Egyptian Faience Amulet 664-332 BC

                                                             

Title: Egyptian Faience Amulet 

Dated: Late Period, Ca. 664-332 BC.

Size: L:33.9mm / W:18.3mm ; 3.83g

Provenance:
From the collection of a London gentleman; formerly acquired in early 2000s; previously in 1970s UK collection.

Description: 
A blue faience amulet of the air god Shu is depicted kneeling with one knee on the ground and the other raised. His arms are raised and bent at the elbows, with the sun represented between them. 
For similar see: The Metropolitan Museum of Art, accession number: 89.2.290.

Acquired from Apollo Art Auctions, London. Fine Ancient Art & Antiquities

Byzantine Bronze Cross Pendant 600-800 AD

                                         

Title: Byzantine Bronze Cross Pendant with the Five Wounds of Christ

Dated: Ca. 600-800 AD.

Size: L:30mm / W:15mm ; 2.86g 

Provenance:
From the private collection of a South London art professional; previously in a collection formed on the UK/European art market in the 1990s.

Description: 
A bronze cross pendant with flaring arms and a suspension loop. It features a motif of concentric punched circles, symbolising the Five Wounds of Christ. These represent Jesus' suffering and sacrifice for humanity's sins in the Christian faith. 

Acquired from Apollo Art Auctions, London. Fine Ancient Art & Antiquities

Survey of Consumer Finances Wealth Study

 https://sxw1166.shinyapps.io/Wealth/

Sofia Wolfson
Emory University
University of Miami

Thoreau's Excursions 1866

                           

Title: Excursions

Author: Thoreau, Henry David

Place Published: Boston

Publisher: Ticknor and Fields

Date Published: 1866

Description:

319 pp. Portrait frontispiece. (8vo) original blindstamped brown cloth, gilt spine. Early printing.

Excursions was collected from various sources by Thoreau's sister, Sophia E. Thoreau, and published the year after Thoreau's death. With London Grove Library Company bookplate.

Condition:

Light rubbing to edges, mild fraying at spine ends; remnants of institutional label on spine; moderate foxing; very good.

Item#:

350827

Acquisition:
Acquired from PBA Galleries, Sale 777: Fine Literature - with Beats, Bukowski & the Counterculture - Rock & Other Posters

First American Edition of Doctor Zhivago 1958

                             

Title: Doctor Zhivago

Author: Pasternak, Boris

Place Published: [New York]

Publisher: Pantheon

Date Published: [1958]

Description:

Translated from Russian by Max Hayward and Manya Harari. Grey cloth, spine lettered in gilt, jacket. First American Edition.

Condition:

Jacket slightly toned and worn, minor chips along edges; faint sunning to volume spine, touches of wear to cloth; near fine in very good jacket.

Item#:

336412

Acquisition:
Acquired from PBA Galleries, Sale 777: Fine Literature - with Beats, Bukowski & the Counterculture - Rock & Other Posters

Bukowski's Tales of Ordinary Madness Inscribed 1972

                             

Title: Erections, Ejaculations, Exhibitions and General Tales of Ordinary Madness - two copies, one inscribed

Author: Bukowski, Charles

Place Published: [San Francisco]

Publisher: City Lights Books

Date Published: Various dates

Description:

2 volumes. (8vo) pictorial wrappers. Comprises:

- First Printing. Signed and inscribed by Charles Bukowski on the first blank page: "Dooreen - Keep it there for me - Charles Bukowski." 1972.

- Seventh Printing. 1982.

With Bukowski's inscription on the uncommon paperback original, the true first edition. Bukowski's best stories are here, most first appeared in underground newspapers like "Open City" and "Nola Express." 

Condition:
Inscribed copy with wear and staining to wrappers, front cover detached but present, soiling to textblock edges; Seventh Printing about near fine with mild toning to wrappers, original bookseller's price sticker on front wrapper.

Acquisition:
Acquired from PBA Galleries, Sale 777: Fine Literature - with Beats, Bukowski & the Counterculture - Rock & Other Posters

Dresden Mandate 1792

 

Title: Mandat, die Behandlung der Leichen, und die, damit nicht todtscheinende Menschen zu frühzeitig begraben werden, auch sonst dabey zu beobachtende Vorsicht betreffend. 

Place Published: Dresden, Germany

Date Published: 1792

Dimensions: 34 x 22 cm. 

