Sumit Raut
email: sumit.raut@tcs.com

Muralidharan Somasundaram
email: muralidharan.somasundaram@tcs.com

Finding Optimal Resources for IT Services

Keywords: IT services, Queuing, resource allocation, delay, waiting time

This paper studies the resource management problem in IT services, where service request arrives to resource management group (RMG) and RMG needs to allocate resources to a request for a service based on availability of resources and service requirement start date. We propose an approach to find optimal number of resources in the context of Poisson arrivals, and service times & lead-times are exponentially distributed. We provide an exact mathematical queuing model for optimal number of skilled resources needed based on waiting time cost, idle time cost and revenue from a customer service for a guaranteed service level agreement. We tested the model with real life data for various types of requirements and analytical results show the benefits of the proposed approach.

Introduction

IT services business is a knowledge intensive industry where highly skilled resources are needed to meet demand for day-to-day services. Typically customers arrive with their requests to resource management group (RMG) for their service for a particular booking date and RMG needs to provide the skilled resources for the given booking date. The customer request needs to go through two stages of a process: (i) keep the request in RMG server till booking date and (ii) RMG allocates skilled resources to complete the request. Early allocation of skilled resources (i.e., before booking date) to request may lose opportunity to allocate resources of other possible future requests, i.e., paying wages while there is no realization of revenue and deterioration in service level. Delay in allocation of skilled resources indicates loss of demand resulting in revenue loss of service provider. Hence, it is important to estimate the right number of resources and active bench (safety stock) in dynamic IT services environment. The problem complexity increases when there is also possibility of substitution and multiskilling. This paper studies the resource allocation (or optimal bench) problem and applied queuing theory approach to determine the optimal number of resources which would also help to decide the active bench and number resources need to be trained for a particular skill. In past, several authors discussed the aspects of determining of optimal number of servers in a different stream of research. Some of the relevant literatures are described below: Grassmann [START_REF] Grassmann | Finding the Right Number of Servers in Real-World Queuing Systems[END_REF] states that: "In respect to real life, all models are approximations." He argues that it may not be practical to apply exact mathematical queuing models to real problems, and offers a number of practical methods for finding the number of servers in waiting line systems. Bassamboo et al. [START_REF] Bassamboo | Design and Control of a Large Call Center: Asymptotic Analysis of an LP-Based Method[END_REF] offer a dynamic model for determining the number of agents and the assignment of arrivals to agents in large call centers using an asymptotic approximation approach, and Borst et al. [START_REF] Borst | Dimensioning Large Call Centers[END_REF] consider an M/M/s system with a large number of servers (s), and propose an asymptotic approximation for finding the optimum staffing levels. Radmilovic et al. [START_REF] Radmilovic | Optimal Number and Capacity of Servers in M x=a /M/c(∞) Queuing Systems[END_REF] provide a method to determine optimum number of servers and optimum server capacity with considerations of bulk arrival rate and estimate the impact in cost with changes of number of servers, group size and bulk arrival rate. Fazel et al. [START_REF] Fazel | An Alternative Approach for Determining the Performance of M/M/s Queuing Model[END_REF] presents an alternative approximate method for analyzing large M/M/s systems, which is highly accurate, and considers both the cost of customer waiting and the staffing cost to develop a model that determines the optimum number of servers that would minimize the total cost. Yang et al. [START_REF] Yang | Optimal Resource Allocation for Time-Reservation System[END_REF] studies the optimal resource allocation in time-reservation systems where customers arrive at a service facility and receive service in two steps; in the first step information is gathered from the customer, which is then sent to a pool of computing resources, and in the second step the information is processed after which the customer leaves the system. They used dynamic programming approach to determine near-optimal number of processors follows a step function with as extreme policy the bang-bang control. They also provide new fundamental insights in the dependence of the near-optimal policy on the distribution of the information gathering times.

All the above works consider the system where jobs are serviced in First-Come-First-Serve (FCFS) order, and how the optimal number of servers varies across workloads. In addition, these queuing models does not consider real-life constraints (except Yang et al. [START_REF] Yang | Optimal Resource Allocation for Time-Reservation System[END_REF]) and impractical to use them for large service centers or industry.

There are few researchers who have been used queuing models successfully for a variety of real-life problems. Some of the real-life solutions examples are for a variety of organizations including police patrol scheduling (Kolesar el al. [START_REF] Ittig | Planning Service Capacity when Demand is Sensitive to Delay[END_REF]), staffing of supermarkets (Ittig [START_REF] Kolesar | A Queuing-Linear Programming Approach to Scheduling Police Patrol Cars[END_REF]), tele-retailing (Andrew et al. [START_REF] Andrews | Establishing Telephone-Agent Staffing Levels through Economic Optimization[END_REF]), and call centers (Artalejo et al. [START_REF] Artalejo | Applications of Maximum Queue Lengths to Call Center Management[END_REF]). These models are commonly used by service organizations to determine the number of servers through trial and error. The available queuing models, however, become exceedingly complex and cumbersome for analyzing large service centers, and the trial and error approach becomes impractical. Therefore, finding the optimum number of resources in a large service industry to provide optimal level of service for different customers at different point of time needs special attention in research.

The main difference between the existing literature and our work is that we aim to (1) optimize the resource allocation cost, idling cost and revenue from servicing customers through effectively releasing resources and estimating right number of resources and (2) satisfy SLA (Service Level Agreement) constraint on the delay of a job, where delay is not same as waiting time. This is in contrast to the aforementioned works which have not focused on resource release time while obtaining number of skilled resources needed for a guaranteed SLA guarantee.

We organized the papers as follows: In Section 2, we describe the model formulation. Section 3 describes the simulation study and results. Conclusions and future directions of research are provided in Section 4.

Model Formulation

The resource allocation of a service request in IT industry has done through two stages (refer Fig. 1): (i) In the first stage, RMG receives requests and allocates the resource to service as per starting date of service; (ii) In the second stage, resources perform the service as per request/competency. The first stage of resource allocation process of RMG is represented by the queuing system where request is arriving with Poisson process (X) in infinite number of servers (since, RMG only need to store the information in this stage) with infinite capacity systems and request leaves the system once it reaches at start-date of service. The time between requests follows exponential distribution (Y) with mean 1/. The service time / stay time of a request in first stage is followed exponential distribution with mean as average lead time (1/µ 1 ). The requests' depart from the first stage would be allocated to second stage of RMG process, i.e., the arrival process of request to second stage is same as departure process of first stage. Therefore, the departure process is D = Y + L and the distribution is as follows:

(1)

The expected value of departure process:

(2)

The second stage of resource allocation process of RMG is represented by the queuing system where request arrival process as request departure process from first stage, i.e., follows hypo-exponential distribution and service time follows exponential distribution with mean 1/µ. Based on the queuing model, we approximate the system in G/M/1 queuing model (this can be expanded for G/M/S queuing model and for different customer priority (Adan et al. [START_REF] Somasundaram | Queuing Theory[END_REF])). The delay time distribution of this model is given below (Adan et al. [START_REF] Somasundaram | Queuing Theory[END_REF], White et al. [START_REF] White | Analysis of Queuing Systems[END_REF]):

(3)

Where

, is a moment of request arrival process in second stage. Moment of request arrival process in second stage of RMG process is as follows:

(4) Now, the equation ( 4) is represented as [START_REF] Borst | Dimensioning Large Call Centers[END_REF] The expected delay time of the system is given below: [START_REF] Grassmann | Finding the Right Number of Servers in Real-World Queuing Systems[END_REF] The distribution of conditional delay time W|W>0 is as follows: [START_REF] Fazel | An Alternative Approach for Determining the Performance of M/M/s Queuing Model[END_REF] The expected value of conditional delay time W|W>0 can be obtained as: [START_REF] Ittig | Planning Service Capacity when Demand is Sensitive to Delay[END_REF] From the equation ( 5) and ( 8), the unique root is calculated as follows: [START_REF] Kolesar | A Queuing-Linear Programming Approach to Scheduling Police Patrol Cars[END_REF] Functional relationship of service rate ( with unique root ( ): From the equation ( 5), we can write the equation as follows: [START_REF] Radmilovic | Optimal Number and Capacity of Servers in M x=a /M/c(∞) Queuing Systems[END_REF] Suppose, , and

Then, the equation ( 10) can be written as: [START_REF] White | Analysis of Queuing Systems[END_REF] Since at weekly level service rate ( ) ≤ 1, average lead-time (1/ ≥1 and the actual arrival rate ( ≥1for IT service industry (in most project cases), then, B≤ 1 and the value of (A + B) is always greater than or equal to AB. Therefore, k can be written as [START_REF] Yang | Optimal Resource Allocation for Time-Reservation System[END_REF] Now, the relation between service rate and unique root is as follow:

(13)

Determination of Optimal Number of Server or Service Rate

Allocating too many resources results in decrease waiting time but comes at high allocation costs and allocating too few skilled resources leads to long waiting times. Therefore, the problem is formulated as follows:

Minimize the expected number of customer's delays and idle resources

) ( 14
)
Maximize revenue by service (Revenue per unit * Avg. customer depart from system) (15)

Where, W cost is average delaying cost, R cost is average resource idle cost per day (i.e., fixed cost per day (pay bill) incurs for each resource and this pay bill need to incur after finishing their work) and Rev is the average revenue for service per day. For priority customers, IT service providers are bounded by obligation to provide the service must. Therefore, we consider delaying time should not exceed t time-length with confidence level 95% as constraint as follows:

>0.95 (16)

The above optimization problem is solved using Gradient search method and produces local optimal solution or optimal number of resources.

