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Study of the Impact on Lean Supply Chain Improvement with Environmental Sustainability Consideration

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Abstract

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1. Introduction

Throughout recent decades, firms from all over the world have incorporated lean operation practice into their business strategy. On one hand, this enables the supply chain to reduce cost and improve quality at all levels of the production. On the other hand, it also increases their overall level of competitiveness in the market (Martínez-Jurado & Moyano-Fuentes, 2014). Lean practices are known for cost reduction activities starting from placing orders to suppliers up to the delivery to the customers. It subsequently eliminates waste, improves quality, and increases the flexibility at all levels throughout the supply chain (Womack et al., 1990; Womack and Jones, 1996). Operations management has become widely aware of the importance of operations outside of the borders of the firm. Coordination of operations along the supply chain has become a crucial factor in lean management. Lean practices are no longer considered solely internal and issues are addressed throughout the entire supply chain in order to gain the most beneficial results (Womack and Jones, 1996; Hines et al., 2004). Moreover, in order to even further increase competitiveness on the market, firms such as Samsung, Hewlett Packard, and Walmart have initiated the integration of green practices into their lean supply chains (Wahab, Mamun, and Ongkunaruk, 2011).

Nowadays, increased pressure from customers and society shifts the focus of the supply chain from performing economically, thus, enhancing lean capabilities to reduce the cost to simultaneously being environmentally sustainable. Moreover, the steady depletion of natural resources, evolutionary technologies, and the green awareness of customers on products obligate firms to transitions towards environmental sustainability (Gandhi, Thanki, Thakkar, 2018). The paradigm of a lean and green supply chain has evolved that needs to combine production effectiveness, efficiency, and environmental requirements at the same time (Ciccullo, Pero, Caridi, Gosling, & Purvis, 2018). However, the integration and balance of lean supply chain operations with environmental sustainability practices arose as a recent challenge for nowadays firms. For instance, while a buyer in the supply chain attempts to become leaner and eliminates inventory by just-in-time production that withholds a small order quantity, the buyer’s product supplier has to decreases lots size and increase transportation. In turn, this causes increased carbon

emission outputs. Therein, lean supply chain practices somewhat impact environmental

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Therefore, the overall impact of environmental sustainability on lean supply chain practices should be analysed more thoroughly in order to improve the overall effectiveness of the supply chain and sustainability of the planet.

The research field of analysing the exact influence and interrelation of a lean & green supply chain is considered to be at a rather early stage and has only been addressed by few in a quantitative analytical manner. Nevertheless, literature from the last two decades has suggested and implied a common view that lean supply chain practices may be able to accommodate the adoption of environmentally sustainable practices (Lamming, 1996; Simpson and Power, 2005; Corbett and Klassen, 2006; Carvalho et al., 2011). It is said that environmental supply chain management, in turn, may have a positive influence on lean supply chain practices (Dües et al., 2013). Some scholars have emphasised the mutual ease in an integration of lean and green practice since initially lean practices are partially green without explicit intention (Bergmiller and McCright, 2009b). However, a major lack still exists on the actual integration in practice of lean and green supply chain practices (Garza-Reyes, 2015).

In theory, Zhu, Zhang, and Tsung (2007) amongst others have focused on EOQ modelling the effects of quality improvements along the supply chain as a sub-aspect of lean management without environmental consideration. Others scholars have used the traditional and extended EQO model, linking inventory-related improvements on a total cost perspective to environmental sustainability issues such as carbon emission factors on the firms level (Battini, Persona, & Sgarbossa, 2014; Chen, Benjaafar, & Elomri, 2013; Bouchery and Jaber, 2012). This research will take the opportunity to extend the current literature by using the model of Zhu, Zhang, and Tsung (2007) as a foundation and further extend it by correlating environmental sustainability factors. Hereby, the main research question is as follows; What is the optimal lean production policy that reduces cost and incorporates environmental sustainability from the integrated supply chain perspective?

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that minimize total lean improvement cost and total carbon emission outputs. Furthermore, this research will conclude with managerial insights and implications that enable the integrated supply chain to better coordinate investments and balance their lean manufacturing practices regarding environmental sustainability. Thus, making lean supply chain practices and green strategies mutually compatible.

