the decision making process needs to be quick due to the lives at stake.

Currently, many organizations are becoming more interested and aware of the importance of supply chains and try to find ways to make humanitarian logistics [START_REF] Thomas | From logistics to supply chain management[END_REF] more efficient and suitable. The scope of supply chain management being broad, for the purpose of this study, we focus on humanitarian logistics, and especially on transportation. Most of the existing research in humanitarian field are focusing on the efficient understanding of the core capabilities of humanitarian logistics and the understanding of processes of disaster relief. Our proposal is to go beyond this theoretical approach and propose a simulation tool to facilitate.

Research background

To describe the current situation on decision-making of humanitarian sector concern in academic research, we have reviewed the articles related on project life cycle management, performance indicators in this sector, Multi-criteria via decision making and Decision Support System (DSS). In this part, the literature is analyzed and synthesized according to the main research areas needed to design and develop a multi-criteria decision support system for humanitarian sectors.

Project life cycle management (PLM)

Another element contributing significantly to the originality and innovative nature of our project is our intention to build our study with a project life cycle management (PLM). Indeed, up until now, many stakeholders, from donors to researchers focus on one specific phase of disaster relief operations, usually one of the two first phases (preparedness or immediate response). According to Zhou et al. [START_REF] Zhou | Identifying critical success factors in emergency management using a fuzzy DEMATEL method[END_REF], "it is clear that the existing literatures mostly deal with a specific activity in the emergency response process. Researchers care more about emergency logistics". Our intention with this proposal is to fill this gap, and take a project life cycle approach to map out the steps needed to complete humanitarian operations with specific targeted results.

In our study, we can map all the processes in one project life cycle, from the initiation until the closing or operations. This PLM approach fits to the humanitarian sector and their way of operating. Indeed, they set up operations in a specific country, were a crisis occurred, and they proceed step by step to send to assist disasters victims, until operations are closed, usually a few months after. Humanitarian operation life cycle and some factors that humanitarian workers must consider are illustrated in Fig. 1. Indeed, during operations, the quantity and quality of available data changes, and so does the priorities of each performance factor (Fig. 1.) [START_REF] Aurelie | Learning from previous humanitarian operations, a Business Process Reengineering approach[END_REF].

Multi-criteria via decision making

There are several critical success factor in the context of humanitarian and supply chains. According to [START_REF] Stephen | Critical success factors in the context of humanitarian aid supply chains[END_REF] there are ten critical success factors consisting in strategic planning, resource management, transport planning, capacity planning, information management, technology utilization, human resources management, continuous improvement, supplier relations and supply chain strategy.

If we consider one of the most important factor, transportation planning, then both long-term and short-term decisions are made. For those decisions, many factors or criteria have to be taken into account. Which one should we focus on?

Quickness or rapid response time of delivery is one of many key performance indicators (KPI) for humanitarians supply chains [START_REF] Anne | Key Performance Indicators in Humanitarian Logistics, B.S. Accounting and Information Systems[END_REF]. Indeed, the timely delivery of critical goods has always been a crucial element of an effective disaster response [START_REF] Boin | Resilient supply chains for extreme situations: Outlining a new field of study[END_REF]. The transportation costs are also needed because of the characteristic of non-profit organizations. As the major income comes from donations, it is uncertain. We should consider this issue because recently there has been an increased pressure on non-profit humanitarian organizations to held them accountable of spending their donors' money wisely. This is a universal indicator which should be included [START_REF] Anne | Key Performance Indicators in Humanitarian Logistics, B.S. Accounting and Information Systems[END_REF]. Nowadays, the environmental impact is also emerging as a key performance indicator. The CO 2 emission, also called carbon footprint, is the main factor affecting transportation. The value of Carbon Footprint is one of many factors which can show how much of equivalent tons of carbon dioxide (CO 2 ) is emitted [START_REF] Juerg | What is a carbon footprint ? definition[END_REF]. As a result, quickness, cost and CO 2 are the main criteria (KPI) of humanitarian logistics for our model.

When we consider these criteria in each phase of the humanitarian operation life cycle in Fig. 1, the importance of each criteria evolves. Whereas quickness has precedence during the first two weeks, costs and CO 2 are also important later on. During each phase of the cycle, the weight of each criteria is changed. To choose transportation means and suppliers locations in each phase, we have to consider those three criteria, but with different weights. One approach for decision maker such as multi-criteria decision analysis (MCDA) and Analytic hierarchy process (AHP) decision model should be considered.

Decision support system (DSS) and management information system (MIS)

To face the lack of available information, we propose to build a system to facilitate planning, preparing and managing operations. The system collects raw data and transforms it into information, as information is a crucial part to support the decision making process. This is the purpose of the following section.