Simulation Study

We consider the resource allocation problem for different competencies in IT service industry for simulation study. We capture six months data on different competency requests arrival (request date, start date) and allocation/service (fulfilled date) details for our analysis. From the above data set, we derive lead time (start daterequest date), delay (min (fulfilled datestart date, 0), and waiting time (min (fulfilled daterequest date, 0). We derive the service rate from the equation (8 & 9) using arrival rate, delay and lead-time information. We observed that there are more than 200 competency levels in dataset. We also observed that the variability is too high in each competency level and most of the cases data is intermittent. Hence, we group the competency as per their similarities, where one competency/skill can be substituted to other. In this way, the total number of competency groups is turned out fifteen. Some of the competency groups are: SAP-ERP, ORACLE-ERP, Programming, etc. The arrival patterns of some of the competency groups are given in Fig. 2.

Fig. 2. Arrival patterns of different competency

The mapping of queuing model to resource management problem in IT-service is measured by error in the estimated delay with actual delay observed. W real denotes the mean delay of serving of each competency group in IT-service process and W model denotes the mean delay (refer equation 8) which derived from two-stage queuing model. The mapping error % (MAPE comp ) for each competency is represented as shown below:

(17)

Fig. 3. Accuracy plot for queuing model accuracy

The mapping accuracy% (100 -MAPE comp ) is calculated using equation ( 17) and results are shown in Fig. 3. The above results showed that the estimation of delay using queuing model is highly accurate. On an average accuracy percentage is 90%. We also tested the waiting time distribution derived from the model with actual data of each competency group. The results show that at 95% confidence level, the difference of waiting time distribution between model and actual data is not significant.

The above analysis suggests that the queuing modeling approach can accurately map the resource management problem in IT-service. Therefore, we used this approach to determine the optimal number of resources needed for IT-service industry based on different cost (holding cost of resources, delay cost) and revenue parameters (revenue for each service). This paper considers the cost ratio between idling/holding cost & delay cost is 1: 7 (ID ratio) and average revenue for service per day & idling cost is 1: 10 (RI Ratio). The total cost using equations (14 & 15) are derived for different number of resources (or service rate) for each competency group and the curve for total cost versus service rate (for ERP-Oapps competency) is shown in Fig. 4. The optimal number of resources for each competency group is determined using equations (14, 15 & 16) and the percentage of cost reduction using optimal number of resources for each competency shown in Fig. 5. The results show that the benefit of using optimal resources is on an average 10% reduction in cost. Overall, the performance of queuing model is found very satisfactory. We have also experimented different scenario based on different cost ratio. The results of optimal number of servers using equations (14, 15, & 16) against ID ratio are shown in Fig. 6.

Fig. 6. Optimal service rate versus ID ratio

The above results show that the service rate needs to increase exponentially with the decrease of ID cost ratio.

Conclusions and Future Directions

Overall, this paper provides a queuing modeling approach to find an optimal number of resources for IT services business. An exact mathematical formulation is derived for optimal number of resources determination. The queuing modeling approach is tested in real-life data from IT services business. The model fitness is tested and showed the accuracy of the model for mapping IT services business. The queuing approach provides the optimal number of servers and showed the effectiveness of the mathematical approach using improvement in total cost reduction. Though, this paper considers the issues of lead time and delay in allocation in IT services business, there are other issues, such as, multi-skilling servers, customer priority, need to be considered in queuing modeling. Therefore, the present approach can be extended for multiskill resources and exact mathematical formulation need to be derived. Based on the cost of multi-skill and single-skill resources, optimal ratio of multi-skill vs. single skill determination can be considered as future directions of research. Also, the changes of resource's shape/skill level due to training need to be considered for future direction of research.

Fig. 1 .

 1 Fig. 1. Resource Allocation Process

Fig. 4 . 5 .

 45 Fig. 4. Total cost versus service rate Fig. 5. Cost reduction % different competency

* Room: 4B-14, TCSL, BIPL, Sector-V, Salt lake, Kolkata -700093


Simone Zanoni
email: simone.zanoni@ing.unibs.it

Laura Bettoni
email: laura.bettoni@ing.unibs.it

Christoph H Glock
email: glock@bwl.tu-darmstadt.de

Karin Giersch

Energy implications in the single-vendor single-buyer integrated production inventory model

Keywords: JELS, joint economic lot size model, energy efficiency, energy consumption, single-vendor single-buyer, lot sizing

Increasing energy efficiency is one of the main objectives of the Directive 2009/28/EC of the European , which aims at decreasing greenhouse gas emissions by jointly increasing the use of renewable energy and improving energy efficiency, all by 20% until 2020. To reduce energy consumption in producing and distributing products, it is of major importance to consider energy consumption in the whole supply chain and for all activities associated with production and distribution. For this reason, this paper studies a single-vendor single-buyer integrated productioninventory system and explicitly takes account of energy consumption. The use of energy is weighted with a cost factor and evaluated together with classical production-inventory costs. We find that if energy costs are considered together with traditional cost components, then the inventory costs of the system increase slightly in the optimum, but the total costs of the system decrease and we observe great energy savings.

Introduction

Reducing energy consumption is an important measure for firms to lower costs and increase competitiveness, especially in energy-intensive sectors. In trying to reduce the use of energy in industry, a coordinated approach of multiple stages of the supply chain is more promising than individual, uncoordinated actions taken by the members of the chain. The benefits of coordinated production and replenishment decisions have frequently been studied in the past. So-called joint economic lot size models [START_REF] Glock | The joint economic lot size problem: A review[END_REF] extend the classical EOQ model to take account of additional cost factors, such as the costs of backorders and lost sales or quality-related costs. Costs associated with the use of energy have, to the best of the authors' knowledge, not been considered in supply chain models before, apart from a preliminary investigation reported in [START_REF] Bettoni | Energy implications of production planning decisions[END_REF].

In many industries, energy is one of the most important resources. Preserving energy is the best way to ensure a reliable and sustainable energy supply and to reduce greenhouse gas emissions. Energy can be saved by introducing higher levels of energy efficiency throughout the supply chain, which includes the generation of energy, its transmission and distribution as well as an efficient end use.

This work aims at integrating the use of energy in the classical productioninventory costs of a single-vendor single-buyer supply chain. The intention is to investigate the behavior of the main decision variables in the presence of energy costs. The developed model helps managers in the tactical decision on how to coordinate production, distribution and storage in a supply chain to meet economic and environmental (strictly linked to the total energy spent) targets.

2

The system

This study considers an integrated production and inventory system with a singlevendor and a single-buyer, as illustrated in Figure 1.

In this paper, we assume that energy consumption in the supply chain can be influenced by changing the production rates of the vendor and by determining the size of the warehouses used at both parties. Both decisions influence the use of energy in the supply chain by determining how much energy the machines of the vendor require for processing a product, and how much energy is used for operating the warehouse. The use of energy in production has been studied by [START_REF] Gutowski | Electrical Energy Requirements for Manufacturing Processes[END_REF], who showed that different production rates result in different levels of energy consumption. In warehousing, especially for products that need particular conditions for proper storage (e.g. food or perishable items), the dimensions of a warehouse directly impact the use of energy, e.g. due to the need to light or cool the facility [START_REF]Greenhouse Gas Impacts of Food Retailing: Final Report[END_REF]. Thus, the consumption of energy is influenced by the lot size decision, and it can be weighted with a cost factor and added to the traditional production-inventory costs. The following notation will be used throughout the paper: q: quantity transported per delivery [unit/delivery] q max,v : maximum level of stock of the vendor that can be kept, which is subject to the dimensions of the warehouse q max,b : maximum level of stock of the buyer that can be kept, which is subject to the dimensions of the warehouse

A 1 :
Q: lot size [unit/lot] = n•q e: energy cost [€/kWh]
a, b: coefficients that depend on the specific production system (machine) of the vendor α, β: coefficients that depend on the vendor's warehouse characteristics γ, δ: coefficients that depend on the buyer's warehouse characteristics TrC: traditional total cost (cost per time unit [€/time unit], a function of n and q) EC: energy costs (cost per time unit [€/time unit], a function of n and q) TC: total costs considering (TC=TrC+EC).

Moreover, we assume that p>D, i.e. the production rate is higher than the demand rate and stock-outs or shortages are not allowed.

Model

In the following, we develop a model that considers the total costs of producing and distributing a product in a single-vendor single-buyer supply chain and that explicitly takes account of energy costs. We then compare the traditional singlevendor single-buyer system to the system that takes account of energy consumption. The total costs of the traditional single-vendor single-buyer system are formulated as the sum of setup costs, order costs and inventory holding costs for the vendor and the buyer.