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2. Theoretical Background

In the first part of this section, the lean & green paradigm is discussed that nowadays supply chains are confronted with to accommodate the reduction of cost and be environmentally friendly. In the second part of this section, relevant literature is viewed that approaches this paradigm from a quantitative analytical perspective. Furthermore, we discuss the concept of the extended EOQ modelling approach and look at ways that enable this research to combine lean factors and environmental impacts into a coherent cost model for further analysis. 2.1 The Lean and Green Supply Chain Paradigm The general principle of the lean supply chain is to eliminate all sources of waste and to identify factors that do not create any added value to the product flow. The main focus lies on creating a product design and manufacturing process that allow reducing the overall cost to a minimum. Herein, the optimization of quality throughout the entire production process is a key element of achieving a lean supply chain (Martínez-Jurado & Moyano-Fuentes, 2014). Similarly to a green supply chain where the focus lays on waste reduction and a minimization of all environmental impacts. Therefore, reducing carbon emission of production and transportation throughout all stages of the supply chain. Despite the seeming compatibility of the two objectives, literature is yet not entirely conclusive, as both negative and positive correlation has been identified (Martínez-Jurado & Moyano-Fuentes, 2014). An increasing interest in literature can be noticed to evaluate the conflicts and complementariness of a lean and green supply chain. Ciccullo, Pero, Caridi, Gosling, and Purvis (2017), explain the lean and green paradigm as being competing, precursor, and complementary at the same time. Factors on both sides can have significant inter-dependency in practice. An instance of the paradigm can be seen as one looks at the increasing need for a high product replenishment frequency while attempting to be lean. At the same time, it increases transportation emission. But also simultaneously decreases inventory-holding emission. Moreover, more frequent transportation in the supply chain caused by the demand for smaller lot-size deliveries, resulting in greater pollution (Martínez-Jurado & Moyano-Fuentes, 2014). Non-conforming products, thus defects, caused by lacking production quality process and initial mismatching product design further increase transportation and disposal emission (Zhu, Zhang, and Tsung, 2007). The identification of these types of conflicts between lean capabilities and environmental objectives is an important part that needs to be understood by manufacturing supply chains. Eventually, the accommodation of these trade-offs is claimed to be key to develop a potential solution to a sustainable lean supply chain (Dües et al. ,2013; Govindan, Azevedo, Carvalho, & Cruz-Machado, 2013)

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2.2 Quantitative Modeling Approach with Environmental Considerations

Few scholars have researched these inter-dependencies from a quantitative analytical and numerical perspective. However, quantitative analysis on a cost perspective of the lean side of the paradigm has received significant attention throughout recent decades. A commonly known model practically implemented to achieve the optimization of cost regarding order quantities in the supply chain is the EOQ (Economic Order Quantity) method. The traditional EOQ model originally developed by Harris and further transformed by the contribution of many others into the dynamic approach. This model is used to determine the least cost intensive order- and production quantity. It determines the optimal values for the trade-off of inventory holding cost and ordering cost. To use and implement the EOQ model amongst others can be recognized as to achieve a leaner supply chain in terms of cost reduction. Many scholars have used the EOQ approach in different ways as the basic model formulation to extend and adapt it with more influencing cost factors.

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Herein, carbon emission factors can be connected to its source in the core model. The lean cost parameters are correlated by following the basic relation principles of carbon emission causes derived from the literature of Chen, Benjaafar, & Elomri (2013) and Hou, Lin, and Huang (2016). The research will consider the decisions of the buyer and supplier from centralized perspective, acting as one firm in the integrated supply chain. Herein, costs are focused on lean-related aspects only, such as inventory cost, defective production cost, as well as quality improvement cost. Other lean cost factors such as delivery, warranty, product specifications, and lead-time are not taken into consideration. Hereby, decisions can be made by the supply chain to determine order- and production quantity as well as product quality that accommodates leanness as well as greenness from a cost perspective. The extension of the model further enables the researcher to examine to what extent a consideration of environmental sustainability influences the manufacturing supply chain in its attempt to be as lean as possible, on a cost perspective.

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3. Model Description

A literature review was made in order to define the gap in the literature and to gain knowledge to develop a mathematical approach to this research. The extended EOQ model that accounts for cost of lean improvements in an integrated supply chain is used and further developed by creating correlations with its environmental impacts. The newly developed model expresses in terms of optimal order- and production quantity as well as production quality rate. It aims to balance lean improvement and environmental outputs on a cost perspective. The methodology used for this research follows the analytical quantitative research approach (AQR) of Zhu (2018). The model is developed by applying the following five components; Assumptions, Decision variables, Parameters, Objective function, and Constraints. Moreover, a section was included that describes the logic of the model.