In our study we aim to develop a multi-criteria decision support system within a project life cycle. We will analyze the factors which should and can be used to support the decision making process. We will work on available internal and external data, but also propose a solution to better take into account the knowledge of past operations. Then we will design and apply a multi-criteria decision support system for one specific organization: the French Red Cross. This study designed to address a need in a specific sector, can then be generalized to fit with other organizations, or private companies. The third and fourth section provide some inputs with regards to the system we are designing and its expected results.

Humanitarian logistics management application

As we mentioned earlier in introduction, due to the limitation of time in during international disaster relief operations, it is hard to manage operations and make a good decision, especially in terms of logistics. For example, at a humanitarian organization, when a crisis happen, a project is planned and the decision maker tries to find the best way to make decision regarding the choice of adequate transportation means, material, medicines, food and supporting staff. What should they do to ensure that their decision will be correct and proper for rescuing people in a limited time?

Problem in the determination of performance in Humanitarian Logistics is quite complex. Both quantitative and qualitative data have to be taken into account, they are unclear, scarce, and often uncertain. Although we can measure the amount of delivery time, cost, and CO 2 from a calculation tool but quantitative data such as criteria weight may have changed overnight (Fig. 1.) that must be considered. It is a complex decision making problem.

The second problem is how to combine quantitative data and qualitative data in one output value? The value should be quantitative data because it is easy to compare and make decision. One of the possible approaches is AHP model. Indeed, according to [START_REF] Saaty | Decision Making with the Analytic hierarchy process[END_REF] AHP was designed for complex problems in situations which involve a number of conflicting criteria, alternatives and defined attributes. It also can reduces the complexity of decision making in reliable way. For these reasons we choose AHP to implement our decision model.

According to AHP decision steps so we need to decompose our model into the following steps.

1. Structure the decision hierarchy of humanitarian logistics.

The goal composed of "Which supplier, warehouse and vehicles should be selected in each project?" are the goal. For supplier is illustrated in Fig. 2. The criteria of case study in our scope are time response (quickness), Cost, CO 2 . The alternative, The supplier composed of China, India and Pakistan. These preliminary data have already been gathered thanks to interviews at the French Red Cross offices.

2. Construct a set of pairwise comparison matrix for supplier goal with three main criteria. The priority of weights (W 1 ,W 2 and W 3 ) will be given by FRC logistics specialist. There is illustrated on Fig. 2 Fig. 2. Supplier AHP decision model and a pairwise comparison matrix of supplier All elements in the matrix is normalized and consistency ratio checked, then get three eigenvectors consist of EQn, ECost and ECO 2 .

3. Construct a set of pairwise comparison matrices pairwise comparison matrix for alternative (Supplier : S 1 ,S 2 and S 3 ) with each main criteria. The priority of weights (w 1 ,w 2 and w 3 ) will be given by our information system automatically which will be discussed next. All elements in the matrix is normalized and consistency ratio checked, then get three eigenvectors consist of EQnS 1 , EQnS 2 and EQnS 3 .

4. Repeat step 3 with remaining criteria (Cost and CO 2 )then we will get parameters in following : ECostS 1 , ECostS 2 , ECostS 3 , ECO 2 S 1 , ECO 2 S 2 and ECO 2 S 3 .

Our formulas are

-S1 =(EQnxEQnS 1 )+(ECostxECostS 1 ) +(ECO 2 xECO 2 S 1 ) -S2 =(EQnxEQnS 2 )+(ECostxECostS 2 ) +(ECO 2 xECO 2 S 2 ) -S3 =(EQnxEQnS 3 )+(ECostxECostS 3 ) +(ECO 2 xECO 2 S 3 )
We can make comparison and get optimize supplier from this simple model. 5. Repeat step 2, 3 and 4 with the other goal ("Which warehouse and vehicles?") then we will also get the optimize of warehouse and vehicles.

From these model we can simulate and get the ideal optimize results that we should select but in fact we have to think in advance and plan to manage uncertain situations or the riskiness that may occur. For our study we are undergoing in this issue.

Due to their characteristics, non-profit organizations may not collect data in a proper way to enable their analysis and usage directly for decision making. Our aim is to provide a DSS which will facilitate data collection and management, and store information that we can use in a complex decision model for our DSS. This DSS will quickly provide possible alternative decisions to users, with their main advantages. The components of our application for the French Red Cross decision support system are inspired from [START_REF] Sprague | Building Effective Decision Support Systems[END_REF]. The decision support methodology and knowledge management activities come from [START_REF] Bolloju | Integrating knowledge management into enterprise environments for the next generation decision support[END_REF]. Both are illustrated in Fig. 3.

Fig. 3. A framework of decision support system

The framework of the DSS we propose the consists of five main components (a) DBMS: We design and develop fundamental database by using relational database. Logistics data is provided by the FRC directly, such as disaster relief, departure location (supplier, warehouse) and destination location, vehicle type, fuel, selected item (first aid kit, food, medicine). External data has been selected the necessary data such as the value of emission factor from existing data in reliable sources.

(b) MBMS: We construct a decision model by using AHP approach. Mapping of elementary criterion and alternatives, In case of supplier choosing. It is illustrated in Fig. 2.