In the traditional model, the total inventory costs (TrC(q,n)) are a function of the lot size and the number of shipments, and they are calculated as follows [START_REF] Hill | The single-vendor single-buyer integrated production-inventory model with a generalised policy[END_REF].

𝑇𝑟𝐶 𝑞, 𝑛 = 𝐴 ! + 𝑛 • 𝐴 ! • 𝐷 𝑛 • 𝑞 + ℎ ! • 𝐷 • 𝑞 𝑝 + 𝑝 -𝐷 • 𝑛 • 𝑞 2 • 𝑝 + ℎ ! -ℎ ! • 𝑞 2 . (1)
Energy costs are formulated as the sum of energy costs during production at the vendor and energy costs caused by storing the products in warehouses at the vendor and the buyer.

As was shown by [START_REF] Gutowski | Electrical Energy Requirements for Manufacturing Processes[END_REF], energy consumption during production can be divided into fixed energy consumption (costs), which is independent of the production rate, and variable consumption (costs). Fixed energy costs are caused by auxiliary tools of machines which are switched on during standby and which consume energy although the machine is not producing. Variable energy costs, in turn, depend on the production volume that is processed per unit of time. Both types of energy consumption are illustrated in Figure 2. The fixed component is not affected by a change in the production rate or an increase in the volume of the warehouse, while the variable component increases as the production rate of the machine is increased. A number of studies have been carried out to benchmark energy uses in warehouses [START_REF]Greenhouse Gas Impacts of Food Retailing: Final Report[END_REF]. Based on this empirical evidence, especially for refrigerated warehouses, the relationship between SEC and the volume of the warehouse can be modeled as in Figure 4. Thus, the SEC decreases both in the production rate as well as in the size of the warehouse. In this work, the energy consumption of the production process and the warehouse are formulated with the help of the SEC, which is a well-accepted variable for measuring energy consumption and energy costs. All types of energy consumption, named SEC Vp for the specific energy consumption at the production line of the vendor, SEC Vw for the specific energy consumption at the warehouse of the vendor and SEC Bw for the specific energy consumption at the warehouse of the buyer, are modeled as follows:

𝑆𝐸𝐶 !" = 𝑎 • (𝑝) !! (2) 𝑆𝐸𝐶 !" = 𝛼 • (𝑞 !"#,! (𝑞, 𝑛)) !! (3) 𝑆𝐸𝐶 !" = 𝛾 • (𝑞 !"#,! (𝑞)) !! (4)
The maximum inventory at the vendor is

𝑞 !"#,! (𝑞, 𝑛) = 𝑞 • 𝑛 • 1 - ! !
, while the maximum inventory at the buyer equals 𝑞 !"#,! (𝑞) = 𝑞. Energy costs are calculated as the energy consumption of the production process, i.e. the specific energy consumption multiplied with the demand and the energy cost (EC Vp ), and the energy consumption of the warehouses, calculated as the specific energy consumption multiplied with the maximum size of the warehouse and the energy cost (EC Vw , EC Bw ), as in the following formula:

𝐸𝐶 !" = 𝑆𝐸𝐶 !" • 𝐷 • 𝑒 (5) 𝐸𝐶 !" = 𝑆𝐸𝐶 !" • 𝑞 !"#,! (𝑞, 𝑛) • 𝑒 (6) 𝐸𝐶 !" = 𝑆𝐸𝐶 !" • 𝑞 !"#,! (𝑞) • 𝑒 (7)
The total energy cost is the sum of the three components given in Eqs. ( 5) to [START_REF] Glock | Batch sizing with controllable production rates in a multi-stage production system[END_REF]:

𝐸𝐶(𝑞, 𝑛) = 𝐸𝐶 !" + 𝐸𝐶 !" + 𝐸𝐶 !" (8)
If energy costs are considered in addition, the total costs are a function of the lot size and the number of shipments, and they are calculated as:

𝑇𝐶 𝑞, 𝑛 = 𝐴 ! + 𝑛 • 𝐴 ! • 𝐷 𝑛 • 𝑞 + ℎ ! • 𝐷 • 𝑞 𝑝 + 𝑝 -𝐷 • 𝑛 • 𝑞 2 • 𝑝 + ℎ ! -ℎ ! • 𝑞 2
+ 𝑎 • 𝑝 !! • 𝐷 • 𝑒 + 𝛼 • 𝑄 • 1 - 𝐷 𝑝 !!!! • 𝑒 + 𝛾 • 𝑞 !!!! • 𝑒. (9)
The optimal values that minimize TrC(q,n) are (q * ,n * ), while the optimal parameters that minimize TC(q,n,) are (q ** ,n ** ). Since it is not possible to use the first order conditions to minimize Eq. ( 9), a numerical analysis is necessary to investigate the behavior of the system.

Numerical Analysis

To study the behavior of the model, we performed a numerical analysis to investigate how the model parameters influence the optimal solution of the energy model. We performed the numerical analysis for a company in the dairy sector of Parmesan production and aging. The high energy consumption in this sector is due to the conditioning of the warehouse, in which milk and cheese are stocked.

Table 1 summarizes the data that was used in the analysis. The results of traditional model (minimizing Eq. ( 1)) and the energy model minimizing Eq. ( 9)) are summarized in Table 2. The results illustrate that the proposed energy model leads to a higher profit than the traditional model which does not consider energy costs. The total costs and the energy costs are lower in the energy model than in the traditional model, even if inventory carrying costs are higher. Figure 5 illustrates the trends of the total costs and its two main components as functions of the lot size Q. Figure 6 illustrates the behavior of the two main cost components while varying the production rate. As can be seen, the result is that both cost components (EC and TrC) increase in the production rate, but that the increase in TrC costs (difference between dashed black line and dashed gray line) for the case where the optimal parameters considering energy consumption (q ** , n ** ) are used is much less than the decrease in EC (difference between continuous black line and continuous gray line) while adopting the same parameters.

Conclusion

This study proposed a variation of the traditional single-vendor single-buyer production inventory model by considering energy cost components for production and for warehousing of items (at the vendor and at the buyer). To model these cost components, we used specific relationship that were found in empirical studies. In particular, the production rate mainly affects the specific energy consumed during the production process, while the size of the warehouse (which is a result of the batch sizing policy) mainly affects the specific energy consumed for properly storing the items at the vendor and at the buyer. The main finding of the study is that it is possible to reduce total costs while accounting for energy components and to obtain consistent saving in energy components, while only a small increase in traditional inventory carrying costs has to be accepted. Moreover, the results of this study are particularly important for energyintensive production processes in which the production rate may be set to a particularly high value.

This study has several limitations. First, it considered a very specific functional relationship to model the impact of the production rate on energy consumption. Other studies have shown that a varying production rate leads to a different pattern of resource consumption [START_REF] Glock | Batch sizing with controllable production rates[END_REF][START_REF] Glock | Batch sizing with controllable production rates in a multi-stage production system[END_REF], and this may be valid for energy consumption as well. It is obvious that if a different functional relationship is assumed, the results of the model may change. Secondly, this study did not take account of all processes in a supply chain that consume energy (e.g. in [START_REF] Zanoni | Chilled or frozen? Decision strategies for sustainable food supply chains[END_REF] the effect on lot sizing decisions of different temperature settings in a cold supply chain is studied). Accomplishing these two and other aspects in an extension of this paper would be helpful to further our understanding of the impact of energy consumption and energy costs on the production and distribution policies of supply chains.

Fig. 1 .

 1 Fig. 1. System considered in the analysis

Fig. 2 .Fig. 3 .

 23 Fig. 2. Energy consumption as a function of the production

Fig. 4 .

 4 Fig. 4. Specific Energy Consumption as a function of the warehouse size.

Fig. 5 .

 5 Fig. 5. Energy and traditional costs as functions of the lot size Q

Fig. 6 .

 6 Fig.6. Energy and inventory costs for the cases where the optimal parameters of the traditional model and the optimal parameters of the proposed energy model are adopted

Fig. 7 .

 7 Fig. 7. Specific Energy Consumptions as functions of the ratio of the demand and the production rate

Table 1 .

 1 Data used in the numerical analysis

	Milk necessary	14 [l/kg cheese]	h2=	0.15 [€/ton year]
	Weight	8 [kg/Cheese round]	α=	392.15
	Volume occupied	30 [Cheese round/m 3 ]	β=	0.256
	p=	2÷50•10 6 [l/year]	γ=	392.15
	D=	2•10 6 [l/year]	δ=	0.256
	A1=	2,000 [€/set-up]	a=	25.354
	A2=	400 [€/order]	b=	0.29
	h1=	0.1 [€/ton year]	e=	0.12 [€/kWh]

Table 2 .