Description:

Scheintod. - Mandat, die Behandlung der Leichen, und die, damit nicht todtscheinende Menschen zu frühzeitig begraben werden, auch sonst dabey zu beobachtende Vorsicht betreffend. 

8 Bl. Lose Druckbogen ohne Einband. 

Dresden, Carl Christian Meinhold, (1792). 
-- VD18 1165970X. – 

Durch Kurfürst Friedrich August I. von Sachsen (1750-1827) priviligiertes Mandat zur Vorbeugung gegen den Scheintod und die in der Bevölkerung weit verbreitete Furcht, lebendig begraben zu werden. Das Doppelblatt am Schluss mit dem "Unterricht, wie Todtscheinende zu behandeln, damit sie nicht zu frühzeitig begraben werden, und welche Vorschrift dabey zu beobachten". Mit detailliert beschriebenen Vorkehrungen wie z. B. Federn vor den Mund halten, ein Glas Wasser auf der Brust abstellen, die Augäpfel vorsichtig eindrücken und abwarten, ob sie sich zurückbewegen etc. – Titel mit kleinem Tintenvermerk.

Artist or Maker: Mandat, die Behandlung der Leichen

Acquisition:

Acquired from Galerie Bassenge, Berlin. Auktion 120 / Hexe, Tod und Teufel 

Predicting Major League Baseball Attendance Using Match Outcome Uncertainty

Introduction

The aim of this essay is to identify a descriptive theory of choice under uncertainty.

Sporting matches have an uncertain outcome as there is doubt in what the result will be. Live sporting events provide a real-world experiment with large sample sizes of decision makers, as tens of thousands of individuals attend each game. We can assume that sports clubs are profit maximizing companies, where demand is measured by the willingness to attend a game. This investigation will look for the best choice set to maximize attendance by looking at the level of uncertainty of game outcomes. A secondary investigation will try to confirm the strength of different predictors on the home team attendance.

There are two main discourses present in the literature (1) fiercer competition and therefore higher uncertainty in the outcome of the game drives attendance and (2) higher expected win/loss and higher certainty in the outcome of the game drives attendance. Three types of uncertainty have been identified in the literature, (1) match uncertainty, which will be covered in this paper, (2) seasonal uncertainty and (3) the absence of long-run domination by a specific team (Borland & MacDonald, 2003).

Uncertainty of Outcome Hypothesis (UOH)

The Uncertainty of Outcome Hypothesis introduces the concept that attendance demand is dependent on the competitive balance within a sports league (Rottenberg, 1956). Neale (1964) calls this the ‘Louis-Schmelling Paradox’, highlighting the ‘peculiar economics’ of sports. He assumes that the ideal position for any firm is that of monopoly, however, for a fighter like Joe Louis, to adopt such strategy and not having competitors to fight with would bring him no income. The competition is what drives interest and the harder the fight the higher the profit. A comparable league-wide effect, also coined in the same paper by Neale, is the ‘League Standing Effect’, where the closer the standings of two teams within a team, the higher the gate receipts. This is different to the ‘Louis-Schemelling Paradox’ as it eliminates the effects of the advertising feedback loop present in singular matches like boxing. The NBA has followed Neale’s advice, by introducing Collective Bargaining Agreements where salaries are capped in order to improve competitive balance and suppress wages (Johnson, 2021).

Further research into this topic has corroborated this theory in English soccer (Forrest and Simmons, 2002), in baseball (Kochman & Badarinathi 1995) and in many other studies (Knowles et al., 1992). Cairns (1986) argues that many of these studies ignore the playing at home advantage, current performance or improvement potential within the league. This is where betting information becomes useful, as it can provide one of the most complete sources of information of a team and can capture outside factors such as injuries, fatigue, current form, the advantage of playing at home and weather.

Loss Aversion and Reference-Dependent Preferences

The theory of loss aversion and reference-dependent decision making states that consumer utility is higher when attending a game with an expected win or loss versus, an unexpected win or loss (Johnson, 2021). This stems from the utility function given by prospect theory, where loss aversion emerges from decisions under uncertainty (Kahneman & Tversky, 1979). Studies have found loss aversion affecting attendance in both the MLB (Coates et al., 2014) and the NFL (Coates & Humphreys, 2010). Humphreys and Zhou (2015) highlight the asymmetry present in the loss felt from an unexpected loss than an unexpected win (in line with prospect theory), and therefore attendance is driven by the avoidance of such losses. They also find that the number one predictor of attendance is a highly expected win.