(c) KBMS: We will combines information from DBMS and MBMS, including past experiences of logistics worker. This phase is undergoing.

(d) UI: We choose to develop a GUI web-based application to manage and simulate information flows of our DSS.

(e) Group of Users : We divide users into three groups : operation user, database administrator and web developer.

We already developed the first version of web-based application for the logistics department of FRC. It is working on client-server model. The DBMS and MBMS are embedded on server-site and scoped in supporting the decisionmaking process in the tactical level and the operation levels.

The first result and expected results

This first tool has been chosen to show the value added of our proposal, with a simple and straightforward application. It corresponds to the specifications required at the FRC. Some web pages are already done as illustrated in Fig. 4. Further work is undergoing to include the other performance dimensions and enable an agile response according to the life cycle phase.

Conclusion

Presently, The work of humanitarian organizations is an increasingly important mission, especially during disaster relief operations. The main issue faced by humanitarian workers is the lack of decision support systems to improve, facilitate and accelerate the process of decision making.

In the near future, we will model the specific AHP decision model. We also plan to design and apply a case study approach to gain some crucial knowledge from involved logistics experts. Then we will continue to develop the DSS application in every needed function and make it fit to user requirements as defined by our case study.

Acknowledgment

This project has been funded with support from the European Commission (EMA2-2010-2359 Project). This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Fig. 1 .

 1 Fig. 1. Humanitarian Operation life cycle

Fig. 4 .

 4 Fig. 4. FRC logistic management application


Supimmas Thienhirun

Pisut Koomsap

Application of Design by Customer in Tile Decoration Business

Keywords: design by customer, tile decoration, conjoint analysis

The new concept of Design By Customer (DBC) has been introduced recently to increase customers' satisfaction by providing maximum involvement channels to the customers so that the customers do not reduce their requirements to meet with the specifications of available products. It is foreseen that if DBC concept is applicable for tile business, it will encourage customers to involve actively in pursuing their own decorative designs. Thus, this research has investigated the potential to apply Design By Customer concept in tile decoration business which requires the following two main issues to be answered. The first issue is on the readiness of technologies at this moment to support the production of different decorative tiles quickly. The second one is on customers' interest in tile decorative design.

Introduction

Tile has been used for surface covering since ancient time. Not only can it provide good surface protection but also allows aesthetic value to be created on the surface from arrangement of its different colors, shapes and sizes. However, in today tile business, manufacturers only allow customers to select or create their decorative designs from the available on shelf tiles. The manufacturers typically offer tiles with various designs, and may display a few tile decorations to attract customers. The customers who like these decorations can resemble by purchasing similar tiles while others are required to mix and match available tiles to create their own designs. Customized decorative design may be available, but at high cost and long waiting time. Therefore, what has happened is the opportunity for the customers to involve in designing their decoration is limited. Moreover, most of them unavoidably relax their requirements to select only available choices that manufacturers offer instead of pursuing their designs.

Recently, Design By Customer concept (DBC) has been introduced in order to encourage manufacturers to increase customers' satisfaction by letting customers engage in a value creation as many channels as possible [START_REF] Risdiyono | Design by Customer: concept and applications[END_REF]. The customers are encouraged to select the products that best match with their requirements, costs and waiting time. The level of involvement (CIDP) then, varies from mass production to individual personalization as illustrated in Fig. 1, depended upon the capability and readiness of manufacturers, customers' interest, and engineering constraint [START_REF] Risdiyono | Customer Involvement in Value Creation[END_REF].

Therefore, applying DBC in tile decoration business might be applicable to increase customers' satisfaction level. As illustrated in Fig. 1, if the customers do not have specific requirement, they can involve in a value creation chain by selecting typical standard products. Furthermore, the customers are able to mix and match some components before assembling and/or manufacturing phase. Lastly, they can participate in designing the products by using the tools that manufacturers provide such as google sketch up, adobe illustrator, also photos and sketches as initial designs. In order to allow customers to participate in various channels with fast response, tile production technologies must support DBC concept. In addition, the product attributes must not violate the manufacturers' capability and crowdscreening can be implemented in order to extract the top rank components and products. Furthermore, the customers themselves should express their interests to participate in designing the products; otherwise, providing these channels will not bring success to the business. Therefore, this paper presents the investigation of two key elements: available tile production technologies and customers' interest.

Review of tiling technologies

Tile production typically consists of eight steps as illustrated in Fig. 2 [3], Raw materials including clay, water, minerals and chemical substances are prepared. These materials are weighted according to the types of tiles before being mixed and grinned together in a mixer until the mixture looks smooth. The fine clay is sent to spray drying process in order to remove the excess water. Once the condition of the fine clay is appropriate, the forming process is started. Various methods i.e. dry pressing, extrusion and punching and pressure glazing are available for making tiles in different sizes and shapes. These tiles are dried in the next step which needs several days to remove the humidity slowly to avoid cracks and shrinkage. Lastly, the dried tiles are glazed before sending to the furnace to increase the strength which also takes several days. The value of tiles increases when they are assembled to express a design. Tiling of a customized decorative design can be done by using ready-made tiles or by fabricating new tiles and their capabilities to support DBC are reviewed in the following subsections.