 2 Results of the numerical analysis

	Q [kg	TC	TrC	EC
	cheese]	[€/year]	[€/year]	[€/

year] Energy storage [TOE/ton] Energy production [TOE/ton] Total TOE/ton

	Min TrC	21,381	840,682	36,757	803,924	3.70	0.33	4.03
	Min TC	3,506	370,629	132,989	237,640	0.86	0.33	1.19


Renato Perrotta

Oduvaldo Vendrametto

Development of Engineering Competencies in Brazil and Innovation Policies, an Overview of the Automotive Sector

Keywords: lack of engineers, engineering competence creation, Brazilian innovation programs, competitiveness in the automotive sector

As consequence of the economic growth, it is observed the increase of the demand for engineering professionals. The problem of scarcity of engineering manpower is being faced as one of the main obstacles to the continuity of the Brazilian economic growth. The natural demands of infrastructure and industry allied to the fact that engineers are also recruited to occupy nonengineering positions constitute, themselves, already a difficult equation to solve. Besides, along the years, the Brazilian's scientific production in the engineering field has demonstrated to be very shy. Such combination, either in macro or in micro-economy, reflects in the innovation and competitiveness. The objective of this research is to discuss actions to create engineering competencies in micro-economy that could attenuate those structural problems. Additionally, this work aims having an overview of those actions combined with the new regulatory policy for the automotive sector announced in April/12, which integrates the program "Inovar-Auto".

Introduction

Recently, concerns about the scarcity of engineering manpower in Brazil became object of researches as per Gusso and Nascimento [START_REF] Gusso | IPEA -Radar N. 12 -Tecnologia[END_REF] and Nascimento et al [START_REF] Nascimento | IPEA -Radar No 6 -Tecnologia[END_REF]. World indicators of education appoint that in Brazil less than 5% of the graduates come from engineering areas, which seems to be a very low figure, while, OCDE members, in average, 12% of its graduates come from this field. Moreover, countries like South Korea have 23% of its graduates in engineering; Japan has 19% and Russian Federation 18% [START_REF]OCDE Indicators[END_REF]. Nowadays, the country contributes with around 2% of the world's scientific production [START_REF][END_REF] and, more specifically, in the engineering subject area, the Brazilian scientific production shows to be very shy, with a number of publications around 2300 documents (Table 1), far behind countries like the USA with around 45000 documents, Japan with 14000, South Korea with 9000 and other BRIC countries:

India with 6000, Russian Federation with 3500 and the impressive number of 63000 from China [5]. A wealth of a nation is determined by its internal productiveness, but, principally through its own capacity to create value, besides of the resources available to be applied. So forth, the growth of a country is no longer sustainable while prevail poor or inexistent policies oriented to the productivity. In other terms, a country should prosper if its people have the right and updated capabilities and skills that enable determined specializations, therefore wealth and growth will be limited concomitantly if skills are merely for some [START_REF] Porter | The Competitive Advantage of Nations[END_REF].

The surplus of US$ 29.8 billion of the Brazilian Trade Balance in 2011, indicates a figure 47.9% higher than 2010 [7], but, a deep analysis shows that 63% of the country exports structure has been based in Food (31%), Agricultural raw material (4%), Fuels (10%), Ores and Metals (18%); whilst Manufactures represents the rest 37% [START_REF]World Development Indicators[END_REF], indication that the nation is losing its competitiveness, once in the year 2000, Manufactures represented 58% of the country's exports structure [START_REF]World Development Indicators[END_REF].

Regarding the Automotive sector, in global perspective, Brazil has already secured its position as one of the main players, through an internal market over 3.6 million units and internal production over 3.4 million units, in 2011 [START_REF] Da Anfavea | Indústria Automobilística Brasileira[END_REF]. For the internal economy, the sector has expressive importance with nearly 20% share in Industrial GDP [START_REF] Anfavea | [END_REF]. However, in deeper investigation, it is possible to find out that the Brazilian automotive industry competitiveness has been based in premises of weak economic pillars. First, the national production is substantially dependent of the internal market, circa 2.7 million units, which represents 81% of the production share [START_REF] Da Anfavea | Indústria Automobilística Brasileira[END_REF]. Second, the imports increased in one year from 18.8% to 23.6% of the market share [START_REF] Da Anfavea | Indústria Automobilística Brasileira[END_REF]. Third, the Brazilian trade balance, positive between the years 2002 and 2007, dropped from US$5251 in 2005 to US$(5404) million in 2010 [START_REF] Anfavea | [END_REF]. Finally, the market share of vehicles with engines over 1000cm3, which means, vehicles with higher added value than the ones that the market has been based, have increased contemporarily with the market share of the imports [START_REF] Da Anfavea | Indústria Automobilística Brasileira[END_REF]. The consequences of the loss of competitiveness can be observed in the Figure 1, which presents a modest increase in production during the period of 2007 to 2011, compared to other BRIC countries, for instance, China and India. That reflected in the position that Brazil historically has occupied as the 6th major world vehicles producer.

Objectives

The objective of this research is to present actions oriented to create engineering competencies in micro-economic perspective, face to the present structural scenario. Furthermore, this work aims the combination of such actions with the new regulatory policy announced for the automotive industry, through the program "Inovar-Auto", valid to the period between 2013 -2017 and that integrates the program "Brasil Maior", which seeks social and economic growth across the mobilization of the productive forces and industrials, focusing the innovation and the competitiveness of the country, both to the internal and to the external markets, through the establishment of a set of industrial, technological, trade and services policies.

Methodology

The In addition, it focuses the analysis and comprehension of program "Inovar-Auto", identifying and determining opportunities for engineering competence creation.

All of this in order to create subsidies that demonstrate the importance to develop engineering competencies, mandatory to foment innovation through researches & developments, innovation one that, enables to create value and differentiation of the country goods. Notwithstanding, the definition of a model, proper to enlarge the development of the human capital and competencies, based, for example, in Asian nations, which have supported the rapid growth that have allowed the technological development for innovation [START_REF] Kim | Technology, learning, and innovation: experiences of newly industrializing economies[END_REF].

Results and Discussion

The numbers of the Brazilian automotive industry in the first months of 2012 demonstrate that the model of concentration of its production to supply the internal market is achieving its limit. The number of vehicles produced in Brazil in the period January to May -2012 is 9.5% lower compared to the same period of 2011, 5.9% lower compared to 2010 and 7.9% lower, when compared with 2008 (Table 2). In fact, the numbers of automobiles produced at this period, surpass only the production achieved in the year 2009, time when the automotive sector experienced the collapse and major automakers like GM and Ford had to go through restructure and very few countries like China and India (Figure 1) had growth in this industry. The strength that allowed the country attenuating the effects of the world economic crisis initiated in 2008 is no longer a fruitful strategy. The internal market sales of the period January to May (Table 3) dropped down to volumes even lower than the ones observed in the first months of 2009 when the country has faced the effects of the world crisis.

Table 3. Brazilian Internal Market -Vehicle Sales from Jan to May -years 2008 to 2012. Adapted from [START_REF] Da Anfavea | Resultados de Maio e de Janeiro-Maio de[END_REF], [START_REF] Anfavea | Dados Relativos a 2010 -Produção por Tipo, Empresa e Modelo[END_REF], [START_REF] Anfavea | Dados Relativos a 2009 -Produção por Tipo, Empresa e Modelo[END_REF], [START_REF] Anfavea | Dados Relativos a 2008 -Produção por Tipo, Empresa e Modelo[END_REF]. Moreover, along the years the country did not take the necessary actions to improve its infrastructure, especially the ones with positive impacts over logistics of transportation of goods. Similar inertness can be observed in the internal bureaucracy, so forth, in the amount and in the burden of the taxes that still prevail. Labor costs, as well as, other direct and indirect costs have increased tremendously.

Period

On the other hand, the level of skills, competencies, such as the efficiency have not grown in same levels. Face to the presented scenario of loss of competitiveness and lack of skilled and competent resources, authorities decided to create the program "Brasil Maior", a set of policies that focus the competitiveness, productivity of the country industry, by supporting and encouraging the increment of the technology of the processes and value chain, all of this, in order to create a proper scenario for investments and for the innovation [START_REF]Brasil Maior -Inovar para competir. Competir para crescer[END_REF].

In respect to the automotive sector, dedicated attention has been observed, the program "Inovar-Auto", a part of the program "Brasil Maior", provides incentives to the technological innovation and the densification of vehicles productive chain to the period between 2013 and 2017. The aim is to support the technological development, the innovation, the safety, the environmental protection, the efficiency and the quality of automobiles, trucks, busses and auto-parts locally produced. Companies already installed and producing in the country, such as companies that have plans approved to install production plants in the Brazil may take advantage, from January/13, of IPI deduction (VAT over industrial production) based on quarterly outlays made in the country in: researches; technological developments; innovation; strategic inputs; tooling and qualification of suppliers [START_REF] Da República | de Abril de[END_REF]. The technological capacity of a nation is a cluster of abilities, experiences and efforts, allowing companies take benefit and, from this capacity, improving and creating new technologies [START_REF] Kim | Technology, learning, and innovation: experiences of newly industrializing economies[END_REF]. By electing strategic areas, Brazilian authorities seek to boost the innovation and R&D initiatives to increment the country competitiveness. Tax reliefs may contribute and force competence creation in micro-economy.

