Improving On-Time Delivery at Madera y Metal A Major Qualifying Project report in partial fulfillment of the Bachelor of Science degree at Worcester

Polytechnic Institute

Submitted by: Hector Benitez

Manuel Freile

Matthew Outama

In collaboration with: Sharon Johnson, Advisor, WPI, Worcester, MA

Madera y Metal, Manufacturing Company, Asuncion, Paraguay

Date Submitted: March 6th, 2020

i

Abstract

Our project goal was to help Madera y Metal, a trophy manufacturing company in Paraguay,

reduce the number of orders delivered later than the internal due date. The team used A3

problem-solving to examine root causes and develop solutions. The team guided the

implementation of a new software system to track orders and set due dates, analyzed seasonality

in demand, and created an order prioritization ranking tool, to allow the company to be more

proactive in avoiding late orders.

ii

Acknowledgements

We want to thank our advisor, Prof. Johnson, for the insight in the technical and research

guidelines that helped us design an ambitious but achievable project.

We would also like to thank Madera y Metal, their sales department, management and the

production floor supervisor for meeting with us on a regular basis, providing us with data and

information useful for our project, as well as helping us test and implement our countermeasures

since we could not be physically present to do it ourselves.

iii

Authorship Statement Manuel Freile

Manuel focused on figuring out what was causing orders to be late using surveys, the

fishbone analysis and 5-Whys. He was responsible for finding methods to determine what

countermeasures to implement through communication with Madera y Metal Management and

an analysis of the difficulty of implementation. He took major responsibility for the prioritization

countermeasure. He also focused on the internal communication within the team making sure

everyone’s opinion was voiced.

Hector Benitez

Hector focused on measuring and averaging production times for calculating the lead

times that were used as standards/benchmarks for the actual production, in finding and setting up

a tool that would support our production process (Monday Software) and in communicating with

the company.

Matthew Outama Matthew focused on collecting and organizing the data, specifically the sales data utilized

in the development of the seasonality countermeasure. With the collected data, and other

information that was gathered, he also designed many of the figures and tables. Additionally, he

ensured everyone stayed on track, created / outlined the presentations, and reports.

iv

Table of Contents Abstract .......................................................................................................................................................... i

Acknowledgements ....................................................................................................................................... ii

Authorship Statement ................................................................................................................................... iii

Table of Figures ........................................................................................................................................... vi

Table of Tables ............................................................................................................................................ vi

1. Introduction ............................................................................................................................................... 1

2. Background and Literature Review .......................................................................................................... 3

2.1. Madera y Metal .................................................................................................................................. 3

2.2. Current Conditions ............................................................................................................................. 6

2.3. Case Studies ....................................................................................................................................... 7

2.4. Countermeasure Literature ................................................................................................................. 8

2.4.1. Aggregate Production Planning .................................................................................................. 8

2.4.2. Production Prioritization ............................................................................................................. 9

2.4.3. Order Due Date Estimation ....................................................................................................... 10

3. Methodology ........................................................................................................................................... 11

3.1. Objective 1: Developing Problem Statement and Collecting Background Information .................. 12

3.2. Objective 2: Identifying Causes of Late Order Delivery ................................................................. 12

3.2.1 Fishbone Diagram ...................................................................................................................... 12

3.2.2 Five-Whys .................................................................................................................................. 13

3.3. Objective 3: Determining the Most Important Root Causes ............................................................ 15

3.3.1 Fishbone Diagram Ranking ....................................................................................................... 15

3.3.2 Effort-to-Impact Ratio................................................................................................................ 17

3.3.3 Late Order Survey and Order Tracking Log Sheet .................................................................... 18

3.4. Objective 4: Developing Countermeasures ...................................................................................... 19

3.5. Objective 5: Providing Final Recommendations ............................................................................. 20

4. Results ..................................................................................................................................................... 21

4.1. General Organizational Improvements ............................................................................................ 21

4.2. Response to Seasonality ................................................................................................................... 22

4.2.1. Sales Data Analysis ................................................................................................................... 22

4.2.2 Production Ramp Up .................................................................................................................. 24

4.2.3 Preventative Maintenance .......................................................................................................... 25

4.2.4 Marketing ................................................................................................................................... 27

4.3. Product and Order Prioritization ...................................................................................................... 28