Decoration from ready-made tiles

For this group, a pattern similar to the design is created by modifying and arranging the ready-made tiles. Mosaic and profile-cut tiles are in this group.

Mosaic tile

Mosaic is the arrangement of small pieces of tile to form the pattern. Recently, the automation tiling system has been developed to support the mosaic tile industry [4] [START_REF] Phooripoom | Design and development of tiling automation for custom mosaic design[END_REF]. The process starts from rendering an input image to a mosaic-like image that its pixels are matched with available color tiles. The identified tiles are released from storages and sorted automatically before assembling in the final step. According to [START_REF] Phooripoom | Design and development of tiling automation for custom mosaic design[END_REF], a 1m x1.5m custom mosaic can be produced in approximately two hours.

Profile-cut tile

This type of decoration involves cutting and assembling the ready-made tiles. Recently, researchers have introduced rapid cutting for tile pattern creation [START_REF] Kuagoolkijgarn | Applying image processing for rapid customization of multi-color nested pattern products[END_REF]. The process starts from tracing the contours on the image of a design. The tile colors are selected next by matching the colors on the image with the colors available in a stock. These pieces of tiles are cut rapidly on a waterjet machine that toolpaths are generated directly from the obtained contour without writing a single G-code [START_REF] Ut | Simplifying Abrasive Waterjet Cutting Process for Rapid Manufacturing[END_REF]. The decorative area is formed by assembling these cut tiles. In case that the contour is larger than the tile, the system will calculate the new toolpath and identify the number of tiles before the cutting process starts [START_REF] Suthamviwat | Enhancing a design by customer for muti-color nested pattern products[END_REF]. Since the toolpaths can be generated quickly, tiles for the decorative area of 1m x 1.5m can be cut within one to two hours.

Decoration by fabricating new tiles

Another group of the customized tiles is related to the creation of a design on tiles in the production process. Additional steps are introduced into the process. This group consists of molding and printed tiles.

Molding tile

Molding tile includes an embossed design on ceramic and sandstone tiles. For molding ceramic tiles, moulds are made at the beginning before preparing the materials and moving to the other steps as in Fig. 2. However, for sandstone tiles, the process is more complicate. The design is created and drawn on a paper that is used to form the clay to a pattern. Once the clay is set, the craftsman will construct a prototype by casting plaster and curving the details by hand. The mold is then constructed from the fine prototype [START_REF]Than to acquire our sandstone[END_REF]. According to local manufacturers, making a standard molding tile in any size takes at least three weeks. Therefore, the waiting time will be much longer for the custom decorative design.

Printed tile

Tile printing is the screening of pictures on the ceramic tile surface. This extra step is done before the glazing step. Furthermore, printing on glass tiles has been developed and recently introduced to the market. The production process is divided into three main steps. First, the image is customized according to the customer preference. Next, the input image will be printed on the glass surface and lastly dried to ensure that the screen is stacked tightly on the surface. The resolution of the image was about 300 dpi [10] and the largest tile size is limited to 24" x 36". According to the local manufacturers, making a customized product takes about three weeks.

Survey of customers' interest

Regarding to the current technologies, the manufacturers are able to offer various tile decoration methods but at different response times. Therefore, the survey of the customers' interest has been conducted by using one-to-one interview and conjoint analysis. In the first part of the survey, the participants were asked about their interest on tile decoration and on designing the tile patterns by themselves. They were also asked to rank tile decoration methods from most to least preferable (most =4, least =1) for different types of pictures as showed in Fig. 3 and to support their ranking with rationale. Table 1. Tile decoration methods 1) Printed tile decoration1: The picture is formed by assembling 8"x12" printed tiles with the gap of 3 mm.

2) Printed tile decoration2: The picture is printed on one 24"x36" large tile that is cut along the picture profile. It is assembled with 8"x12" background tiles with the gap of 3 mm.

3) Mosaic tile decoration: The picture is displayed by arranging small pieces of tile (1.5-2.5 cm 2 ) with the gap approximate 2-3 mm.

4) Profile-cut tile decoration: 24"x36" solid color tiles are cut and are assembled with 8"x12" background tiles to form a picture. The gap is 3 mm.

Since molding tile has long processing time, it is not suitable for DBC. Therefore, only the other three types were used in the survey. Four options created from the other three types as shown in Table 1 were presented to participants.

