Reflection on the Discrete Flow Model

2.5 Reflection on the Discrete Flow Model

Implementing the DFM for our example revealed different outcomes as explained in the previous section. This section reflects on the achievements of these different methods that covers financial and environmental impacts. In order to understand these impacts, we place our focus on the following aspects:

 Input - What sort of data is required for DFM?

 Computation - How does the model work?

 Output – What is the outcome that should assist the decision?

This section analyzes the performance of DFM model by checking above mentioned aspects.

2.5.1. Input

2.5.2 Data Collection

The project required assortment of several data regarding the demand, and operational processes. We used the following data sources for the above-mentioned requirements:

- Interviews with Nike Supply Chain, and Supply Chain Innovation professionals.

- Interviews with ELC (European Logistic Center) professionals.

- Demand data for 2015, 2016 and 2017 (Masked).

In this section we will treat the input parameters depicted in figure 2.1.

Demand Behavior: To make a comprehensive analysis, demand data is significant as the output may vary based on different demand behavior. For our study, we state that negative binomial distribution fits more appropriately as it is a retail industry trait, however, this model would provide different results for different demand distributions.

Manufacturing Failure behavior: As output is directly related to failure behavior, this parameter allows us to characterize the manufacturing setting. This makes the models more realistic as it provides flexibility. In Nike case, we assumed exponential failure behavior with a mean of 30 days, however, other settings might as well be modeled using this parameter.

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Transit Time: Transit time is a strong determinant of inventory. Therefore, we study the behavior of the outcome as transit time increases. In such cases we incur high inventory cost as transit time increases. A benefit of this parameter allows for modeling many supply chain scenarios. For instance, Nike has the production and distribution in different continents for EMEA region, which makes lead time an interesting aspect of this study and shows the risk that lead time poses to distribution networks.

Life Cycle of Product: This input has relatively more variance as it depends on the product type, quality and customer behavior. However, changing the life cycle of product has limited impact of on the model output, as the percentage does not change, however, warm up period changes proportionally with the life cycle of product.

Cost and Emission Parameters: These parameters concern the unit cost of production, cost of lost sale, transportation, inventory cost of warehouse and ELC, return cost of unit product. We may also include a constraint on percentage, such as legislative constraints enforced by the governments.

2.5.2 Computation of DFM

A critique of both financial and environmental analysis will be provided in this section.

The weaknesses and strengths of DFM will be analyzed based on the outcome of different models.

Financial Management

As explained in the section 2.2, we have 1000 periods to run the model given the parameters, which means that for every period we exercise the 20 equations in section 2.2. Following these calculations, we end up with the total cost and total emission of these 1000 period. Then we average the cost and emissions per percentage of sold products, which provides our

essential outcome per percentage. Furthermore, cost figures do change slightly for each model run due to random behavior of demand and failure of facilities, especially due to high

variance of demand figures (see section 3.3.) Another downside of this method is that its assumptions may be unrealistic regarding the failure behavior of manufacturing facilities as we do not have sufficient data on this. The most significant drawback of this method is the highly complex equations, which causes obstacles to interpret the results, since many details

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exist within the model. In order to understand the impact of cost structure, there are many aspects needed to explore such as lost sale behavior per percentage. Even though we decide on the percentage of sold products that should return to manufacturing, we have challenges to understand its root causes and explain it immediately. Furthermore, after calculating all the equations, output is solely the percentage of products, which does not provide insight regarding the optimal capacity of important entities such as ELC and MW.

2.5.3 Relevance

We argue that literature does not cover the integrated DFM problem, which is a combination of financial and sustainable management of end-of-life products. We first explain the gap in the literature to argue the relevance of subject. Discrete flow model is used for the design, control and the optimization of production systems. DFM is deployed as an alternative to the queuing systems to analyze discrete event systems (Turki et al, 2017). In fact, discrete flow models prove to be more realistic for discrete manufacturing systems rather than continuous flow models (Yao and Cassandras, 2012). Although there are methods to quantify the cost structure of closed-loop supply chains (see section 2.3.), this only considers financial impact.

Limited attention has been paid to the discrete flow models that incorporate the environmental impact. We argue that incorporating the environmental impact is significant. The reason is that both financial and environmental measures influence each other. Turki et al. (2017) treat a DFM problem with deterministic demand, which is modified to a variable demand in our integrated model. They consider a closed-loop supply chain that incorporates

remanufacturing, manufacturing, transportation, and selling of goods under financial management. Our thesis study will take on from the financial management and improve the model by the quantification of carbon emission. Nike is currently not able to quantify that emission due to remanufacturing operations, which makes this case relevant to the company.

Moreover, this thesis ought to add value to the existing literature by providing a framework to decide based on carbon emission whilst benchmarking with financial impact in a Retail Industry. Academia and businesses attempt to understand the profit of sustainability

(McKinsey, 2014). Whilst making decisions on sustainability, one must consider the carbon emission figures as well as the financial figures, which highlights the importance of our integrated Discrete Flow Model. Further developments would be incorporating carbon pricing to this model as well.

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