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reduces cost and incorporates environmental sustainability from the integrated supply chain perspective. Eventually, Microsoft Excel is the software that is used to model and analyse the equation. With the support of the tool solver, the equation can be minimized and solved. The outcome can be used as generic insight for manufacturing supply chains and contribute to existing literature.

3.1 Assumptions

The assumption is made that the production of the product takes places constantly at a fixed rate of demand in units per year. The supplier always fulfills the buyer’s yearly demand. Also, the rate of the overall production at the supplier is always greater than the demand of the buyer. Since the decisions are made in an integrated supply chain are centralized and the supply chain acts as

one firm we assume that the supplier’s production quantity is always equal to the buyer’s order

quantity. Moreover, it is assumed that the production process can never be perfect and therefore to some extent defective production still occurs. Both the non-conforming unit rate and out-of-control rate determines the quality level of production. The lower limit of the out-of-control rate as well as non-conforming units produced is set to be 0,05. This boundary sets the lowest possible defect rate of 0,25% of overall defective production. Furthermore, it is assumed that the overall product quality level maintains at the same rate when no investments are made. Whenever investments are made by the supply chain the overall product quality level always increases. Herein, as lower the rate of non-conforming units and production out-of-control rate as higher the quality level of the production process and eventual product. We further assume that every cost factor considered in the supply chain to some extent causes carbon emission

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𝜇* = The optimal rate of the out-of-control state in the production that minimizes lean cost and emission cost. 3.3 Parameters To develop the lean and green integrated supply chain model the following input notations are adopted: D= Demand units per year (constant) H= Buyer’s inventory-holding cost per unit per year K= Fixed ordering cost for each order placed with the supplier k= Setup cost for each run of production r= Units produced at supplier’s facility in units per year h= Supplier’s inventory-holding cost per unit per year s = Cost incurred by producing a non-conforming product (defects) per unit SC!= The supply chain’s cost to improve the percentage of non-conforming units produced in the out-of-control state SC!= The supply chain’s cost to improve the mean time of production in the out-of-control state

𝛼 = Initial percentage of non-conforming units produced in the out-of-control state after the buyer has invested in improving quality, hence reduced parameters

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Inventory-Related Cost The Buyer’s inventory related cost; 𝐂𝑩𝒊= !"!+ !"! and the supplier’s inventory related cost; 𝐂𝑺𝒊= !"! + !!"!! gives the supply chain’s yearly inventory related cost; 𝐂𝑺𝑪𝒊 =(!!!)!! + !"! + !!"!! Looking at the inventory-related costs, whenever the buyer places an order with the supplier a fixed ordering cost K occurs. And the buyer has to tackle a constant demand rate D unit per year. K is the fixed ordering cost the buyer has to pay every time placing an order with his supplier. This cost is multiplied by the overall yearly demand, which then is the cost of ordering every product one by one. Therein, it is divided by a choice of order quantity to gain the cost that resulted from ordering a certain quantity from the overall yearly demand. Hence, the number of placed orders with the supplier on a yearly basis, multiplied by the fixed ordering cost. The buyer also incurs an inventory holding cost H per unit per year. H is the cost the buyer has to pay for every unit stored at its storage location, thus it is multiplied by the choice of the quantity that is ordered, which at the same time is the amount that is received at the buyer’s storage at one point all at once. However, 2 divides this cost since the amount holding in the storage at the buyer is gradually depleting to zero until a new choice of order quantity arrives at the buyer’s storage. Since in practice the buyer’s warehouse on a yearly basis is averagely half full.

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and hence related inventory holding cost that is to be accounted for. Once again, multiplied by the order quantity divided by 2 that determines the average inventory level eventually gives the related holding cost. Inventory-Related Emission The buyer’s inventory-related emission cost; 𝐂^𝑩𝒊 = !!! + !!! and supplier’s inventory-related emission cost; 𝐂^𝑺𝒊 = !!! + !!"!! gives the supply chain’s inventory-related emission cost; 𝐂^𝑺𝑪𝒊 = (!!!)!! + !!! + !!"!! Herein, similarly to the model formulation of Chen, Benjaafar, & Elomri (2013), emission cost can be associated and related to the aforementioned inventory related cost for lean improvements. This results in green costs associated to lean improvements and expressed in carbon emission cost of warehousing at the buyer. Carbon emission cost caused for each order placed with the supplier denoted as 𝐾. Furthermore, carbon emissions cost that is caused for each unit held in inventory per year denoted as 𝐻. 𝐾 is the fixed emission cost for each order placed with the supplier that are associated with ordering a chosen quantity and thus computed in the same manner as lean inventory cost. 𝐻 is emission cost that is produced for each unit held in the buyer’s storage per year. The amount of inventory stored, hence the order quantity will determine the emission caused in the storage.