4.3.1 Multi Attribute Decision Making (MADM) Tool ...................................................................... 29

v

4.3.2 Tool Limitations ......................................................................................................................... 30

4.3.3 Tool Benefits .............................................................................................................................. 31

4.4. Improved Due Date Estimation........................................................................................................ 32

4.4.1 Unitary Production Calculation ................................................................................................. 33

4.4.2 Limitations of New Due Date Estimation Method .................................................................... 33

5. Conclusion, Recommendations, and Reflection ..................................................................................... 34

5.1 Conclusion ........................................................................................................................................ 34

5.2 Recommendations ............................................................................................................................. 34

5.3 Reflection .......................................................................................................................................... 35

5.3.1 Design Reflection ....................................................................................................................... 35

5.3.2 Design Constraints and Impact .................................................................................................. 36

5.3.2 Lifelong Learning Reflection ..................................................................................................... 36

5.3.4 Project Teamwork ...................................................................................................................... 37

References ................................................................................................................................................... 38

Appendices .................................................................................................................................................. 40

vi

Table of Figures Figure 1: Factory Layout .............................................................................................................................. 5 Figure 2: Fishbone Diagram ....................................................................................................................... 13 Figure 3: Fishbone Diagram Ranking ......................................................................................................... 15 Figure 4: Effort to Impact Diagram ............................................................................................................ 17 Figure 5: Total Orders (by month) .............................................................................................................. 23 Figure 6: Average Work per Day (by month) ............................................................................................. 24 Figure 7: Sample Maintenance Form (Orbit, 2014) .................................................................................... 26 Figure 8: Multi Attribute Decision Making Tool ........................................................................................ 30

Table of Tables Table 1: Approximated Machine Capacities ................................................................................................. 7 Table 2: Optimal Number of Workers ........................................................................................................ 25

1

1. Introduction

Madera y Metal (Wood and Metal) is a trophy manufacturing company located in

Asuncion, Paraguay. The company is one of the few trophy manufacturers in the area and the

only one offering customizable options, which causes a high demand. As demand continues to

increase, the risk of potential problems also increases. Madera y Metal is hoping to better meet

their customers’ demands by combating the potential problems. Their main objectives are to

reduce process times, improve response to seasonality, develop a new scheduling system for

order placement, and implementation of lean practices.

Madera y Metal produces a variety of award recognition products with a focus on

trophies, cups, and medals. To date, Madera y Metal has struggled with delivering products to

customers on time. More specifically, the company is having issues meeting their own internal

due dates. We think that the company will substantially benefit from taking a more data-oriented

approach; the team kept track of the time it takes to manufacture several orders for the products

of interest (trophies, cups and medals), and extrapolated durations for the overall fabrication

process for each product. These measurements were used to implement quantitative internal

deadlines and monitoring the impact on internal deadlines coverage. The percentage of fulfilled

orders by internal deadlines was used as the main metric for measuring project success.

The main goal of our project was to help Madera y Metal improve their on-time delivery

of products to customers, which was achieved through the overall sub goals of setting order

prioritization, accounting for seasonal demand and reviewing the internal due date setting

method currently used by Madera y Metal. We were able to accomplish this by analyzing sales

data and developing protocols to respond to seasonality, developing a product prioritization

2

model, and implementing new software to help generate better due date estimations. The

methods used to determine the countermeasures included; a PICK chart to help identify which

countermeasures are viable and likely to make an impact; a Fishbone diagram to find what

problems were causing issues with meeting due dates; and the 5 Whys method to understand the

root cause of the problems ranked as more important by Madera y Metal Management. We

developed Surveys and Log Sheets to track orders and understand why orders were late.

Looking ahead, our report first introduces Madera y Metal and the current conditions

within the company. We then discuss the methods used to develop our countermeasures,

followed by a results chapter that describes the countermeasures. Finally, we provided

recommendations to the company, and investigated potential areas that could have been

expanded on throughout this project.

3

2. Background and Literature Review

This chapter provides background information and current conditions of Madera y Metal,

and a review of how similar problems have been resolved. Each section supports the following

chapters of this report.

2.1. Madera y Metal

Madera y Metal is a manufacturing company located in Asuncion, Paraguay. The

company currently employs 20 people. The employees are divided between management, sales,

and production: 2 in management, 4 in sales, and 14 in production. The primary business of

Madera y Metal is the production of recognition awards such as trophies, cups, and medals.