Furthermore, conjoint analysis was used to identify the customer preference and key attributes by letting the participants take every combination of attributes in consideration rather than a single factor [START_REF] Kotri | Analyzing customer value using conjoint analysis: The example of a packaging company[END_REF]. The important attributes are classified into five groups and three levels each as followings: These attributes can be combined into 243 profiles; however, the combinations were reduced to eighteen profiles by using orthogonal design in SPSS in order to avoid confusion when the participants rank the cards [START_REF]Using Conjoint Analysis to Model Carpet-Cleaner Preference[END_REF]. The examples of conjoint cards are showed in Fig. 4. According to the survey, printed tile is the most preferable to customer as it provides a realistic picture but the printed tile decoration 2 will not be available for large decorative area due to the available size of tile. The attractiveness of the printed tile decoration 1 is lower and is not much higher than mosaic decoration. Furthermore, due to the long lead time, approximately three weeks, the design from customers at the beginning phase is not recommended. The offering channels should be limited to only mass production and mass customization1. The customers are allowed to choose the standard products from the stores or customize each design of tiles by themselves rather than design the new one.

Mosaic tile

Some groups of the customers are interested in this decoration method. The smaller the tile size and the gap size are, the more attractive, this method will be. Regarding to assembling the ready-made tiles group, the lead time is shortened to only a few hours. Therefore, the manufacturers can open all four channels to the customers to involve. As a result, the customers can participate from selecting the final products to design the new one themselves. However, the technology is not ready to allow the variation of the size and shape of tile.

Profile-cut tile

The fast response of the available technologies allows manufacturers to open all four channels to the customers even though the limited number of the customers has interest in this decoration method. This method might be an attractive choice for abstract picture and the picture of cartoon character that display by solid colors.

Molding tile

According to the slow response, the customers can hardly involve in the designing process. Therefore, the manufacturers are suggested to open only two channels, letting the customers participate in selecting standard products, and mixing and matching components.

Conclusion

This paper presents the potential to apply DBC in tile decoration business. From the study, customers are interested in custom decorative design, but their involvement will be varied depending on the manufacturers' capabilities. The manufactures will be able to offer four channels from mass production to individual personalization for tile decoration methods that use the ready-made tiles, but for printed tile decoration that the customers prefer most they will be able to offer only two levels; mass production and mass customization1 due to the slow response time. If the manufacturers can turn customers' interest to mosaic, both sides will benefit most from DBC. Therefore, future research will focus on applying choice architecture to direct customers' interest to the decoration methods that use the ready-made tiles.

Fig. 1 .

 1 Fig. 1. Channels for customers' involvement in Design By Customer (DBC)

Fig. 2 .

 2 Fig. 2. Tile production process (reprinted from http://www.madehow.com/Volume-1/Ceramic-Tile.html)

Fig. 3 .

 3 Fig. 3. Pictures for tiling method survey (a) http://www.umarin.com (b)www.renders-graphiques.fr (c) http://justbeingmysel.blogspot.com (a) (b) (c)

1 .

 1 Surface: Glossy, Matt, Rough 2. Design: Picture, Tracy, Plain 3. Material: Ceramic, Glass, Stone or Sand 4. Size: Small (2x2cm.), Medium (20x20cm.), Large (60x60cm.) 5. Waiting time: Within three days, Within two weeks, More than two weeks

Fig. 4 . 7 Surface

 47 Fig. 4. Example of cards (a) http://freevectorsb.wordpress.com (b) www.cotto.co.th

Table 2 .

 2

	Re esult and Di iscussion
	From the in nterview of thi irty-two partici ipants who hav ve planned for r tiling or expe eri-
	enced in til ling, thirty peo ople were inter rested in tile d decoration of w which twenty--six
	participants	preferred to e	ngage in desig gning tile decor ration by them mselves. The ran nk-
	ing score in n Table 2 revea als that the part ticipants prefer rred the printed d tile decoratio on 2
	most for all	three pictures . Most of the p participants cla aimed that this	decoration me eth-
	od has only y a few tile gro uts on the pict ture which mak ke it looks dom minant and rea alis-
	tic. The prin nted tile decora ation 1 came s	econd. Some p participants pre eferred this me eth-
	od because		it looked smo ooth like a pri inted picture o on a paper. Th he profile-cut	tile
	decoration c came last since e they felt that i it doesn't prov vide realistic pi	ctures.
	From proce ssing scores in n SPSS program m, the card num mber2 as show wn in Fig. 5(a) h had
	the highest		utility score	of 11.227 (Y 2 2 = 10.2+0.26 61+1.133+0.694 4+0.139-0.7-0 .5).
	The utility	score is referre ed to the prefe erence of each h attributes. Th hus, higher uti lity
	result refer s to higher p preference from m the view p point of the p participants [1 12].
	Furthermore e, as illustrated d in Table 3, de esign is the mo ost important a among five attr rib-
	utes from th he customers'	point of view.	Therefore, the e customers te end to take des sign
	in considera ation as the key y factor before			making a deci ision.
				Survey result	on tiling metho od preferences
	Deco	ration method				Average sc ore
	Printed tile dec coration 1	23.67 7%	25.33% %	26.67%	25.22%
	Printed tile dec coration 2	32.33 3%	35.00% %	33.67%	33.67%
	Mosaic tile dec coration	22.67 7%	20.00% %	22.33%	21.67%
	Profile-cut tile		decoration	21.33 3%	19.67% %	17.33%	19.44%
				Table 3. Im mportance Valu ues
	Regarding t to the manufac cturers' capabi ilities and the c customers' int	erests in Table e 4,
	DBC has th	e potential to b be applied in ti ile decoration b business. Howe ever, the levels s of
	customer in nvolvement wi ill be varied d depending on	the manufactu urers' capabilit ties
	which are d	iscussed as fol llowings.