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Quality Cost

The integrated supply chain’s quality cost; 𝐂𝑺𝑪𝒒= !"#$%!!

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operates in the out-of-control state 𝜇. (𝛼, 𝜇) is the so-called status quo since no investments in quality improvements are made yet. In practice, the buyer may invest in the design of his product in a way that suits the production processes of the supplier better and/or assign inspection personnel at the supplier's production facility to increase quality control. The supplier may invest in upgraded production technology and/or preventive maintenance actions. These actions, therefore, reduce the aforementioned quality-related parameters. However, a mentioned the cost of the buyer and the cost of the supplier are equal as the act as one firm. Meaning, in the integrated supply chain, the decision always falls for the cheapest quality improvement option that the supply chain needs to account for. Other than Zhu, Zhang, and Tsung (2007), in this research, we combine the buyer’s and the supplier’s investment parameters as the integrated supply chain makes a decentralized decision and investments are made together. Therefore, the

supply chain’s cost to improve the percentage of non-conforming units produced in the out-of-control state 𝑆𝐶!

and the supply chain’s cost to improve the mean time of production in the out-of-control state 𝑆𝐶! will give the total cost of the integrated supply chain’s annual quality improvement. Herein, the supply chain can decide to invest in any quality improvement to reduce quality related parameters from (𝛼, 𝜇) to (𝛼, 𝜇). Further investments in a reduction of the quality parameters by the supply chain will result in the final denotation of the quality process 𝛼, 𝜇. Since the multiplying effect from reducing either of the quality rates are completely the same 𝛼𝜇 = (𝛼/𝛼)(𝜇/𝜇)𝛼𝜇, the integrated supply chain always focuses on improving the relevant quality rate with the least associated cost parameter min(𝑆𝐶!, 𝑆𝐶!). 𝑆𝐶! and 𝑆𝐶!are a determined

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Quality-related Emission The buyer’s quality emission cost; 𝐂^𝑩𝒒 = !"!!!"!! ! !!!" ! and supplier’s quality emission cost; 𝐂^𝑺𝒒 = !"!!!"!!!" !!!"# ! gives the integrated supply chain’s emission cost; 𝐂^𝑺𝑪𝒒 = !"!!!"!!!"!!!!"!!"# ! !!!" !

The approach of Hou, Lin, and Huang (2016) is followed by categorizing carbon emission cost into fixed and variable carbon emission cost in order to establish the relevant carbon emission

association with the aforementioned quality cost. Fixed emission cost at the buyer 𝐸𝑓! and fixed

emission cost at the supplier 𝐸𝑓! are carbon emissions cost-related factors such as assumptions

on handling emission and product process emission. 𝐸𝑓!+ 𝐸𝑣! are accordingly the buyer’s and

supplier’s variable emission costs. These costs are related to the number of items and quantity of defective items that need to be shipped, reworked, or disposed amongst both facilities of the supply chain. Herein, at the buyer, transportation variable emission cost of the order quantity Q is paid and, at the supplier, variable emission cost for the number of defective items 𝛼Q is paid for reverse shipment’s emission causes. Divided by 1 − 𝛼𝜇 , which accounts for the variability in product quality as a percentage. !! determines the number of production set-ups. Multiplying the aforementioned by !! eventually gives the overall total emission cost associated with the quality level of the production. As a result, as the non-conforming unit rate 𝛼 and out-of-control rate 𝜇 decrease the overall emission cost will lower and visa versa. In practice, this reflects the process of having to handle, transport, and rework fewer products amongst the facilities of the supply chain and thus fewer emission outputs.

To summarize;

Lean-related cost consists of inventory-related cost, quality cost, and quality improvement cost:

C

!"#

+ C

!"#

+ C

!"#$

and emission-related cost consists of inventory-related emission cost and quality-related emission cost:

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Eventually, the overall total cost of the integrated lean supply chain with environmental consideration (Lean & Green SC) can be therefore formulated as;