Trophies and cups are similar. The main difference between trophies and cups is that

trophies have an MDF wood base. Furthermore, even though they both use aluminum for the top

part and the whole thing respectively, the process and the material are actually different.

Trophies are entirely produced using aluminum discs and use an embossing lathe machine for

shaping, placed on top of the MDF base. Cups on the other hand are produced using a molding

process; another option is cutting stainless steel.

The production of medals has a different process as well. This process can be divided into

two separate steps. The first part of the process is called spin casting which is used to give the

medals shape, it consists of making the molds for the medals. The second part of the process is

called electrolytic deposition or electroplating. This part of the process focuses on coloring the

metal of the medals, the three most common colors of medals are: gold (first place), silver

(second place) and bronze (third place). The materials used for medals are low melting point

4

alloys such as lead, tin, or antimony. If a more resistant material with a lighter weight is desired

medals are made from Zamak 3, which is an alloy made of zinc, aluminum, magnesium and

copper.

Madera y Metal’s philosophy is that every customer is unique, so customers have the

option to make their own designs. Another characteristic that can vary between customers is the

volume or demand. Personalized products are reviewed and assessed by Madera y Metal with the

purpose of giving a fast but plausible lead time, the time from when the order is placed until it is

completed and delivered to the customer. Madera y Metal uses an enterprise resource planning

system (ERP system) to plan the management of resources based on customer orders: volume,

value, and types of products. The ERP system is not used consistently. Orders are put in the

system when the order was placed but not updated after the order was delivered. The system also

lacks critical information on lead times and capacities.

Even though each product has a different production process, up to 70% of the machinery

is shared between different products, the newer machinery is more commonly shared because it

has fewer specific purposes. There are some exceptions to this, for example the spin casting

machine is used exclusively for medal production. A visualization of the production floor can be

seen in figure 1, which shows the sales department, the design and assembling workshop and the

management offices on the left. While to the right, the figure 1 shows the carpentry workshop

and the foundry workshop. These are the essential spaces in Madera y Metal’s operations.

5

Figure 1: Factory Layout

In addition, the three main products all go through a design process where they share

packaging and finishing processes. The steps of the process that are shared and those that are

specific to the three products can be seen in Appendix A.

6

2.2. Current Conditions

Madera y Metal’s current methods for setting internal deadlines are not rigorously

elaborated. Whenever an order is placed by a customer, the sales assistant who took the order

creates an internal deadline for the order based on the following rule of thumb:

• If the anticipated production time for the order is 1 week, the available internal

production time would be 5 days.

• If the anticipated production time for the order is 2 weeks, the available internal

production time would be 1 week and 3-4 days.

• If the anticipated production time is 1 month, the available internal production

time would be 3 weeks.

Machinery is also an important factor in the efficiency of a production process. At

Madera y Metal, there are many new machines being used to produce the recognition awards.

The newer machines consist of CNC technology (CNC Router) and Laser cutting. According to

the company, 70% of these newer machines are being used for multiple processes. In contrast,

the older machines tend to be used for only one purpose. Some of the older machines are

carpentry saws, sanders, smelters, and a spin casting machine. A list of machines and their

capacities is shown in Table 1.

7

Table 1: Approximated Machine Capacities

2.3. Case Studies

To help us better understand how to improve on-time delivery and lean process

improvement, we looked at three case studies. The first case study looked at the implementation

of different lean improvements within a knife manufacturing company (Vinas, 2018). The

second case study looked at the implementation of value-stream mapping within the automotive

industry (Rahani, 2012). The third case study looked at the use of lean focused on the customer

promise date (Hyden, n.d.). Some of the key points from these cases studies include:

• Utilizing a variety of Kanban systems to improve flow of production

• Level loading production planning for the biggest clients

• Using mapping techniques and GEMBA walks to identify bottlenecks within the

system

• Encouraging operators to value the needs of the customer and the “promise” date

8

• Creating flexible capacity by offering advancement opportunities and rate

adjustments to meet increasing demands

More detail on the case studies can be found in Appendix B.

2.4. Countermeasure Literature

2.4.1. Aggregate Production Planning

Aggregate production planning deals with the quantity and scheduling of production for

the future (Gallego, Guillermo). An aggregate plan usually works within a 3 to 18 month time

frame. In order to develop an aggregate plan, one must investigate forecasting. Additionally,

timing is very important. Seasonality and seasonal demand impact a company’s aggregate plan.

