Business Intelligence 10

498 Part III • Prescriptive Analytics and Big Data

model of the decision problem without having to consider the uncertainty of any vari- ables. Then we recognize that certain parameters or variables are uncertain or follow an assumed or estimated probability distribution. This estimation is based on analysis of past data. Then we begin running sampling experiments. Running sampling experiments consists of generating random values of uncertain parameters and then computing val- ues of the variables that are impacted by such parameters or variables. These sampling experiments essentially amount to solving the same model hundreds or thousands of times. We can then analyze the behavior of these dependent or performance variables by examining their statistical distributions. This method has been used in simulations of physical as well as business systems. A good public tutorial on the Monte Carlo simulation method is available on Palisade.com (http://www.palisade.com/risk/monte_carlo_ simulation.asp). Palisade markets a tool called @RISK, a popular spreadsheet-based Monte Carlo simulation software. Another popular software in this category is Crystal Ball, now marketed by Oracle as Oracle Crystal Ball. Of course, it is also possible to build and run Monte Carlo experiments within an Excel spreadsheet without using any add- on software such as the two just mentioned. But these tools make it more convenient to run such experiments in Excel-based models. Monte Carlo simulation models have been used in many commercial applications. Examples include Procter & Gamble using these models to determine hedging foreign-exchange risks; Lilly using the model for deciding optimal plant capacity; Abu Dhabi Water and Electricity Company using @Risk for fore- casting water demand in Abu Dhabi; and literally thousands of other actual case studies. Each of the simulation software companies’ Web sites include many such success stories.

Discrete Event Simulation

Discrete event simulation refers to building a model of a system where the interaction between different entities is studied. The simplest example of this is a shop consisting of a server and customers. By modeling the customers arriving at various rates and the server serving at various rates, we can estimate the average performance of the system, waiting time, the number of waiting customers, and so on. Such systems are viewed as collections of customers, queues, and servers. There are thousands of documented applications of discrete event simulation models in engineering, business, and so on. Tools for building discrete event simulation models have been around for a long time, but these have evolved to take advan- tage of developments in graphical capabilities for building and understanding the results of such simulation models. We will discuss this modeling method further in the next section. Application Case 8.8 gives an example of the use of such simulation in analyzing complexities of a supply chain that uses a visual simulation to be described in the next section.

Introduction

Cosan is a Brazil-based conglomerate that operates globally. One of its major activities is to grow and process sugar cane. Besides being a major source of sugar, sugar cane is now a major source of ethanol, a main ingredient in renewable energy. Because of the growing demand for renewable energy, etha- nol production has become such a major activity for

Cosan that it now operates two refineries in addi- tion to 18 production plants, and of course, mil- lions of hectares of sugar cane farms. According to recent data, it processed over 44 million tons of sugar cane, produced over 1.3 billion liters of etha- nol, and produced 3.3 million tons of sugar. As one might imagine, operations of this scale lead to com- plex supply chains. So the logistics team was asked

Application Case 8.8 Cosan Improves Its Renewable Energy Supply Chain Using Simulation

Chapter 8 • Prescriptive Analytics: Optimization and Simulation 499

to make recommendations to the senior manage- ment to:

• Determine the optimum number of vehicles re- quired in a fleet used to transport sugar cane to processing mills to preserve capital.

• Propose how to increase the actual capacity of sugar cane received at the sugar mills.

• Identify the production bottleneck problems to solve to improve the flow of sugar cane.

Methodology/Solution

The logistics team worked with Simio software and built a complex simulation model of the Cosan supply chain as it pertains to these issues. According to a Simio brief, “Over the course of three months, newly hired engineers collected data in the field and received hands-on training and modeling assistance from Paragon Consulting of San Palo.”

To model agricultural operations to analyze the sugar cane’s postharvest journey to produc- tion mills, the model objectives included details of the fleet of road transport sugar cane crop to Unity Costa Pinto, the actual capacity of reception of cane sugar mills, bottlenecks and points for improvement in the flow of CCT (cut-load-haul) of cane sugar, and so on.

The model parameters are as follows:

Input Variables: 32 Output Variables: 39 Auxiliary Variables: 92 Variable Entities: 8 Input Tables: 19 Simulated Days: 240 (1st season) Number of Entities: 12 (10 harvester composi- tional types for transport of sugar cane)

Results/Benefits

Analyses produced by these Simio models provided a good view of the risk of operation over the 240- day period due to various uncertainties. By analyz- ing the various bottlenecks and ways to mitigate those scenarios, the company was able to make bet- ter decisions and save over $500,000 from this mod- eling effort alone.

Questions for DisCussion

1. What type of supply chain disruptions might occur in moving the sugar cane from the field to the production plants to develop sugar and ethanol?

2. What types of advanced planning and prediction might be useful in mitigating such disruptions?

What Can We Learn from This Application Case?

This short application story illustrates the value of applying simulation to a problem where it might be difficult to build an optimization model. By incorpo- rating a discrete event simulation model and visual interactive simulation (VIS), one can visualize the impact of interruptions in supply chain due to fleet failure, unexpected downtime at the plant, and so on, and come up with planned corrections.

Sources: Compiled from Wikipedia contributors, Cosan, Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index. php?title=Cosan&oldid=713298536 (accessed July 10, 2016); Agricultural Operations Simulation Case Study: Cosan, http:// www.simio.com/case-studies/Cosan-agricultural-logistics- simulation-software-case-study/agricultural-simulation- software-case-study-video-cosan.php (accessed July 2016); Cosan Case Study: Optimizing agricultural logistics operations, http://www.simio.com/case-studies/Cosan-agricultural- logistics-simulation-software-case-study/index.php (accessed July 2016).

u SECTION 8.9 REVIEW QUESTIONS

1. List the characteristics of simulation. 2. List the advantages and disadvantages of simulation. 3. List and describe the steps in the methodology of simulation. 4. List and describe the types of simulation.