𝐓𝐂

𝐋&𝐆

= 𝐂

𝑺𝑪𝒊

+ 𝐂

^𝑺𝑪𝒊

+ 𝐂

𝑺𝑪𝒒

+ 𝐂

𝑺𝑪𝒒𝒊

+ 𝐂

^𝑺𝑪𝒒

3.5 Objective function

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4. Numerical Studies

In this section, input data (Table1) is used for computation and analysis of the model at hand. Herein, the model was created in Microsoft Excel and Solver is further used to determine the optimal: order quantity Q*, the production defects rate 𝛼*, and the production out-of-control rate 𝜇*, which minimizes the total cost of the integrated supply chain. A sensitivity analysis is conducted by changing input parameters of the model in order to receive a diversity of output data of the model. This can further be used to indicate and analyze possible trade-offs of costs among lean improvements and emission causes. In the first part of this section, the optimal values are determined that minimize the total cost of the lean SC without emission consideration (1) and Lean & Green SC (2). Moreover, the optimal values are determined that minimize total emission for the Lean & Green SC (3). The difference in lean-related cost and carbon emission cost are outlined and compared. Herein, the results of solving the lean SC without emission consideration in line with the model of Zhu et al. (2007), provides numerical data output serving as a basis for a comparison with the newly developed lean & green SC model. In the second part of this section, a sensitivity analysis of diverse data input of parameters in the model is made. Herein, the same sensitivity analysis for a variety of data input is created for the lean SC (1) and the lean & green SC (2). Eventually, a comparison between the lean SC and lean & green SC is made, wherein the impact on cost and emissions are outlined and evaluated in detail. The results provide detailed insight to what extent the lean-related cost of the lean SC and the lean & green SC are differently influenced. It further helps us to evaluate which parameters have the most impact on the lean supply chain while considering carbon emission outputs. In the last part of this section, the impact of constraints on the decision variables Q, 𝛼, 𝜇, which determine the production policy, are looked at more closely. Since the integrated supply chain in practice is not always able to apply a production policy using the herein determined optimal values. We will make assumptions, and look at the impact of varying and fixed decision variables from different viewpoints and evaluated their influence on cost and emission outputs.

The data inputs of the following table are derived from Hou, K., Lin, L., & Huang, Y. (2016), Battini, D., Persona, A., & Sgarbossa, F. (2014) and Chen, X., Benjaafar, S., & Elomri, A. (2013). In order to illustrate an analytical modeling approach, as realistic as possible. Values from recent literature are combined and translated into the proportion that stands in approximate coherence with nowadays-industrial reality. The following data inputs are used as the base-case for the following parametric computation in this section;

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Table 1:

*Lean-related cost (Blue) *Emission-related cost (Green)

Parameter Input Data D (unit/year) 100.000 H (€/unit) 5 K (€/order) 100 h (€/unit) 2 k (€/order) 400 H (€/unit) 0,50 K (€/order) 10 h (€/unit) 0,20 k (€/order) 40 r (unit/year) 1.000.000 s (€/unit) 5.000 SC! (€/unit) 50.000 SC! (€/unit) 80.000 𝛼 0,72 𝜇 0,30 𝐸𝑓! 4 𝐸𝑣! 0,5 𝐸𝑓! 100 𝐸𝑣! 10 4.1 Solving the Equations (1), (2), (3) and Determine Optimal Values Minimum Lean Cost - Supply Chain without Carbon Emission Consideration (Lean SC) (𝟏)

Using the input data of Table 1 the optimal values for Q, 𝛼, 𝜇 of the integrated supply chain without emission consideration that minimizes the total cost is computed to be;

Table 2:

Q* 953 units

𝛼* 0,69

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Using the above optimal values reveals the following minimized cost; Table 3: Total cost 106.384,73 € Inventory holding-related cost 54.954,53 € Quality cost 50.000,00 € Quality improvement cost 1.430,20 € Therefore, it can be said that the optimum and therefore lowest cost the integrated supply chain can reach when solely focusing on being lean will be at a total cost of 106.384,73 €. Herein, the point of minimum costs at the same time is defined to be the leanest point that the integrated supply chain can reach. Minimum Emission - Supply Chain with Carbon Emission Consideration (Lean & Green SC) (3)

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Table 5:

Total cost Lean related cost Emission cost 363.388,20 € 308.004,37 € 55.383,84 €

Minimum Total Cost - Supply Chain with Carbon Emission Consideration (Lean & Green SC) (𝟐) In the integrated supply chain both the supplier and the buyer make centralized decisions, acting as one in order to minimize the overall cost of the supply chain, herein, balancing lean cost and carbon emission cost at the same time. Minimize total cost of the integrated supply chain with emission consideration and solving the equation using the data input from table 1 reveals the optimal values of the decision variables Q, 𝛼, 𝜇 (Table 6). Herein, the lean & green supply chain prefers a medium invest in quality improvements that increase the lean cost to some degree, but simultaneously reduces carbon emission to a certain extent. Furthermore, a medium order- and production quantity preference can be observed. Table 6: Q* 2194 units 𝛼* 0,14 𝜇* 0,30