To develop a learning process, the studies appoint to a model based on competence development, as per Kim [START_REF] Kim | Technology, learning, and innovation: experiences of newly industrializing economies[END_REF] [19] and Ulrich [START_REF] Ulrich | Recursos Humanos Estratégicos: novas perspectivas para os profissionais de RH[END_REF].

According to the model, Knowledge initiates from the Cognitive Perception (Knowwhat), which is the base knowledge obtained through education and schooling. Despite the fact that this is a pivotal knowledge, other, more advanced ones are required for competitive advantage [START_REF] Ulrich | Recursos Humanos Estratégicos: novas perspectivas para os profissionais de RH[END_REF]. That is the kind of knowledge that a nation, at least, should care about. Advanced Skills (Know-how) are necessary for any productive activity; it is the ability to turn knowledge into practice, the capacity of applying learned disciplines in real and complex problems [START_REF] Kim | Technology, learning, and innovation: experiences of newly industrializing economies[END_REF], [START_REF] Kim | Imitation to Innovation: the dynamics of Korea's technological learning[END_REF], [START_REF] Ulrich | Recursos Humanos Estratégicos: novas perspectivas para os profissionais de RH[END_REF].

Finally, the Systemic Comprehension (Know-why) is the inclination of understanding the principles of a technology. It is the most advanced knowledge that allows professionals thinking beyond the execution, pro-acting in cause-effect analysis [START_REF] Kim | Technology, learning, and innovation: experiences of newly industrializing economies[END_REF], [START_REF] Kim | Imitation to Innovation: the dynamics of Korea's technological learning[END_REF], [START_REF] Ulrich | Recursos Humanos Estratégicos: novas perspectivas para os profissionais de RH[END_REF].

The tax benefits shall contribute to develop competencies, even in face of a scarcity of engineering human resources, they could have important role, and their own history can determine technological efforts across its objectives [START_REF] Kim | Imitation to Innovation: the dynamics of Korea's technological learning[END_REF].

The nation can adapt the model adopted in South Korea, that thru its industries, individuals have developed the necessary technological skills [START_REF] Kim | Imitation to Innovation: the dynamics of Korea's technological learning[END_REF].

Although individual enterprises are one of the keys for the technological activity, the capacity of a nation is more than a sum its companies' skills and competencies, in individual perspective. It comprehends extra-market systems of networks and the relation between companies, the way of do business and the presence of development institutions [START_REF] Kim | Technology, learning, and innovation: experiences of newly industrializing economies[END_REF]. Companies shall handle competencies through methods of competence analysis and management, defining own criteria and steps to achieve know-how / know-why levels.

In any case, technology shall not be obtained uniquely through a radical R&D, the learning process can be developed adjacently to reverse engineering, imitation, licenses, cooperation agreements [START_REF] Kim | Imitation to Innovation: the dynamics of Korea's technological learning[END_REF], [START_REF] Andreassi | Gestão da Inovação Tecnológica[END_REF].

The example of innovation of Japan should also be a reference, since, there, the competence is obtained through innovation, incrementally, so that an innovation becomes subsidy to the next [START_REF] Kim | Imitation to Innovation: the dynamics of Korea's technological learning[END_REF], [START_REF] Nonaka | The knowledge-creating company[END_REF], [START_REF] Nonaka | Hitotsubashi on Knowledge Management[END_REF].

Fig. 1 .

 1 Fig.1. Countries Vehicles Production Statistics. Adapted from[START_REF] Oica | Production Statistics[END_REF]

  methodology applied for this research is based in bibliographic review, based upon data of the Brazilian automotive sector, that have been obtained through the ANFAVEA annual report (National Association of Auto-motor Vehicles Producer), across the world development indicators -World Bank 2012 , global education figures of 2010 -OCDE (Organization for Economic Co-operation and Development), IPEA (Institute of Applied Economic Research) publications regarding engineering -2010 and 2011, ranking of global scientific production and scientific production in the area of knowledge engineering -SJR (SCImago Journal & Country Rank, by drawing a parallel with other bibliographic references.

Table 1 .

 1 Top 20 World Scientific Production -Year 2010 -Subject Area: Engineering.

				Adapted from [5]
	Ranking Country Documents	Citable documents	Citations	Self-Citations	Citations per Doc-ument	H index
	1	China	63361	62857	26110	18570	0,4	157,0
	2	United States	44983	42787	40246	22501	0,9	448,0
	3	Japan	13716	13382	7073	3516	0,5	181,0
	4	Germany	10270	9821	9126	3920	0,9	197,0
	5	United Kingdom	9956	9348	9342	3621	0,9	221,0
	6	South Ko-rea	8733	8550	6657	2635	0,8	129,0
	7	France	8233	7944	7221	3019	0,9	180,0
	8	Canada	7452	7174	6527	2186	0,9	180,0
	9	Taiwan	6666	6501	4882	2143	0,7	123,0
	10	Italy	6457	6188	5803	2393	0,9	160,0
	11	India	6065	5887	4316	1826	0,7	115,0
	12	Spain	5003	4833	4992	2133	1,0	127,0
	13	Australia	4220	4015	4235	1449	1,0	137,0
	14	Iran	3916	3819	3737	2018	1,0	63,0
	15	Russian Federation	3540	3498	1102	506	0,3	85,0
	16	Netherlands	3021	2888	4121	1294	1,4	153,0
	17	Poland	2588	2517	1153	540	0,5	75,0
	18	Turkey	2480	2397	1986	772	0,8	83,0
	19	Singapore	2430	2354	3291	1036	1,4	122,0
	20	Brazil	2355	2298	1444	577	0,61	90

Table 2 .

 2 Vehicles Production in Brazil from January to Mayyears 2008 to 2012. Adapted from[START_REF] Kim | Technology, learning, and innovation: experiences of newly industrializing economies[END_REF],[START_REF] Da Anfavea | Resultados de Maio e de Janeiro-Maio de[END_REF] [14],[START_REF] Anfavea | Dados Relativos a 2009 -Produção por Tipo, Empresa e Modelo[END_REF].

			Vehicles Production
	Period
		JAN	FEB	MAR	APR	MAY
	2008	255,2	254,0	283,7	302,5	293,9
	2009	184,9	204,4	275,1	253,9	269,5
	2010	233,4	236,6	318	274,8	297,3
	2011	238,9	294,6	295,1	282	304,2
	2012	211,8	217,8	308,5	260,8	280,8


Gerrit Bogdanski
email: g.bogdanski@tu-braunschweig.de

Malte Schönemann
email: m.schoenemann@tu-braunschweig.de

Sebastian Thiede
email: s.thiede@tu-braunschweig.de

Stefan Andrew
email: s.andrew@tu-braunschweig.de

Christoph Herrmann
email: c.herrmann@tu-braunschweig.de

An Extended Energy Value Stream Approach Applied on the Electronics Industry

Keywords:

In today's manufacturing companies lean production systems are widely established in order to address the traditional production objectives such as quality, cost, time and flexibility. Beyond those objectives, objectives such as energy consumption and related CO2 emissions gained relevance due to rising energy costs and environmental concerns. Existing energy value stream methods allow the consideration of traditional and energy related variables. However, current approaches only take the energy consumptions of the actual manufacturing process and set-up times into account neglecting non-productive operational states and technical building services related consumption. Therefore, an extended energy value stream approach will be presented that provides the necessary degree of transparency to enable improvements of the energy value stream of a product considering also the influence of product design parameters.

Introduction

The rising environmental awareness in society as well as worldwide increasing prices for natural resources and energy impose higher pressure on the manufacturer of goods than ever before. During the last 20 years an increase in energy prices of up to 100% has been recorded in Germany [1]. To cope with these cost advantages, companies have to identify measures to lower their energy consumption while maintaining their throughput. To accomplish the challenge of identifying and reducing the energy consumption within the production environment, the principles of the lean management have been adapted and extended towards this topic and a "lean and green" philosophy has been developed. From a methodological perspective the value stream mapping has been extended in order to identify the main energy consumers in a production line -the energy value stream mapping [START_REF] Erlach | Energiewertstrom -Der Weg zur energieeffizienten Fabrik[END_REF], [START_REF] Shahrbabaki | Green and Lean Production Visualization Tools -A Case Study exploring EVSM[END_REF]. In this paper the printed circuit board (PCB) manufacturing will be used as a case study for the methodological developments. PCBs are used in most electronic devices. The manufacturing of PCBs is a complex and energy intensive process. As about 88% of the worlds PCBs production is situated in Asian countries and only approximately 5% of the production located in Europe [START_REF]World PCB Production Report for the Year[END_REF], this imposes a high cost pressure on the PCB producers in Europe. A possible cost reduction can be achieved by reducing the energy demand of the production system. While there has already been done a comprehensive research work on recycling of PCBs [START_REF] Wang | Prioritizing material recovery for end-of-life printed circuit boards Original Research Article[END_REF], [START_REF] Guo | Recycling of non-metallic fractions from waste printed circuit boards: A review[END_REF] as well as in terms of life cycle assessment for PCBs and PCB using products [START_REF] Taiariol | Life Cycle assessment of an integrated circuit product[END_REF], [START_REF] Kanth | Comparative Study for Environmental Assessment of Printed and PCB Technologies[END_REF] the energy consumption aspects in the PCB manufacturing have not been addressed so far.