Other studies have found that US consumers do not value outcome at all (Nalbantis & Pawlowski, 2019) and that different teams can have different effects (positive or negative) of uncertainty on attendance (Mills & Fort, 2018).

The aim of this study is to see if the win probability of a game has any predictive ability of stadium attendance turnout for the St. Louis Cardinals. The first hypothesis is that the higher the win probability the higher the attendance will be. The second hypothesis is in regards to other metrics that may influence attendance such as the present rank within the conference and the importance of a game to win the world series.

Methodology

The data used in this paper contains a sample of 160 games from the 2019 Major League Baseball Season for the St. Louis Cardinals from the website “baseball-reference.com”. Two games had to be omitted because betting data was not available. The betting data was taken  from “sportsbookreview.com“, using 888sport betting odds. Win percentages were calculated using the formula present in the book The Logic of Sports Betting (Davidow, 2019). Home team win probability shows the likelihood that the home team will win the game.

Win Probability = 100 / ( 100 + positive odds )
Win Probability = ( |negative odds| ) / ( 100 + |negative odds| )

Rank is the St. Louis Cardinals’ rank in the conference at the time the game was played, attendance is the number of people who attended the game, cLI (Championship Leverage Index) is the importance of this game on this team’s probability of winning the World Series, odds are the sport book odds and winp is the win probability given by the formulas above. A cLI of less than one is below average importance, one is average importance and above one is higher importance. cLI is calculated by using the current probability of winning the world series and subtracting the same probability assuming that the team wins the game. Such difference is the possible impact of this game.

RESULTS

The first model specification is a linear regression given by:

Model 1: attendance= 13574 + 2152*rank + 7146*cLI – 0.7193*odds + 14881*winp Using this model the only significant variables are rank at the 0.05 alpha level and cLI at the 0.001 level. This model achieves a p-value of 0.0047 and a low R2 of 0.09181. We need to remove the odds variable, because it creates issues for multicollinearity given that odds are completely correlated to the win probability. After doing this, win probabilities become significant at the 0.1 level.

Model 2: log(attendance) = 9.79 + 0.06*rank + 0.24*cLI + 0.35*winP
Model 3: log(attendance) = 10.27 + 0.31*winP

When modeling win probability on attendance alone (model 3) we do not get any significant predictive ability. It is a positive relationship so we can infer that the higher the probability of winning the higher the turn out.

Further visual analysis was done to look at trends over the season as time series data. Graphs 1, 2 and 3 (in the appendix) map attendance, win probability and Championship Leverage Index across the season. Attendance has some cyclical seasonality present which may be due to natural weekly cycles (more people present during the weekend). There seems to be no overall trend and the points do not look sticky. Win probability has an increasing variance as time progresses as well as a slight positive upward trend over time. Graph 1 and 2 do not seem to follow the same pattern. Graph 3 is the most interesting as the points are naturally sticky. This is due to the nature of the cLI as each subsequent game is highly dependent on the previous. There is an upward linear trend and an interesting dip towards the end of the series followed by a sharp rise for the last four data points.

Discussion

The analysis has revealed that while winning probabilities do have a positive impact on attendance they are not significant enough to predict attendance. This may be due to multiple reasons. Firstly, the sample size was arguably small with only 160 games. Another limitation is the data set being limited to only one team, suggesting that there may be team differences present. For example, demand for games may have a variable elasticity depending on the city and nature of the fan base of a team. Say that a team has just won the World Series the previous year, the fan base may be more inelastic in watching games due to the excitement and expectations of the previous year. A team that did not have a very good previous season may have a more elastic fan base where people have less anticipation and therefore a wider margin of expectations for their team.

Another question arises when looking at the seasonality present in the data series, as the World Series comes closer the more attendance is present. The very sticky nature of the Championship League Index, as well as the upward positive trend should capture these trends in the model. This shows the importance of not only using win probabilities of a single event but their relationship to the rest of the season and it’s outcome. Further analysis could include looking at the uncertainty of the outcome of the season as a whole rather than a single game. Summing all attendance of a season and comparing it to the initial likelihood of winning the world series may prove to be significant across seasons and different teams.