When looking at both quality improvement variables 𝛼 and 𝜇, the decision of the quality improvement investment greatly depends on the lowest of the two given quality improvement cost factors SC! and SC!. Since 𝛼𝜇 = (𝛼/𝛼)(𝜇/𝜇)𝛼𝜇, the impact of reducing either of the quality

parameters on the cost rate is the same. As SC! shows the lowest improvement cost (Table1), a

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Table 7:

Total cost Lean related cost Emission cost 215.297,15 € 132.597,05 € 82.700,10 € Inventory Holding 28.493,83 € Inventory Holding 2.849,38 € Quality product 23.761,02 € Quality product 79.859,72 € Quality Improvement 80.333,20 €

It can be further observed that when taking into account carbon emission, the overall lean-related cost increase by 24,64% from 106.384,73 € (Table 3) to 132.597,05 € (Table 7). This observable is the result of a costly quality improvement investment in the supply chain that is needed to reduce the defect rate (Table 7). The defect rate not only directly affects the cost that occurs for defective production but also determines some of the emission cost that arises from the quality of the product. As a result, a trade-off is noticed between having low-quality improvement cost from investments, but high-quality product cost when the defect rate is higher as it can be seen in Table 3, compared to high-quality product emission or vice versa (Table 7). While the lean supply chain considers carbon emission outputs one prefers a lower defect rate that reduces both quality product cost and its related emission over cost cuttings of actual investments in a defect reduction. Furthermore, while considering carbon emission outputs one also prefers a larger lot size with a total of 46 production set-ups and high-quality improvements. Whereas, the supply chain that doesn’t consider carbon emission shows a much higher preference of production set-ups of 105 times and a significantly lower quality improvement investment. It can be further seen that when reducing carbon emission to a minimum point the overall lean-related cost highly increase by 132,29% from 132.597,05 € (Table 7) to 308.004,37 € (Table 5). While at the same time, the carbon emission can only be reduced by 33,03% from 82.700,10 € (Table 7) to 55.383,84 € (Table 5).

Eventually, observing figure 1, the total cost function 𝑇𝐶!&! and the lean cost function 𝑇𝐶! as part

of the total cost function, are both convex in the cost of carbon emission. A threshold can be observed for the total cost of the integrated supply chain 𝑇𝐶!&! at the emission cost of 80.000€.

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the aforementioned threshold point. The threshold of the lean cost function occurs at an emission cost level of 95.000€. From that point on it can be noticed that with a lean cost increase of 9.943,75€, carbon emission cost can be further decreased by 20.000€. It can be further noticed that when the supply chain attempts to reduce the emission cost below the level of 70.000€ the cost sharply rises at a much higher rate while carbon emission level reaches its minimum. Therefore, an opportunity for the lean & green SC can be observed to further reduce emission outputs with a considerably small trade-off on cost increase. Figure 1: 4.2 Sensitivity of Input Parameters - Comparison Lean SC vs. Lean & Green SC In this section, the sensitivity of the lean cost 𝑇𝐶! without emission consideration (Lean SC) and the lean cost 𝑇𝐶!as part of the total cost function 𝑇𝐶!&! with emission consideration (Lean & Green SC) are being analyzed and compared.

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Table 9 shows the sensitivity of the cost factor s that determines the cost that the integrated supply chain needs to account for in the loss of defective production. It can be observed at a relatively lower range of cost for defective production the impact on the lean cost when considering carbon emission is very high compared to the lean cost without emission consideration. However, a threshold can be pointed out between the range of 15.000€ - 20.000€ of s. From this point on the impact on the lean cost without carbon emission consideration starts to be greater than on the lean cost of the lean & green supply chain.

At the sensitivity of Sα (Table 10), similarly to the sensitivity of s, one can observe a difference in impact at a lower cost investment range, needed in order to decreases the defective production rate of the product. At the range of 1.000€ to 30.000€ the lean-related cost when considering carbon emission are significantly lower and thus the impact of consideration does not negatively affect the lean cost in the lean & green supply chain. However, when the investment needed to reduce the defective production rate falls between 35.000€ - 70.000€ the effect turns around as lean cost of the lean & green supply chain steadily increases and lean cost without emission consideration stay unaffected and remain at almost the same cost level. Furthermore, it can be observed that at both supply chains whenever the investment increases that is needed to reduce the defective production rate above 70.000€, lean cost at both sides remain at the same level, as the investment seems to be too high to undertake quality improvements without scarifying overall leanness on a cost perspective.