Some strategies that a company can use to deal with sudden changes in demand are:

a. Increasing or decreasing workforce through hiring and firing cycles

b. Changing the rate of production with overtime, idle time, or

subcontracting

c. Holding seasonal inventory

d. Backorder planning

Two models that are used to develop an aggregate production plan are: just-in-time

(chase strategy) and production-smoothing (level strategy). A just-in-time production plan leads

to changes in production rate to meet demand. When implementing a just-in-time strategy, it can

result in lower holding costs, but higher costs in areas such as hiring or firing costs). This plan is

best when the costs to change the rate of production is manageable. For production-smoothing,

production rates are kept constant. When using production-smoothing, the production costs are

reduced but the holding costs may be high. The high holding costs are a result of constantly

9

building inventories to match increases in demand. Thus, production-smoothing is best when

inventory carrying costs are low (Gallego, Guillermo).

Specific information is needed when creating an aggregate production plan. The

information can be utilized in a linear program to get the optimal inputs and outputs. The

information that is needed for an aggregate production plan include (Cholette, n.d.):

• Demand forecast (for each period)

• Production costs

• Labor / Machine hours per unit

• Material requirements per unit

• Holding costs

• Stock-out / backlog costs

• Company constraints

2.4.2. Production Prioritization

The main reason priorities are needed in production is because we can reduce the lead

time for certain orders but when we do this the lead time for other orders would consequently

increase. According to All About Lean website an approach for prioritization of orders is called a

VIP (Very Important Parts) lane. If there's a single lane of production there is no prioritization,

when adding a second one, identified through KANBAN cards that mark what parts are very

important parts for its lane. prioritization for orders that are made to stock is an approach that lets

you account for seasonal demand as well as prioritizing orders., tackling two problems at the

same time. The article How to Prioritize Your Work Orders recommends creating a new stock

system that will prepare for emergency situations when products run out of stock fast, by

10

creating a plan that goes along the seasonal demand analysis priorities could also account for

material availability. According to Pareto distribution 80% of the work put in production comes

from 20% of the products. Those 20% are what is known as exotic products and it is where the

prioritization should primarily focus, mainly because a system that prioritizes 80% of its work is

not sustainable. Furthermore, prioritization should begin right before production to avoid waste

as the Lean article explains, and it should be done by small batches, having clear schedules that

are available in all areas of production and clearly defining which orders are priority since the

beginning could be very beneficial for the Paraguayan company (Roser, 2018).

2.4.3. Order Due Date Estimation

Lead time refers to the time taken between the start and completion of an operation or

project (Corporate Finance Institute, n.d.). Using this definition, we proceeded to track the time it

takes for a product or products to be produced, from the time the order containing it enters the

system to the time the order is delivered.

We also found recommendations for future improvements aside from the manufacturing

lead times that were measured. Snapp suggests developing standard lead times for procurement

lead times and shipping lead times, to take s bigger scope of the supply chain into account, which

will make the minimum possible due date estimation more accurate (Snapp, 2019).

11

3. Methodology

The main goal for Madera y Metal was to improve the delivery time of products relative

to their internal due date. The company provided us with several areas they believed could be

improved, and also allowed us to identify any additional problems that could be resolved. The

objectives that our team set out to meet are as follows:

• Develop problem statement and gather background information

• Identify the root causes for the late order delivery of products

• Determine which root causes are the most important

• Develop countermeasures for the most important root causes

• Provide recommendations to the company based on our observations, research,

analysis, and results

To understand the full scope of the project, we used an A3 process which allows us to

identify, analyze and find tentative solutions/countermeasures to the company’s problems. An

A3 process is ultimately a problem-solving tool that allows for continuous improvement.

According to Quality One, who provide business consulting and training as well as project

support justifies the use of A3 to identify problems and possible solutions recommending that

“Companies must start viewing problems as opportunities of improvement” (Quality-One

International, n.d.). The first part of an A3 process is to develop a problem statement. The next

step is to gather background information to understand the current conditions. After that is

completed, a root cause analysis is conducted, and root causes are identified. Once the root

causes are identified, countermeasures are developed in response.