While these findings were insignificant in regards to win probabilities the rank and CLI proved to be significant predictors. No studies have previously been interested in using these metrics or found them to be significant in predicting stadium attendance. The rank metric is in line with the loss-aversion and reference-dependent preferences theory, and the Championship League Index is closer to the UOH theory. When a team is ranked higher, we assume that the fan base will be more hopeful in a win and thus engage in loss aversion. The League Index, where the anticipation of a game having a higher importance on the outcome of the overall season predicts higher attendance shows us the attraction that people have for competition. While the competition does not have to be very close the sole expectation of being able to compete in the finals with other successful teams drives attendance. My final interpretation of uncertainty in baseball as a driver of attendance is that there is an interplay of both loss aversion, shown by ranking (on the level of a singular game), and competitive balance, displayed by championship league index (on a seasonal level).

Appendix

Works Cited

Borland, Jeffery, and Robert MacDonald. "Demand for sport." Oxford review of

economic policy 19.4 (2003): 478-502.
Rottenberg, S. (1956). The baseball players' labor market. Journal of political

economy, 64(3), 242-258.
Neale, W. C. (1964). The peculiar economics of professional sports. The quarterly

journal of economics, 78(1), 1-14.
Johnson, C. (2021). Loss Aversion, Reference-Dependent Preferences, and Collective

Bargaining Agreements in the National Basketball Association. International Journal of Sport Finance, 16(2), 69-78.

Forrest, D., & Simmons, R. (2002). Outcome uncertainty and attendance demand in sport: the case of English soccer. Journal of the Royal Statistical Society: Series D (The Statistician), 51(2), 229-241.

Kochman, L., & Badarinathi, R. (1995). Baseball attendance and outcome uncertainty: a note. American Economist, 39(2), 87. https://link.gale.com/apps/doc/A17974807/AONE?u=anon~2fd57a4b&sid=googleScholar&xid= 7f49a645

Knowles, G., Sherony, K., & Haupert, M. (1992). The demand for Major League Baseball: A test of the uncertainty of outcome hypothesis. The American Economist, 36(2), 72- 80.

Cairns, J., Jennett, N., & Sloane, P. J. (1986). The economics of professional team sports: A survey of theory and evidence. Journal of Economic Studies, 13(1), 3-80.

Kahneman, T. (1979). D. Kahneman, A. Tversky, Prospect Theory: An Analysis of Decisions Under Risk,„. Econometrica, 47(2), 1979.

Coates, D., Humphreys, B. R., & Zhou, L. (2014). Reference‐dependent preferences, loss aversion, and live game attendance. Economic Inquiry, 52(3), 959-973.

Coates, D., & Humphreys, B. R. (2010). Week to week attendance and competitive balance in the National Football League. International Journal of Sport Finance, 5(4), 239.

Humphreys, B. R., & Zhou, L. (2015). The Louis–Schmelling paradox and the league standing effect reconsidered. Journal of Sports Economics, 16(8), 835-852.

Nalbantis, G., & Pawlowski, T. (2019). US Demand for European soccer telecasts: a between-country test of the uncertainty of outcome hypothesis. Journal of Sports Economics, 20(6), 797-818.

Mills, B. M., & Fort, R. (2018). Team-level time series analysis in MLB, the NBA, and the NHL: Attendance and outcome uncertainty. Journal of Sports Economics, 19(7), 911-933.

Miller, E., & Davidow, M. (2019). The logic of sports betting. Ed Miller. 

Improving the Predicting Modeling Selection Process Using Lean Tools and Methods

Introduction:

This investigation looks at the process of creating a linear regression model in R Studio for

a random data set. The process name, mission and definitions are identified, followed by a flowchart and metrics. Then, different Deming-based Lean Six Sigma tools are used, namely, 5S, Total Productivity Maintenance, Quick Changeovers (SMED) and Mistake Proofing (Poka Yoke). Theoretical areas for improvement and methodology are mentioned to optimize and improve the key CTQs: cycle time, number of errors and run time of the entire process.

I. Naming the process and describing its mission

Process Name: Identifying and applying a regression model to any data set with a continuous response variable

Process Mission: Finding a best fit model by eliminating unnecessary steps, reducing complexity of processes and decreasing the amount of time to find such model.