Background

Manufacturing processes transform inputs into value-added outputs. Energy consumption is a physical necessity to perform that transformation. Gutowski et al. have shown in early studies that the actual consumed energy for machining processes is exceeded by the energy demand for related auxiliary processes like coolant pumps, lubrication supply and technical air ventilation [START_REF] Gutowski | Environmentally benign manufacturing: Observations from Japan, Europe and the United States[END_REF], not mentioning the indirect energy drawn by technical building services such as heating, air conditioning or air suction. Additionally, Devoldere et al. are stating that in energy assessments of manufacturing processes the time aspect cannot be neglected. Specific time studies have shown that less than 13% of energy expenditures have been utilized for productive operations [START_REF] Devoldere | Improvement Potential for Energy Consumption in Discrete Part Production Machines[END_REF]. This clearly indicates the need to approach the assessment of energy demand of production systems by considering also the dynamic behaviour of machines. As depicted in Fig. 1, the dynamic load profile of a manufacturing process can be broken down over time by energy analysis, clearly stating, that all defined operating states of the given process are varying in their mean power demand, timing and therefore also in their energy amounts (indicated by the shaded areas). To cope with these dynamic energy demands, a suitable assessment of energy performance can be applied as proposed by Zein [START_REF] Zein | Transition towards Energy Efficient Machine Tools[END_REF]. And feedback using key performance indicators (KPI) can be given. One effective way to assess the energy demand of production lines is a tool adapted from lean production. The energy value stream mapping (EVSM) is an extension of the lean production tool value stream mapping (VSM). VSM is utilized to analyse and prioritize sources of waste as listed by Womak and Jones [START_REF] Womack | Lean Thinking: Banish Waste and Create Wealth in Your Cooperation[END_REF] in order to enable future process improvement efforts through eliminating them by a continuous improvement processes. Extending methods for value stream mapping are presented by several authors. The U.S. Environmental Protection Agency (EPA) has given a recommendation to enhance the widely known lean process data by process specific energy use data [START_REF]US EPA: Lean, Energy & Climate Toolkit: Achieving Process Excellence Through Energy Efficiency and Greenhouse Gas Reduction[END_REF]. Erlach and Westkämper are proposing a rather more detailed assessment of process energy. Next to the lean process variables the calculated energy intensity (EI) of the specific process [START_REF] Erlach | Energiewertstrom -Der Weg zur energieeffizienten Fabrik[END_REF] is displayed.

In terms of lean KPI, both methods are using similar syntax and structure. While, the EPA is giving recommendations to include energy usage data without clearly stating how to ascertain the energy intensity KPI, Erlach and Westkämper are clearly allocating the mean energy demands of the processing state to the main energy carriers of production environments (electrical power, compressed air volume flow and gas volume flow) and giving them a common physical unit of measurement (kW) in order to allow an easier comparison. The common KPI for energy intensity (EI) is calculated by Erlach and Westkämper by summing up all mean power demands of the process specific energy carriers and multiplying this sum with the specific customer takt time and the number of resources [START_REF] Erlach | Energiewertstrom -Der Weg zur energieeffizienten Fabrik[END_REF]. The idle time of the processes is considered indirectly in the EI indicator. A drawback of this is the lack of visibility of the impact of the unproductive and productive time shares in matter of energy intensity as well as the influence of supporting processes from technical building services side. As shown earlier in Fig. 1, the power demand for idle states (non-productive) and processing (productive) can differ significantly. Additionally, the influences of product design parameters on the process design and scheduling are not represented in current approaches, as they represent the basis for a truly co-evolving energy-aware product and process design. Therefore, an approach is needed that is able to differentiate between different states of a production process and is able to indicate influences from product design perspective in order to truly derive hot spots of energy intensity and trigger improvement measures into the right direction.

Concept

Fig. 2 shows the conceptual framework of the extended energy value stream mapping approach. In a first step, the basic procedure is similar to the (energy) value stream mapping approaches mentioned above -from the perspective of one product (family), it is necessary to identify related processes and collect relevant data on time and energy related indicators. However, three innovative characteristics are introduced here:

Dynamics of energy consumption (A)

As indicated above, the pure consideration of single (average or nominal) values in energy value stream mapping impedes a closer look into composition of consumption and, thus, the derivation of improvement potentials for all relevant forms of energy. Therefore, the proposed approach distinguishes between three different machine states: ramp-up, processing and idle. The necessary data is being obtained through power measurements which provide power (P) profiles of the considered processes, respectively machines as shown in Fig. 1 exemplarily for a drilling process. Besides the power for ramp-up, idle and processing also the necessary time (t) values can be directly derived from the measurement as shown. The proposed method takes into account a specific production scenario in order to derive the necessary time values. As indicated in equation (1) the base is an available production time (e.g. per day) in which the machine can basically be used for production.

Integration of further relevant consumption portions (B)

In general, manufacturing process chains are embedded within a factory system which includes technical building services (TBS). TBS are not value adding in itself but provide necessary forms of energy and media (e.g. compressed air generation, coolants) and conditions (e.g. lighting) for enabling production [START_REF] Hesselbach | Energy Efficiency through optimized coordination of production and technical building services[END_REF], [START_REF] Seow | A framework for modelling energy consumption within manufacturing systems[END_REF]. They can sum up to a significant share on energy demand of manufacturing companies, but were not considered in energy value stream mapping so far. Within the proposed extended EVSM approach TBS related energy demand can be either considered as • related to specific processes respectively machines (e.g. air suction), or as an • energy overhead with relevance for the whole process chain. This distinction is made based on the technical circumstances but also depending on data availability while often a process specific breakdown is simply not available with reasonable effort. For the first case this indirect energy demand is directly added to the energy demand of the specific processes (TBS*). In the second case the energy demand of the TBS is equally distributed over all products within the considered time frame. The necessary energy demand rate and the time value for the specific TBS system is obtained through measurements or estimations. According to all those influences, e Conceptual framework of the extended energy value stream mapping approach quation [START_REF] Erlach | Energiewertstrom -Der Weg zur energieeffizienten Fabrik[END_REF] shows the calculation of the energy demand for the defined period of time. For the specific energy consumption, this value is divided by the amount of produced units within this timeframe.

Consideration of product characteristics (C)

Product design may have significant influence on manufacturing energy demand since already in the design phase necessary processes and partly also process parameters are determined. However, besides few publications for the case of metal machining (e.g. [START_REF] Narita | Environmental burden analysis due to high speed milling[END_REF], [START_REF] Nawata | Life-cycle design system for machined parts -linkage of LCI data to CAD/CAM data[END_REF], [START_REF] Seow | Improving Product Design based on Energy Considerations[END_REF], [START_REF] Thiede | Energy Efficiency in Manufacturing Systems[END_REF]) just little work has been done to systematically address those interdependencies so far. Some tools provide environmentally related information to product designers already, but this information is mostly related to, e.g. used materials or recycling issues. Against this background, the proposed approach consciously integrates product related issues in energy value stream mapping. Therefore, for each process it is questioned to which extend the energy demand is influenced by product design characteristics. This paper specifically focuses on printed circuit boards (PCB) -in this case the amount of PCB layers (single-sided, double-sided or multi-layered), the PCB geometry in terms of size (how many PCB fit in one panel) and the number of electronic parts (separated according to SMD and THT parts) are relevant product related parameters. Whereas design parameters are mainly related to later functionalities of the product, the proposed approach provides manufacturing related information to the designer in order to create cost-and eco-efficient product designs.

Application

The extended EVSM method was applied exemplarily for the real case of a PCB value chain consisting of a PCB manufacturing and PCB assembly company, covering 46 sequential processes. In the selected scenario, the available production time per day is ten hours, the availability is assumed to be 100% and only one product type is produced. The maximum number of panels (products) per day is limited by the capacity of the bottle neck process. Since some processes can handle several panels at once, the bottle neck is not necessarily the process with the longest processing time. It is further assumed that no parallel process equipment is available and that the production system is in steady state. For each process, energy and time related parameters were determined and the energy demand was calculated. The process related indirect energy demand for air suction and compressed air and light was added to the process' energy demand if allocation was possible. Otherwise the indirect energy demand is summed up to the energy overhead of the process chain. In Fig. 3, the approach is exemplarily shown for the processes drilling and reflow soldering (the load profiles of the drilling process is shown in Fig. 1) for the fabrication of single-sided PCB with 50 SMT and 50 THT parts. Drilling has a processing time of 50 seconds for one panel and reflow soldering requires 390 seconds for four panels, hence 97.5 seconds per panel. The total energy demands of the drilling process and reflow soldering process for one PCB are 0.04 and 0.36 kWh respectively. However, the energy consumption characteristics of these processes differ. While the power demand of the CNC drill is 1.06 kW during processing and 0.42 kW during idle, the reflow oven demands 8 kW constantly. Further indirect energy usage for air suction and compressed air is caused by the drilling process while 2.3 kW of energy demand for air suction is related to the reflow soldering process. Knowledge about these differences in energy consumption of production processes is important for the derivation of improvement measures. Therefore Fig. 4 presents the energy demand for each operational state of all 46 processes as well as for related TBS. It shows that several processes cause high energy consumption during idle mode. These processes with long idle times and high power demand during idle mode should be subject for production management, e.g. the energy consumption could be reduced by different scheduling strategies such as batch production. these processes as well as the energy consumption increases with the number of layers. The energy demand per PCB for different types of PCBs is shown Fig. 5a). Further, the process times for the assembly of SMT and THT parts on the board increase with the number of parts. While the SMT assembly is performed by a machine the THT parts are manually mounted. The analysis shows that the total energy consumption is not increasing significantly with an increasing number of SMT or THT parts due to the low energy demand of assembly equipment. However, with a higher number of THT parts the THT assembly becomes the bottle neck process and the energy consumption per PCB increase due to a reduced output of PCB, as shown in Fig. 5b). The energy demand per PCB also depends on the number of PCBs per panel (ranging from 3.8 to 6.2 kWh), which is restricted by the geometry and size of each single board.