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4.3 The Impact of Changes in the Production Policy regarding Decision Variables In this section, assumptions on different production policies are made and evaluated. Different constellations of the three decision variables Q, 𝛼, 𝜇 are analyzed. Their impact on cost levels and emission levels are looked at in detail from the viewpoint of the lean SC and the lean & green SC. We take a look at the cost of a stable production defect rate αμ while one chooses a variety of order- and production quantity Q (Figure 2). It can be observed that the total cost function 𝑇𝐶!&! as well as the lean cost function 𝑇𝐶! as part of the total cost function are both convex in Q for a fixed optimal production defect rate α*μ*. We notice while the lean & green supply chain decreases its order- and production quantity from 10.000 to 2.000 the total cost, as well as lean cost, steadily decreases while carbon emission cost stays relativly stable (Appendix 2.11). At the order- and production quantity of 2.000 units a threshold can be observed. Whereby lean cost rapidly increase and emission slowly increases as the order- and production quanitiy drops below this point. It shows that there is a trade-off between cost and emission outputs as the lean & green SC has reached a stable high level of production quality. Whenever the lean & green SC would need to make policy adjustment in terms of their order- and production quantity an increase in the quantity is prefered to counterbalance the increase of cost and emission.

Similarly, one can observe a convex of the total cost function 𝑇𝐶!&! as well as the lean cost

function 𝑇𝐶! as part of the 𝑇𝐶!&! in the non-conforming unit rate α for a steady order- and

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5. Discussion

The main purpose of this part is to provide insight that was gained from the numerical studies and eventually contributes to answering the research question. These insights are discussed and compared with the relevant literature at hand. The research outcomes of this particular study present a variety of influencing effects on lean improvements while incorporating carbon emission into the integrated lean supply chain. These insights add to existing literature and may support manufacturing supply chains to achieve the optimal lean production policy that reduces cost and incorporates environmental sustainability at the same time.

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environmental sustainability the manufacturing supply chain cannot be considered lean anymore from a cost perspective. In the model of Zhu et al. (2007) the main drive of the integrated supply chain is to invest in quality improvements to reduce their quality-related cost. We can add now that when the focus shifts to a simultaneous cost and emission reduction, the incentive to reduce carbon emission cost is accelerated as emission outputs are highly influenced by the quality level of the production. Therefore, the urge to invest in even higher quality improvement efforts rises at the Lean & green supply chain. Since the cheaper option for the supply chain of quality improvement is to reduce the non-conforming unit rate, the out-of-control rate stays relatively unaffected until a certain point, where the improvement of non-conforming units becomes more expensive. If an assumption is made that the lean & green SC has already achieved a relatively high-quality level of its manufacturing production and chooses not to further invest in quality improvements as in section 4.3, we can compare our findings with the insights of Zhu et al (2007). They conclude that at high-quality levels the supply chain prefers to manage the order- and production quantity over further investments in quality improvements. At a certain point of a quality improvement threshold, a preference to reduce the order- and production quantity is observed. In our model, while considering environmental sustainability, we can observe the same preference. However, in the lean & green supply chain, there is a significantly higher threshold of quality level and the order- and production quantity can only be reduced until a certain point before lean cost and emission start to highly increase again. In our model, reducing the order- and production quantity can further reduce lean cost. Thus decrease the batch size, while carbon emission can be kept relatively stable. However, the lean & green SC should never lower their order- and production quantity below 2000 units. From this point on, not only the lean cost will increase dramatically but also the carbon emission outputs start to significantly rise. Nevertheless, whenever the main goal is to reduce emission outputs the production preference found by Zhu et al. (2007) does not hold anymore. Hereby, quality levels are increased to a maximum and order- and production quantities are increased to reach the lowest emission output level.

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6. Conclusion

In an integrated lean supply chain, both parties contribute as one firm, in order to achieve the best possible policy that serves for the lowest cost. In this research paper, environmental sustainability in form of carbon emission outputs are incorporated in the integrated lean supply chain. Carbon emission outputs have been transformed into carbon emission cost, in order to be incorporated into the original integrated lean supply chain model. Investigating the effect that carbon emission consideration has on a lean supply chain. A comparison is made between the “original lean supply chain” without carbon emission consideration and the “lean & green supply chain” with carbon emission consideration. A numerical study is made in order to evaluate the impact of the integrated lean supply chain with environmental sustainability consideration, from a cost perspective. Optimal values for both supply chains are determined that minimize cost and emission outputs. A sensitivity analysis is performed in order to obtain further insight into the impacts.