Table 4 .

 4 Assessment of manufacturers' capability and customers' interest

	Category	Decoration	Customers'	Mass	Mass	Mass	Individual
		method	interest	production	customiza-	customiza-	personaliza-
					tion1	tion2	tion
	Using the	Mosaic	21.67%	x	x	x	x
	ready-	tiles
	made tiles	Profile-cut	19.44%	x	x	x	x
	in a pattern	tiles
	Fabricating	Molding	-	x	x
	new tiles	tiles
		Printed tiles	25.22%	x	x


Bernardo Nicoletti

Lean and Automate Manufacturing and Logistics

Keywords: Lean Six Sigma, Production, Logistics, Automation, ICT

This paper presents the Lean and Automate method on how to streamline and automate simultaneously the production and logistics processes in order to improve quality, reduce waste, and increase agility using Lean Six Sigma with the support of some advanced technologies. The approach is based on research which showed that for the best application of Lean Six Sigma to production and logistics, the new processes should be improved taking into account also the possible use of advanced automation. The case studies provide good practical examples of the applications and implications of advanced technology in production, with theoretically grounded insights. The findings suggest that Lean and Automate could be a key enabler of Lean Six Sigma in the manufacturing organizations.

Introduction

The economic crisis has pushed the organizations to become more agile and leaner, in order to adapt rapidly to changing socio-economic conditions. The reengineering of production and logistics processes must take into account this imperative. Lean Six Sigma combines the better of two distinct methods [START_REF] Nicoletti | Lean and Digitize[END_REF]: Six Sigma, which helps in reducing the number of defects and the variation of the outputs, and Lean Thinking which helps in reducing the cycle times. A certain number of organizations have achieved excellent results by the use of these methods in different fields. There is another extremely important aspect in the optimization of the processes not included in this approach, which is the flexible integration of the processes and their activities. The lean approach includes the principle of flow. The recipe to achieve flow is to balance the work in the process. This approach often assumes a constant production. It becomes a hard coded integration. Lean and Automate aims at the same time to streamline and automate the production and logistics processes and make them agile and lean at the same time. Out of a long experience in different types of organizations and several research works in production and logistics process design and management and tools and digitization techniques [START_REF] George | What is Lean Six Sigma[END_REF], this approach shows clearly the sequence of activities to integrate the methods of business process improvement and automation in any production and logistics organization.

Literature Review

Lean Six Sigma includes a set of principles and tools that assist in the identification and steady elimination of waste (Muda) and the reduction of defects and variability in the output of a process Quality and costs in production and logistics are strategic elements [START_REF] Nicoletti | Lean and Digitize[END_REF], Flexibility is paramount in modern times. There is the need to use in the world of production and logistics the flexible automation technologies, now more and more available at lower costs, applying them to re-engineered processes. Lean Six Sigma implementations provide several benefits, among them: cost reduction, productivity increase, quality improvement, lead time reduction, supplies reduction, and customer satisfaction improvement. Lean Thinking has been applied in the production context through Lean Production studies, However, these studies do not take into account new technologies more and more available. Actually, Sugimori et al. argued that the use of the automation for production introduces unnecessary costs, overproduction, and uncertainty [START_REF] Sugimori | Toyota production system and Kanban system[END_REF]. This theory contrasted with the trends since the 80's, when the interest on MRP and ERP systems, numerical control machines, and fully-automated production lines has been huge. The highly automated organizations were less vulnerable to the typical problems of manual work. However, there were examples of over-investment in automation that have worsened the flexibility and the ability to respond to the demand changes (General Motors in the'80s; CIM), The automation alone to improve performance remains uncertain [START_REF] Cheung | An agent oriented and knowledge-based system for strategic eprocurement[END_REF]. This is the reason why the automation must be flexible and connected with an improvement in the processes. Powell and Skjelstad [START_REF] Powell | RFID for the extended lean enterprise[END_REF] applied a specific sensor technique (RFID) to the manufacturing processes. They presented a method and two business cases which proved the usefulness of this type of advanced technology in manufacturing companies. This paper is a descriptive study [START_REF] Arbnor | Methodology for Creating Business Knowledge[END_REF]. Inductive reasoning is used to propose a conceptual framework for the use of advanced technology as an enabler in the development and further support of the Lean Six Sigma approach. The primary research method deployed is multiple case study research. The scientific foundation for the research is the literature from the production and logistics management with a focus upon the advanced automation technologies [START_REF] Bell | Lean Enterprise Systems: Using IT for Continuous Improvement[END_REF] and [START_REF] Nicoletti | Lean and Digitize[END_REF].