Summary and Outlook

Altogether the extended energy value stream is a powerful method which takes into account more realistic circumstances and provides valuable information for both manufacturing engineers and product designers. It gives very clear indications about the composition of energy demand according to all relevant subsystems of the factory and the different operation states of involved equipment. Based on this detailed analysis, a specific derivation of technical and organizational improvement measures becomes possible. The application of the approach further shows the influence of certain product properties on the energy demand of the production system. This information can be used during product development in order to evaluate design options regarding the trade of between higher production cost (due to energy consumption) and development effort. It is also the ideal base for product related cost or carbon footprint calculations. Future work will focus on the validation of the method on different production scenarios in other industries. Moreover the results of the case study indicate the importance of the co-evolutional development of product design and production system. Therefore the relevant dependencies have to be investigated and new ways and tools for the coordination and communication need to be established.

Acknowledgements. The introduced methodology was developed within the research project EnHiPro (Energy and auxiliary material efficiency in production) funded by the German Federal Ministry of Education and Research (BMBF) within the framework concept "Research for Tomorrow's Production" (02PO21201) and managed by the Project Management Agency Karlsruhe (PTKA).

Fig. 1 .

 1 Fig. 1. Electrical load profile of a drilling process broken down into productive and nonproductive energy shares

[

  01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00

Fig. 2 .

 2 Fig. 2. Conceptual framework of the extended energy value stream mapping approach Through subtracting the calculated daily ramp-up time (number of ramp-ups per day multiplied by single ramp-up time, see equation) and processing time values (number of products multiplied by single processing time, see equation), a good estimation of idle time per day can be derived., ,

Fig. 3 .

 3 Fig. 3. Exemplary Extended Energy Value Stream Mapping for drilling and reflow soldering

Fig. 4 .

 4 Fig. 4. Energy demand of each operational state and TBS of all 46 manufacturing processesBesides specific energy consumption profiles of the processes, the influence of product properties on energy consumption is considered. For double-sided or multilayered PCBs extra processes are required compared to single-sided PCBs; some of them also need to be repeated for each additional two layers. Thus the utilization of

Fig. 5 .

 5 Fig. 5. Energy demand for one PCB for single-sided, double-sided and multi-layered boards with four, six and eight layers (a) and for different numbers of SMT and THT parts (b)


Kuowei Chen

Richard Lee Storch
email: rlstorch@uw.edu

Mass Customized Large Scale Production System with Learning Curve Consideration

Keywords: Mass customization, large scale production, learning curves, shifting bottleneck procedure, simulation

This paper presents the structure for modeling a large scale production system with learning curve considerations. The model will be used to develop a scheduling method that facilitates the production performance for mass customized products. Mass customization is an important manufacturing management strategy but it might lead to unnecessary production losses. Most manufacturing systems' throughput is constrained by one or more bottlenecks and the critical bottleneck may shift from one work station to another. The proposed scheduling method will consider learning curve effects and employ the concept of Shifting Bottleneck Procedure to guide production scheduling decisions. Ultimately, the goal is to improve the throughput of large scale assembly manufacturing systems. A simulation model of a wind turbine assembly line case study is also presented to validate the capability of the proposed method.

Introduction

This paper focuses on large scale product industries which are known to produce or assemble large scale products. Examples are commercial airplanes, ships, and wind turbines. A large scale production system often consists of a group of structured organizations to produce a family of complex and customized products in a certain geographic region [START_REF] Lu | Modeling customized product configuration in large assembly manufacturing with supply-chain considerations[END_REF]. There are several crucial characteristics among these industries and the nature of a large scale production system is influenced by its products. The most common characteristics of a large scale production system are long cycle time, low total throughput and limited buffer space. For instance, the total assembly cycle time of a wind turbine nacelle may be up to sixteen hours to be completed and the throughput is limited to five units per week. The buffer space or inventory space may be limited. Long cycle time leads to the limited total production volume in the large scale product manufacturing industry which means total throughput, the average output of a production process per unit time, is limited. In order to improve the total throughput, manufacturing engineers are striving to develop more efficient production process methods.

Mass customization (MC) has been a well known contemporary manufacturing industry management technique, enabling a company to compete with their competitors in the diverse market. Broadly, the concept of mass customization was first introduced by Davis [START_REF] Davis | From "future perfect[END_REF] and he described that manufacturing companies tended to produce goods to serve their relatively large market; meanwhile, satisfying specific needs of individual consumers by customizing products with acceptable cost. However, it is not always free to implement product customization in a production line and the common impact of mass customization on manufacturing trade-offs are important system performances: cost, process time, quality, delivery and volume [START_REF] Squire | The impact of mass customisation on manufacturing trade-offs[END_REF]. Such challenges need to be overcome by those companies who are struggling to achieve mass customization and must consider those performance factors. In spite of a large number of practical approaches and processes for customized product discussed in the literature [START_REF] Silveira | Mass customization literature review and research directions[END_REF][START_REF] Ulrich | Product Design and Development[END_REF][START_REF] Huang | Linking learning and effective process implementation to mass customization capability[END_REF], a link between product customization and bottleneck based heuristic with learning curve consideration is still a research area that needs investigation.

This paper proposes the use of learning curve consideration in a bottleneck selection scheduling heuristic to improve the total completion time or system throughput under mass customized large scale product industry. Manufacturers can improve the total throughput by decreasing process time in the bottleneck stage. Those customized products with the similar process on a certain machine are defined as having learning effect when those products are scheduled consecutively. As a result, the customized production system will be closer to the mass production system and then improve the total completion time and throughput. We give a description of bottleneck based heuristic and learning curve effect in Section 2. Section 3 demonstrates the use of learning curve in a bottleneck based scheduling heuristic. Section 4 provides a simulation example which applied the method under a simulated large scale production industry. Section 5 presents the conclusion and future work.

Background

Bottleneck based heuristic

Manufacturing involves many operations on many machines and effective management requires many decisions. There have been many classical scheduling theories for the deterministic problem of scheduling a fixed number of jobs on a given set of machines to improve the production performance such as minimizing makespan, minimizing tardiness, and minimizing lateness. The classical job shop problem can be described as a set of machines that process operations on jobs. Each job has a predefined operation schedule on each machine with the required process time on it [START_REF] Blazewicz | The job shop scheduling problem Conventional and new solution techniques[END_REF]. These algorithms can be classified as optimization and approximation algorithms [START_REF] Jain | Deterministic job-shop scheduling Past, present and future[END_REF][START_REF] Wenqi | An improved shifting bottleneck procedure for the job shop scheduling problem[END_REF]. Among those approximation algorithms, Adams et al. [START_REF] Adams | The Shifting Bottleneck Procedure for Job Shop Scheduling[END_REF] proposed the Shifting Bottleneck (SB) procedure and it is probably the most well known among all heuristics for job shop scheduling problem. A brief statement of the Shifting Bottleneck procedure is as optimizing the sequence on each machine repeatedly, while keeping the sequence of other machines fixed. Each one machine solution is compared with the all others and the machine is identified as the bottleneck machine based on their solution. The SB procedure terminates once all machines have been scheduled. It is generally characterized by following steps to minimize the makespan: bottleneck identification, bottleneck selection, and sequence re-optimization.

Learning curve effect

Learning effect has received considerable attention in management science and it can be described as a steady decline in process time when operators perform the same task repeatedly. Dr. Wright [START_REF] Wright | Factors Affecting the Cost of Airplanes[END_REF] first introduced his famous log2 learning curve model in 1936 and this model is still one of the best ways to predict operator's performance. Biskup [START_REF] Biskup | Single-machine scheduling with learning considerations[END_REF][START_REF] Biskup | A state-of-the-art review on scheduling with learning effects[END_REF] might be the pioneer to consider the connection between learning effect and scheduling problem and proved that single-machine problem with the consideration of leaning effects remains polynomially solvable for two objectives which are the minimized deviation from a common due date and minimized sum of flow time. Then he introduced a modified formulation to describe the learning effects in scheduling problem with , 1,...,

i p i n 
being the normal process time and ir p as the process time of job i if it is scheduled in position r in a sequence (1) as below:

a ir i p p r  (1)
where 0 a  is the learning index, given as the logarithm to the base 2 of the learning rate, and thus for the 80% hypothesis 2 log 0.8 0.322 a    holds. According to the equation above, the needed process time in position r decreases by the number of repetitions which represents that operators gain experience and skills after repetitive operations.