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consider a manufacturing policy: With medium-high production quality levels. Find low-cost production quality improvements. Apply a medium level of order-and production quantity. Most importantly, aim for a threshold on a medium-low level of carbon emission outputs, that doesn’t scarify leanness completely on a cost perspective. The main limitation of this research is to not be able to rely on first-hand input data for the base-case scenario from a primary source. Input data was collected from several secondary sources and approximately scaled to proportion to create an overall realistic case. However, first-hand data input from one and the same source would increase accuracy and reliability of the data outputs. Secondly, the developed model does not take into consideration carbon emission that may arise from the actual process of investment when increasing the quality of the production process. Even though this might have a rather small influence, future research is needed to further explore and understand the impact. Moreover, further research is needed that not only analyzes the effects of incorporating carbon emission in the integrated lean supply chain but also in a decentralized lean supply chain, wherein the buyer and supplier act independently. In such a lean supply chain the supplier acts upon the buyer’s improvement actions and may undertake further improvement actions. This could lead to a different outcome of the lean & green SC policy as well as new opportunities may arise.

Acknowledgements

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References

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Appendix 2. Lean Supply Chain with Emission Consideration (Lean & Green) Appendix 2.1 Sensitivity of Cost to Changes on Demand D Total

Cost(Tc) Lean cost (Lc) Emission cost (Emc) Q α μ

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Appendix 2.2 Sensitivity of Cost to Changes on Quality Cost factor (s)

s

Total

Cost(Tc) Lean cost (Lc) Emission cost (Emc) Q α μ

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Appendix 2.3 Sensitivity of Cost to Changes on Quality improvement Cost factor (Sα) Total

Cost(Tc) Lean cost (Lc) Emission cost (Emc) Q α μ

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Appendix 2.4 Sensitivity of Cost to Changes on Initial Product Defect Rate ( ) Total

Cost(Tc) Lean cost (Lc) Emission cost (Emc) Q α μ

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Appendix 2.5 Sensitivity of Cost to Changes on Initial Production Out-of-control rate ( ) Total

Cost(Tc) Lean cost (Lc) Emission cost (Emc) Q α μ

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Appendix 2.6 Sensitivity of Cost to Changes on Variable Emission Cost Factor at the Buyer Total

Cost(Tc) Lean cost (Lc) Emission cost (Emc) Q α μ

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Appendix 2.7 Sensitivity of Cost on Optimal Quantity (Q*) for Lean&Green SC with Changing Non-Confirming Unit Rate (α)

Fixed Q* α Total Cost(Tc) Lean cost (Lc)

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Appendix 2.8 Sensitivity of Cost on Optimal Quantity (Q*) for lean SC with Changing Non-Confirming Unit Rate (α)

Fixed Q* α Total Cost(Tc) Lean cost (Lc) Emission cost (Emc)

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Appendix 2.9 Sensitivity of Cost on Optimal Quantity (Q*) for Lean&Green SC with Changing Out-Of-Control Rate (μ)

Fixed Q* μ Total Cost(Tc) Lean cost (Lc) Emission cost (Emc)

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Appendix 2.10 Sensitivity of Cost on Optimal Quantity (Q*) for Lean SC with Changing Out-Of-Control Rate (μ)

Fixed Q* μ

Total

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Appendix 2.11 Sensitivity of Cost on Stable Non-Confirming Unit Rate (α) and Out-Of Control Rate ( with Changing Quantity (Q)

Fixed α* μ* Q Total Cost(Tc) Lean cost (Lc)

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Appendix 3. Integrated Supply Chain Both with and without Emission Consideration (Lean vs. Lean and Green)

Appendix 3.1 Sensitivity of Cost to Changes on Demand

D Lean SC Lean&Green SC Difference

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Appendix 3.2 Sensitivity of Cost to Changes on Quality Cost factor (s)

s Lean SC Lean&Green SC Difference

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Appendix 3.3 Sensitivity of Cost to Changes on Quality improvement Cost factor (Sα)

Lean SC Lean&Green SC Difference

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Appendix 3.4 Sensitivity of Cost to Changes on Initial Product Defect Rate ( )

Lean SC Lean&Green SC Difference

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Appendix 3.5 Sensitivity of Cost to Changes on Initial Production Out-of-control rate ( )

Lean SC Lean&Green SC Difference

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