12

3.1. Objective 1: Developing Problem Statement and Collecting Background Information

The first task our team worked on for this project was to develop a problem statement.

This was done by discussing areas of improvement with Madera y Metal and pulling information

from the written notes they provided us.

The next task was to collect background information to learn more about the current

conditions of the company. This was also done by holding phone calls with members from both

sales and production within Madera y Metal.

3.2. Objective 2: Identifying Causes of Late Order Delivery

After identifying Late Order as the main problem to tackle through the A3 method we

used two tools widely used in LEAN organizations to understand what causes the problem. The

following Fishbone Diagram and Five Whys methods complement each other as our root cause

analysis of why orders are running late.

3.2.1 Fishbone Diagram

In order to understand what caused the problem of orders being internally late we did a

root cause analysis. The selected tools to do this was first to create a fishbone diagram. A

fishbone diagram is a tool used to identify potential root causes to a problem. In addition to

helping determine the root causes, the diagram also encourages group participation and identifies

areas where data can be collected (Ciocoiu, 2010).

With research and information provided by our liaisons in Madera y Metal we looked at

probable causes that had to do with delays caused by the machinery, problems with the process

in general, and how management and sales could improve to help reduce late orders. In addition,

13

we looked at what could cause delays in how the production workers interact with the process,

how materials could potentially cause delays to the finished goods, and other factors like the

environment associated with the industry. The fishbone diagram we designed can be seen in

figure 2 below.

Figure 2: Fishbone Diagram

3.2.2 Five-Whys

We also carried out a root cause analysis using a five-whys exercise. A five-whys

analysis is a systematic questionnaire technique to identify probable causes. One uses the tool by

asking “why?” until it becomes difficult to respond to the question (Pojasek, 2000).

14

After performing the five-whys analysis ourselves, we identified the following causes for

late orders: seasonal demand, order prioritization, and the current rule of thumb to determine

internal due dates.

First, seasonal demand is hard to control, but we found that this causes late orders

because the company’s workload is uneven throughout the year. This is a consequence of

tournaments generally taking place towards the end of the year. This would not be a problem if

customers put in their orders with a lot of time before the tournament begins, but they do not.

Customers might be more eager to plan ahead and design and order their products with more lead

time if promotions made it cheaper for them to order earlier. To improve this we believe that the

visualization of demand has shaped in the past (orders) will help the company to anticipate

peaks; They would be able to develop a better organization to fulfill demand on time (data for

volume of every product in every order for several years are required for a forecast like this to be

successful).

We also found out that the cause of a delay in many orders was because they decided to

prioritize other orders. Time is lost while choosing how to prioritize because the process is not

automated, this causes inconsistency when prioritizing. This inconsistency could be alleviated if

the decisions for prioritization were driven by data but the root cause of this is that there are no

defined measures for prioritization not enough data being tracked to make it a data driven

process. For this we implemented a log that helps keep better track of orders as well as one that

is more integrated with the ERP system, which is not being used to its full potential as explained

in the background.

Finally, we focused on their rule of thumb because there seems to be a lot of late orders

internally and we think that it could have to do with how these internal due dates are set. We

15

think these dates are set by the sales department solely to fit their purposes but not that of the

whole operation. Meaning the dates, they set can often be somewhat unrealistic. The cause of

this comes again to the problem of lack of data to be able to set internal due dates based on

realistic data driven systems that can tell decision makers how much should an order of a

particular size take based on orders of different sizes done in the past.

3.3. Objective 3: Determining the Most Important Root Causes

3.3.1 Fishbone Diagram Ranking

We discussed all problems with Madera y Metal management and asked them to rank in

orders all the causes they felt really affected why the orders are late. They do not think that

machinery, materials, or management and sales are real causes for production to be late. We do

think that better organization and clear consistency when inputting data and constantly keeping

track of the data can help the company follow orders more successfully throughout the system.