The Lean and Automate Method

The Lean and Automate method can be divided into six macro phases and 20+1 micro phases (see Fig. 1) 1 . The 21st phase is optional. The organizations must treat the initial Lean and Automate project as the beginning of an iterative cycle that generates continuous improvement. Process improvement should not be triggered by a problem, but be part of the organizational culture.

Macro

The Changing Environment

A research on the trends in production showed that one of the most relevant aspects for improvement is the technological optimization of processes 2 . Modern technologies improve the efficiency of processes. The manufacturers will buy more and more equipment to save costs and optimize business processes. We state that it is important to move from the automation of machines to the automation of processes. Machining centers are sophisticated Computer Numerically Controlled machines that can perform milling, drilling, tapping, and boring operations at the same location with a variety of tools. They are still isolated. An important development is to automate the full process rather than just one of its components. In order to reach this goal, it is essential to integrate different machines or entire sectors of the production cycles. This paper concentrates on an architecture based on four technologies that can help on this respect: The Internet of Things (IoT), Sensor technologies, either with bar codes or RFID; Big Data; and Business process intelligence. The following sections define each one of these technologies and analyze each one of them and their possible contribution to process improvement. Their implementation should be done according to several generations, implementing one technology at a time and using some generalized middleware for managing interfaces in a more standardized way3 .

IoT

Internet of Things (IoT), also called Machine to Machine (M2M), refers to technologies that allow both wireless and wired systems to communicate with other devices. Such communication can be accomplished by having a remote network of machines relay information back to a central hub for analysis. Normally, a multi-tier architecture is used to exchange information at lower levels IoT when combined with Internet and with cloud computing is an excellent way to add flexibility and investment saving to the collaboration also with vendors [START_REF] Nicoletti | Cloud Computing in Financial Services[END_REF]. A variation to IoT is to use a ZigBee network.

RFID

Radio-frequency identification (RFID) is the use of a wireless non-contact system allowing radio-frequency electromagnetic fields to transfer data from a tag attached to an object. The purpose is automatic identification, possible storing of data, and tracking. Unlike a bar code, the tag does not need to be within line of sight of the reader. It may be embedded in the tracked object. An RFID tag attached to a product during its manufacturing can be used to track its progress through the production line. In the last few years, three key factors drove a significant increase in RFID usage: decreased cost of equipment and tags, increased performance to a reliability of 99.9%, and improved international standards [START_REF] Glover | RFID essentials[END_REF].

Big Data

Big Data is a collection of data characterized by high volume, variety (structured and unstructured) , and velocity (in terms of speed of access) so complex that it becomes difficult to process using traditional database management tools or data processing applications. Data sets grow in size in part because they are increasingly being gathered by information-sensing mobile devices, aerial sensory technologies (remote sensing, for instance RFID), software logs, video, cameras, microphones, radio-frequency identification readers, and wireless sensor networks.

Business Process Intelligence

Business Process Intelligence (BPI) is an innovative approach focused not only on measuring and reporting but also on managing the processes. through their data and workflows, differently to traditional Business Intelligence. BPI supports also analytics, with production of statistics, reports, and dashboards. They allow a better visibility of the processes. For example in the case of production, the analytics tools allow analyzing the KPI and help take actions.

Flexible integration Technology

As sensor technology and network technology develop, a computer system (or a machine) is becoming able to collect information about the real world by communicating with other machines without any help from human beings. In conventional IoT systems, servers for collecting and analyzing information from machines were developed individually. Now, virtualization and cloud technologies have enabled the IoT cloud or ZigBee network, in which all the functions such as communication with various devices and the collection, storage, and use of Big Data and BPI can be managed centrally. These clouds have the following features:

(1) Data collection and storage: An IoT cloud can communicate with any devices, collecting, storing data, and managing devices. A combination of multiple data sources can be analyzed easily thanks to a large amount of information available;

(2) Data analysis: The Big Data allows performing statistical calculations;

(3) BPI can operationalize the decisions in a flexible way. An example of the integrated solutions can be the use of a e-Kanban to replace the Kanban cards with RFID tags. This allows to fully automating the 'identification' of the status of the processes, with greater reliability and without operator intervention [START_REF] Drickhamer | The Kanban e-volution, Material Handling Management[END_REF] and [START_REF] Powell | Radio Frequency Identification, Inform[END_REF]. E-Kanban can reduce the levels of inventories of work in progress (WIP), maintain tight control, and integrate with suppliers. Another application is to use automated sensors, wireless networks and Big Data to update digital billboards. This allows organizations to implement what is called Andon and allows quick changes for messages [START_REF] Nicoletti | Lean and Digitize[END_REF]. The system may include the stop of production so the issue can be corrected. Some modern alert incorporate audio alarms, text, or other displays.