II. Mission of the process

Mission Statement: Improving the process of my statistical analysis to become a better business analyst

III. Flowchart and dashboard of the process’ objectives and metrics

Dashboard

Strategy: Lean Six Sigma Tools and Methods for Process Improvement

Objectives

Reducing the time between accessing the data set and creating a best fit model

Reducing the number of errors in the code

Reducing the number of steps needed to obtain the optimal model

Metric

Cycle time from start to end

The number of times the program says error

The number of lines of code needed to find the final model

Reducing the amount of time it takes to run all of the code

After completing the code, the number of seconds it takes to produce a document from the script

Model Selection Flowchart

IV. Operationally Define Each Metric

CTQ Definition Definition of a defect Opportunity for defects

Cycle Time	Cycle time is defined by the amount of time between opening a clean data set and finding the best fitting model for such data set.	For an efficient process it should take < 2 hours to find a model for a data set with 20 variables. Every 10 more additional variables adds about 0.5 hours of cycle time. Optimal cycle time (in hours) is therefore given by the following formula: Cycle Time ≤ 2 + (n – 10)*0.05, where n is the number of variables in the data set A cycle time that does not satisfy the equation above is a defect.	• Erroneous code • Inefficient code
Number of Errors	Number of times the output of a line of code says error (see figure 1 below for example).	For an efficient process the script should not include any errors. The code to make a model should be applicable to any data set without error. Number of Errors = 0 Any error is a defect in our process.	• Program needs update • Wi-Fi Connection lost • Typing errors
Run Time	Run time is defined by the amount of time it takes to run all the lines of code one after the other to produce the best fitting model.	For an efficient process the program should be able to run all of the code to produce an output in less than 1 minute. An additional minute can be added for every additional 100’000 observations included in the data set. Optimal run time (in minutes) is given by this formula: Run Time ≤ 1 + (n – 100’000)*0.00001 where n is the number of observations in the data set A run time that does not satisfy the equation above is a defect.	• Computer Memory • Extra packages installed in the program • Wi-Fi Speed • Inefficient Code

Figure 1

V. Using 5S, TPM, Quick Changeovers (SMED) and Mistake Proofing (Poke Yoke) to fix our model selection process.

5S

Seiri: Elimination of unnecessary packages and data sets (waste)

Most programs require multiple statistical analysis tools which can be found in add-on

packages which are installed prior to running code. Many of these are unnecessary and downloading them may create a lag in the run time or produce errors in the code. This happens because unwanted packages override commands in packages that are needed to perform analysis resulting in errors and reduced cycle time. Previous data sets already uploaded in the program can also result in higher run times. It is important to identify unnecessary data which is already uploaded in the program.

Figure 2 is the red tagging process for the first ten packages installed. We can remove these packages to increase the speed of our run time.

Figure 3 and Figure 4 shows us the environment with previous data sets, before and after red tagging.

Figure 3 Figure 4

Figure 2

Seiton: Keeping a clean coding space

We can organize the code so that it is easy to read in multiple ways:

(1) Using “chunks” to contain code and naming them according to the step they are on so that they can easily be viewed and accessed by the user. Figure 4 shows lines of code contained in a “chunk” delimitated by the symbols “ ``` ” and “ ``` ”. The chunk of code is named ‘r histograms’ to indicate that we are at Step 5 of our process.

Figure 4

(2) Using the same format for the entire code. In our program the arrow symbol (<-) and equal sign are interchangeable (=). However, it is much easier to read code when we use a standardized symbol system. In Figure 5, even though all the lines have the same function, we can see that lines 4 and 5 have a much cleaner and organized look than lines 1 and 2. We want all lines to be standardized like lines 4 and 5 so that it is much easier to spot mistakes and read the code.

Figure 5

Seiso: Cleaning our workspace (laptop)

The biggest factor that affects speed is RAM (Random Access Memory), a laptops short

term memory. When a lot of applications are open on a computer, more of its short term memory is used, slowing down the overall performance. We want the majority of the memory to be focused on the app we are using. To clear memory, in phase 1, we can access the short term memory through our systems manager and click the big X at the top circled in Figure 6. This reduces one of our opportunities for defect in the run time mentioned above. In phase 3, we can proceed to clean the program itself after cleaning our computer, as shown in Figure 7, by using the broom function circled in red. This eliminates all slow-down from previous build up in the program and eliminates the problem from the root cause.

Figure 6

Figure 7

Seiketsu: Developing best practices

The processes above can be automated, rather than assigning the responsibility to the

analyst at the beginning of the process. To clear computer memory, shutting down the computer rather than putting it to sleep will clear all RAM memory. Upon shutting down, the computer programs provide an option to either “save” or “don’t save” the “workspace image” (seen in Figure 8). By clicking the option “don’t save”, one can prevent clutter from forming for future use. If these two best practices are implemented after every use, then we standardize the process.