Figure 3: Fishbone Diagram Ranking

16

Madera y Metal found the environment to be the main cause for late orders. Furthermore,

seasonal demand was ranked as cause number 1 which makes sense since we know that demand

is based on the tournament schedules which usually follow the same seasons. Under

environmental causes we also found disorganized workspace and humidity ranked in number 3

and 7 respectively. Humidity being ranked last and having a high effort vs low impact solution

was disregarded. Another category that seemed to cause delayals was the process itself, the main

cause being the lack of any product/order prioritization. The lack of established guidelines for

prioritization caused several orders to be late in surveys we gave people from sales to fill in for

all orders that are late. They found a lack of communication between the production floor and

the sales department was ranked 5/7 as a problem, we believe this is related to the lack of use of

data through the company as a whole as well as the lack of visual representation of the data in

the production floor which would, help keep the work space more organized. Finally, the

remaining two causes ranked by management were related to the production workers, which are

problems more out of reach for the team. The rankings themself can be seen in figure 3.

17

3.3.2 Effort-to-Impact Ratio

Figure 4: Effort to Impact Diagram

Figure 4 is a Pick Chart which is a commonly used Lean tool that allows to evaluate

problem solving ideas based on how much effort they require to be implemented and how much

impact will these ideas have when fully implemented (Icasas). For this area we qualified the

effort to impact ratio of applying different countermeasures, the lower right quadrant are ideal

solutions while the upper right quadrant are countermeasures that would require more effort but

could still have a significant impact in the reduction of late internal orders for Madera y Metal.

We want to stay away from the upper left quadrant since those countermeasures require too

much work in comparison to the impact they have. The lower left quadrant does not have a huge

effect, but it is good to consider them since they don't require a lot of effort.

18

3.3.3 Late Order Survey and Order Tracking Log Sheet

The team designed a survey to be filled out by the sales department every time an order

was late. The form meant to figure out what type of product and what quantity was the order for.

It tracks the orders lead time through the dates it was ordered until it was delivered. It asks a

reason for why the order was late, if there was any contact with the customer informing them

about the late order. The survey aims to figure out the current workload in the production floor

and finally all the machines that were used for the production in this product to find out if there

are any common bottlenecks between late orders. The survey can be found in Appendix C.

We also created an order tracking log sheet. The purpose of this sheet is to help get a

sense of the lateness of specific orders. The results collected from this tool were very little; the

implementation did not go as planned. Had we gathered a significant amount of data; we would

have been able to verify the late order percentage. An example of the order tracking sheet can be

found in Appendix D.

19

3.4. Objective 4: Developing Countermeasures

Before developing the actual countermeasures, we had to determine the root causes for

late orders. This was done with the fishbone diagram, which was then used to rank each problem.

From this ranking, we decided to focus on seasonality, product prioritization, and due date

estimation.

After we narrowed our focus on the problems we would try to resolve, we brainstormed

potential countermeasures. The first step in this process was through research of literature. The

next step in this process was to create a preliminary list of countermeasures. For each

countermeasure we created, we linked it back to the root causes found in the fishbone diagram.

The discount for low demand seasons, design stocking system, looking into patterns and trends

to plan ahead, and understanding triggers linked back to seasonality. The prioritization based on

customer loyalty, effort, and value linked back to product prioritization. The rule of thumb based

on past orders and data collection linked back to due date estimation.

Next, we took the preliminary list and identified the levels of effort and impact associated

with each countermeasure. In picking the best problems to prioritize, we were looking for

countermeasures with high impact. This left us with: design a stocking system, look into patterns

and trends to plan ahead, base the rule of thumb on past orders, collection of data, prioritization

(based on effort and value), and understanding the current triggers.

From the countermeasures that were left, we conducted more research to help in our

design of each countermeasure. This research included looking at data that was collected (sales

data) and background research. We also considered the feasibility of each resolution to finalize

the countermeasures to be used in response to each major problem (seasonality, product

prioritization, and due date estimation).

20

Once each countermeasure was developed, we detailed them in our results, and discussed

how they would be implemented.

3.5. Objective 5: Providing Final Recommendations

Finally, we provided several recommendations to Madera y Metal based on our findings

and the development of each countermeasure. For the recommendations, we analyzed the results

and impact of each countermeasure to determine the best follow-up actions for the company. The

recommendations highlight the benefits the company should expect.

21

4. Results

Through the late order surveys, the team found that the main problem causing orders to

be unexpectedly delayed was that there was no established prioritization method or criteria.

Alongside with the prioritization tool explained below the team recommended hiring a

Supervisor that could help follow orders being produced closely and to aid in the communication

between Sales Department and Production Floor.