The final architecture is have the vision of where one plan. Each generation wi brings all the components together and reap

The Support of Automation

This architecture allows Data, and Business Process Intelligence

• RFID can bring the benefits of automatic sensing in processes.

• IoT can improve the flexible integration of the • Big Data can help in making the flow pulled by the customers, by allowing adding an intelligenc

• BPI can bring the benefits of the combination of Business Intelligence and the management of process workflows.

All together these technologies and the improvement of processes thanks to their Positioning/localization. the Lean Six Sigma approach

• Define and Measure, and Verify:

o Managing the life cycle of the components (that is tools); o Compliance; production so the issue can be corrected. Some modern alert incorporate audio alarms, text, or other displays. The final architecture is shown in Fig. 2. It takes time to build. It is important to ision of where one want to arrive and then build in a multi-generation plan. Each generation will bring substantial benefits per se. The final generation all the components together and reaps the synergies.

The Support of New Technologies to the Lean and utomation method

This architecture allows reaping the benefits of flexible integration. IoT, RFID , and Business Process Intelligence introduce additional benefits per se the benefits of automatic sensing in processes. can improve the flexible integration of the activities in the value stream Big Data can help in making the flow pulled by the customers, by allowing an intelligence of the demand of the customer; can bring the benefits of the combination of Business Intelligence and the management of process workflows. The new architecture can help especially in reducing some of the seven wastes, listed in Lean Six Sigma, such as waiting time, by the improvements in search times, waiting, and movements; reduction in repairs/errors/faults, thanks to the identification of non-standard components, any failure or theft, and help in the analysis of the root causes; reduction in stocks, by the identification of components not good to be phased out.

Case Studies

In order to explore the practical relevance of Lean and Automate, we report a summary of two case studies, which compare the situations where advanced technology is used versus when it is not used [START_REF] Dovere | Valutazione dell'efficienza delle machine utensili con parco utensili gestito tramite sistemi d'identificazione RFID[END_REF]. In both cases, engineering manufacturing companies were considered with each job being different from another one, each one with either one or few items. The products were dies for aluminum extrusion. In both cases, we analyzed the management of a tool inventory with 2500 tools for ten work stations for the processing and production of hot channels for plastic materials. In one of the companies, RFID tags were applied to the tool holder cone on machining centers. BPI software was used to analyze and manage processes. The possible inefficiencies in the tool management on the side of the machines are their possible damage, the times for the data entry and setting up the tools. The possible inefficiencies on the side of the tools are the times to find the suitable tool, the reduction in the useful life of the tools, oversizing of the tool inventory, improper choice of the tools, and loss of historical information. The advanced technology can impact on all the inefficiencies listed above. Quantitative benefits are the savings and the improvement in the Overall Equipment Effectiveness -OEE, a hierarchy of metrics to evaluate how effectively a manufacturing operation is utilized [START_REF] Smyth | Exploring the usefulness of a conceptual framework as a research tool: a researcher's reflections[END_REF]. OEE is based on three separate but measurable components: Availability, Performance, and Quality. Leaving aside the details of the two cases, the use of advanced technology brought the reduction in the number of tools in the inventory, in the unscheduled events and errors in the data entry; the possibility of implementing policies of tool sharing among the machines; the choice of the right tools: the reduction in the machine downtime doing in parallel the operations and the search of the right tools, the reading of the part program and the presetting. From a quantitative point of view, the advanced technology brought an increase in the average value of the OEE per each machining center of 27% [START_REF] Dovere | Valutazione dell'efficienza delle machine utensili con parco utensili gestito tramite sistemi d'identificazione RFID[END_REF].

Conclusions

Lean and Automate responds to the lack in the literature of a consistent method that manages and integrates the classical activities of streamlining production and logistics processes with the activities of flexible automation. Our research implies: Proposition 1: Unlike the traditional production and logistics context, where the Lean Six Sigma method requires a reduction of digitization, automation is essential for the improvement of the processes, especially to make them flexible; Proposition 2: Automate a process not streamlined is counterproductive.

The practical examples in this paper represent a starting point for the application of advanced automation to enable the deployment of Lean and Automate throughout the manufacturing and logistics processes. More examples are necessary. Further research should investigate the applicability of advanced technology for the optimization of fields such as production scheduling by using real-time information

Macro Phase 1 :Fig. 1 . 2 :

 112 Fig. 1. The Lean

Fig. 2 .

 2 Fig. 2. The Final Architecture.

This is a modified version of the Lean and Digitize method presented in[START_REF] Nicoletti | Lean and Digitize[END_REF] to take into account the peculiarities of the manufacturing environment.

http://en.wikipedia.org/wiki/Productivity_improving_technologies_(historical)

One example of standard for interfaces is for instance XLM and OAGIS, as powered by OAGi (http://www.oagi.org/dnn2/, accessed 10 March 2013).