Figure 8

Shitsuke: Self discipline

Through the automation of the process above we can reduce the amount of work needed to

be done by each analyst. The reminders to “shut down” and “save workspace image” come automatically when a file is closed or unattended for a long time, providing an extrinsic reminder to clean the workspace. Hopefully, returning to a clean workplace will provide intrinsic motivation and reminder at the beginning of each session to continue shutting down and clearing the workspace image moving forward.

Total Productivity Maintenance

Jishu Hozen

Operators of the code can be more involved with finding the optimal model by learning and understanding what each step means. Through this, coders are better equipped to diagnose the errors when they occur. There are many steps involved in the flowchart and knowing why one comes after the other and how each one relates is very important. By using Jishu Hozen, one could run each individual section out of order to see if each part works and to learn more about how each relates to the other. If one line of code does not work, the analyst will be able to understand why, rather than focusing on the whole process to figure out the problem.

(1) Breakdown Maintenance

We do not want breakdown maintenance to occur because it will increase the cycle time of the process. However, steps should be identified to quickly run through this type of maintenance. The table below tackles the first five reactions one should have to an error in the code.

1. 1  Check for syntax error in the code

2. 2  Check for missing packages needed to run the code

3. 3  Check for missing data

4. 4  Check for vector distances

5. 5  Check for previous examples of the error

(2) Preventative Maintenance

The operator of our process should be up to date with the most common errors found in regression analysis. This can be done by visiting websites such as stackoverflow.com and looking for the most frequent errors. A statistical analysis of errors found in these programming forums can be done to identify and educate people on them. This will help the operator avoid them and fix them during breakdown maintenance.

(3) Corrective Maintenance

Many times there are too many errors compiled in the code and one must start from scratch. One should always clean their workspace using techniques mentioned in the Seiso section. Then one can proceed to eliminate the latest line of code until the program runs again. Sometimes, one will return to a blank page and will have to restart the process entirely. This will return the system to an operational condition. Removing one line at a time is more time consuming but the problem can be identified without the need of eliminating all the work that has been done.

Quick Changeovers (SMED)

The majority of activities in regression analysis are internal, meaning that they occur when the machine is stopped. This is because computer run time is usually very short, and each individual line of codes takes seconds or even milli-seconds to run. The only time where the machine is working for a long time is in our second to last step (number 3 in the table below), when we fit the model with missing data. Rather than having the operator remaining idle during this time we can have them look deeper into what the missing variables are, adding that one minute from external to internal so that both the analysts and the machine are working at the same time.

		Current Time		Improvement	Proposed Time
Number	Task/Operation	Internal	External		Internal	External
1	Receiving the data set	2 minutes	1 second	Use the 5S to eliminate the need to remove packages before use	0.5 minutes	1 second
2	Statistical Analysis (each step after 1)	3.5 minutes per step (30 steps)	~ 5 seconds	Perform function checks and periodic preventive maintenance	3.5 minutes per step	~ 3 seconds
3	Fitting model with omitted data	1 minute	1 minute	Further understand missing variables by looking at what they are	2 minutes	1 minute
	Current Total	108 minutes	3.6 minutes	Improved Total	107.5 minutes	1.5 minutes

Mistake Proofing (Poka Yoke): Contact Method

We can easily use the contact method for many of our process systems. For example, one

can only proceed to a certain step only after having completed the previous one, otherwise the program will not compute the calculation. This contact check for errors is built within the machine. For example, if one does not receive the data, one cannot continue to check for missing data. Another more complicated example is performing certain statistical analyses with outliers. Results will not be significant if one keeps the outlier and they will not be able to provide the final product, a significant predictive model. The built-in mistake proofing in the program reduces the variation that comes with the analyst making sure that everything is correct.

Having a template with checks after the code or even encouraging copy and pasting from the template can reduce the amount of mistakes done by the analyst. Following the same template allows for an easier flow of the process and reduces overall cycle time. A template can be perfected over time as the process manager understands what is closer to their preference and works best for them.

VI. Conclusion

We applied different tools to make our process leaner and to improve the three main CTQ’s: cycle time, error and run time. Many of these tools are versatile and tackle similar areas of improvement. An important implication I learned is that these tools can be universally applied on both the internal processes (the analyst) and external processes (the computer), to achieve the most efficient and simple process.