Lee et al. [START_REF] Lee | Minimizing total completion time in a two-machine flowshop with a learning effect[END_REF] discussed the goal of minimizing total completion time for the two machine flow shop problem. They assumed that the same job has different learning effect on different machines and this assumption is common for those multi-machine scheduling problems with learning consideration. A modified equation (2) with learning effect was introduced by Lee and Wu to represent ijr p as the process time of job i if it is scheduled in position r in a sequence on the machine j :

a ijr ij p p r  (2)
where 1,..., jm  is the machine index. With respect to the equation above, the needed process time in position r on machine j decreases by the number of repeti- tions, hence, different processes on each machine might affect the learning situation.

According to results in [START_REF] Biskup | A state-of-the-art review on scheduling with learning effects[END_REF][START_REF] Lee | Minimizing total completion time in a two-machine flowshop with a learning effect[END_REF][START_REF] Anzanello | Learning curve models and application: Literaure review and research directions[END_REF] scheduling problem with learning curve consideration is considered to have practical impact to manufacturing schedule decision. Besides, it has shown the capability to improve the mass customization production system.

Methodology

The method we propose is based on the concept of Shifting Bottleneck procedure [START_REF] Adams | The Shifting Bottleneck Procedure for Job Shop Scheduling[END_REF] with learning curve consideration in a large scale customized product production environment. The objective is to improve production performance such as total completion time or throughput by assigning products which have similar process on the bottleneck stage consecutively in order to reduce the process time by utilizing the characteristic of learning curve. Jobs are subject to the constraints that (i) the sequence of machines for each job is prescribed; and (ii) each machine can process only one job at a time. Besides, the processing time of each job might be effected by learning effect when similar processes are scheduled on the same machine consecutively and this causes the processing time of each job on each machine to be changed. Figure 1 presents a flow chart of schedule decision by using bottleneck selection with learning effect consideration.

We start by defining system variables. For instance, i denotes the serial number of job, j denotes the machine or working stage, and k denotes the customization cate- gory. Steps 1 and 2 are key steps in this procedure to determine which work station is the real system bottleneck by different criteria. We start to check whether there is any current processing job in the system. If not, the first job is allowed to access into the system according to different dispatching rules and we use first come first served rule here. Otherwise, each work station needs to be measured and analyzed in terms of bottleneck detection criteria. There are basically two well known methods to define the bottleneck in a manufacturing system. The first method is to measure utilization of each work station and the one with highest utilization is considered as a bottleneck. However, it might not be sensitive enough to detect the real bottleneck while there are more than one work stations with same utilization. The second method defines bottleneck by measuring the longest queue line of unprocessed jobs in front of each work station or longest waiting time of each work stage. It is also a simple method to observe the length of queue on each machine in a manufacturing production line. The drawback of this method is lack of accuracy when the maximum of buffer size is limited. The number of unprocessed jobs on more than one busy work stations is restricted by buffer size and this causes difficulty to define a real bottleneck. This is a common situation in the real life especially for large scale production system, for example, an airplane assembly line may just have one buffer space in front each stage. Therefore, we choose the largest waiting time as a bottleneck selection criterion in this paper:

1 0 2 1 1 1 1 1 max , ,..., n n n i i i i ij ij i i i t c t c t c i i i                  (3)
for all j ,where

Fig. 1. Steps of proposed scheduling procedure with learning effect

Once the bottleneck station is determined, we go to the next step to identify relevant job information in order to make schedule decision. Steps 3 and 4 should be performed at anytime to those unprocessed jobs in front of the main system. Under the customization production environment, the information of each job is assumed to be well prescribed according to customer's requirement; however, the assembly sequence can be assigned by the manufacturer to achieve flexible manufacturing. Next, this model accumulates the number for each option in the corresponding work station.

Once the bottleneck is determined in Steps 1 and 2, this model assigns those jobs which are in the same job family with most frequent number in the queue of unprocessed jobs corresponding to bottleneck stage with higher priority. The purpose of this step is to accumulate the same process on the bottleneck stage and then learning effect can be triggered to reduce the process time. This step always schedules those jobs with higher priority first to enter the main system unless the bottleneck changes or there are no more jobs defined as higher priority in the queue of unprocessed jobs in front of the main system. We go back to steps 1 and 2 to re-determine the new bottleneck immediately when the bottleneck changes. Otherwise, this model keeps on steps 3 and 4 to search for the most frequent job family and assign them with higher priority to access into the system if those jobs in the previous majority job family have been all processed to enter the main system. The final step implements the learning curve on the bottleneck machine to reduce process time when a job is still in the same job family as the previous one. There are numerous descriptions and practical applications of learning curve discussed in the literature [START_REF] Biskup | A state-of-the-art review on scheduling with learning effects[END_REF][START_REF] Roser | Shifting Bottleneck Detection[END_REF], however, the proposed heuristic in this paper uses the classic Wright's log2 learning curve model given a learning index 2 log 0.8 0.322 a    for the 80% hypothesis.

Simulation and Results

The proposed procedure in section 3 is applied in a simulated example for a wind turbine assembly industry and this model is carried out to analyze the simulation results using the ARENA simulation software. The example assumes a wind turbine company can offer its customers customized nacelles with 3 options ( 1...3 k 

) on 15 different categories ( 1,...,15 j  ) without supply shortage or other unexpected emergent situation such as labor absence or delay of delivery and the simplified process flow is presented in Figure 2. On the other hand, these jobs all need to process through 15 work stations to be completed with same process sequence in this complex system. There are a total of 700 unprocessed jobs with continuous arrival time and the information of each job is prescribed. This example assumes the buffer space of each work station is restricted to 2 as well as the bottleneck is determined by measuring the largest waiting time.

Fig. 2. Simplified process flow of nacelles assembly line

According to figure 3, the dash trend presents a schedule decision with traditional first come first serve dispatching rule (FCFS) which means a job is scheduled according to their arrival time. The earlier a job arrives in the queue in front of the main system, the earlier it can be processed into the main system. The solid trend presents a schedule decision using proposed scheduling procedure with learning curve consideration (LC) and it shows that the average completion time has been improved and reduced from around 4016 minutes to 3450 minutes in this wind turbine example. By following the proposed procedure, every job that arrives in the queue in front of the main system must be checked by the model to determine if it meets the criteria as a higher priority level. It seems that the stage 2 is defined as the primary bottleneck and it consists of sub-processes 13, 14, and 15. Figure 4 (a) (b) (c) depict the process time of individual jobs on each work station respectively. It can be easily seen that learning curve happens on all of three work stations, especially on station (13) and station [START_REF] Lee | Minimizing total completion time in a two-machine flowshop with a learning effect[END_REF]. This also indicates that a work station is determined as a critical bottleneck when there is an exponential decrease period influenced by learning effect. However, a bottleneck might go along with a short learning effect period and this just means the bottleneck changes to another work station quickly in a short time. Besides, learning effect may happen on a non-bottleneck work station according to random combinatorial sequence and this non-bottleneck work station can also benefit by the nature of learning effect. In the present paper, we propose a structure of modeling with learning effect under mass customization production system. A simulation model also is developed to demonstrate this procedure and analyze the results according to a simulated wind turbine assembly line example. The total completion time has a significant improvement by using the proposed scheduling procedure. This paper concluded that this heuristic has potential capability to improve the performance of a customized production system by assigning job family on bottleneck stage to reduce the process time. In addition, it also can provide manufacturers better scheduling decision support to satisfy customer's demand. Future development of the method may include (a) using different bottleneck detection method to determine a real bottleneck in a more accurate and sensitive way (b) comparing the result by different learning curve models, and (c) analyzing the influence of other objectives to the proposed heuristic such as minimizing tardiness or minimizing the variation of completion time. We selected the total completion time as the objective in this paper. Additionally, more complicated and therefore more realistic production systems will be considered.

Fig. 3 .Fig. 4 .

 34 Fig. 3. A comparison of predicted completion time

  is the completion time of job i on machine j .

	1... jm 	being machine index,	t denotes the arrival time of job i on
						ij
	machine j and
					3. Assign unprocessed unit i with
	Define system variables (i,j,k)				system bottleneck j higher priority if the unit is belong to the most frequent k of	No
					4. Schedule high priority unit	6. Availability of
	bottleneck and find 1. Define sub-process	Yes	the system with earliest arrival time to enter	Yes	assigned k unit with previous
	system bottleneck (j)
					The production system. (Trigger
					learning curve when stage
				processes same job consecutively)
	2. Bottleneck
	exists?
			No (FCFS)	5. Bottleneck changes?	No
	Yes

ij c