4.1. General Organizational Improvements

Monday Software is a critical tool for accomplishing the following; inputting the

specifications for orders that arrive, tracking orders wherever they are in the manufacturing

process in real time, monitoring if production is going as planned (using the standard lead times

as reference) and fixing unplanned, recurring variabilities in production. With this digital tool, we

can infer what the production capacity is, and in case we are not meeting these expectations, work to fix

any problem that might be causing it. Screen shots from the software can be seen in Appendix E. In order

to implement the use of this system, Madera y Metal hired a person familiar with the use of computers

and with working in teams to operate the software, leaving him in charge of monitoring the status of

production for the orders that are being produced at any given moment.

22

4.2. Response to Seasonality

After analyzing the data and conducting further background research, our team designed

solutions for Madera y Metal to respond to busier production periods. The design of this solution

is broken down into three categories: production ramp up, preventative maintenance, and

marketing.

The goal of these solutions is to improve how Madera y Metal manages sudden increases

in production. The proposed pocess will begin with the marketing team notifying the production

manager when they can expect the number of orders to significantly increase. This will allow the

production manager to let the supervisors know production needs to be ramped up. The

production manager can then look at increasing the workforce (whether it be long-term or

temporary). To prevent any delays in production, a preventative maintenance team runs through

a list of tasks (which should be conducted during slower production periods) to ensure all

machinery is in good condition. To help ease the burden of peak production periods, the

marketing team will also be in charge of communicating with regular customers and getting them

to plan their orders ahead of time (purchase orders sooner).

4.2.1. Sales Data Analysis

Our team confirmed seasonality was a relevant area of concern after analyzing the sales

data. Madera y Metal currently utilizes an online database to track their orders. The data itself has many

limitations. The limitations include: no delivery date, a lack of clarity in order quantity / volume, and a

lack of clarity on the status of orders.

Therefore, to make use of the data, we analyzed total and daily production for each month. This

would not provide any data on late orders, but it would give us a sense of how much work the company

handles each day. One useful function of the database was the ability to generate reports of sales. From

23

these reports, we were able to isolate the data by month more easily. The data itself was counted from the

reports and inputted into a separate excel file.

Based on the sales data, we identified that the most orders occur in November, at 497.

The least amount of orders occurred in January, at 196. This was found by analyzing sales data

across 3 years (from 2017 to 2019) and taking the average (for each month across 3 years). This

analysis is sumamrized in figure 5 below.

Figure 5: Total Orders (by month)

Further analysis of the data also shows that the average number of orders worked on per

day was also greatest in November. This result can be seen in figure 6 below. For November

there was an average of 22 orders per day across the 3 years. The month with the lowest average

was January, with about 9 orders per day. Taking this data into account, we can also estimate the

number of orders each operator could complete per day. We calculated that the average work per

day was about 16. The number of operators working on the manufacturing floor is 14. This

means that one operator could probably take care of about 1 order per day (16/14 = 1.14).

24

Figure 6: Average Work per Day (by month)

After looking at the data, we were able to confirm that specific times of the year vary in

production levels. This meant that Madera y Metal’s concern in meeting seasonal demand was

relevant in the countermeasure development process.

4.2.2 Production Ramp Up

Based on the data we analyzed for seasonality, we were able to model how changing the

workforce size impacted production. If we kept the standard number of workers (14), this group

could produce 224 orders in one month. This production rate would only be able to meet the

demand of January. Considering the average demand for each month, we determined the optimal

number of workers for each month, as seen in table 2.

25

Table 2: Optimal Number of Workers

4.2.3 Preventative Maintenance

When production is increased, there is likely to be more risk of machine failure. Our team

developed a preventative maintenance plan for Madera y Metal to utilize. This plan will ensure

that machine failure is kept at a minimum.

Starting at the beginning of the year, January, production is the lowest. Which means the

ideal time to conduct a preventative maintenance check would be at the start of the year. The

steps that go into a preventative maintenance check varies depending on the company, and the

type of machine being used. For Madera y Metal, a sample of what a preventative maintenance

form / schedule can be seen in Figure 7.

26

Figure 7: Sample Maintenance Form (Orbit, 2014)

27

4.2.4 Marketing

In addition to utilizing preventative maintenance practices and ramping up production,

we designed protocols for the marketing team to handle. The first protocol we designed was a

peak season alarm system. This alarm system would be initiated by the marketing team. Based

on our analysis of the sales data, we have already predetermined the months where production

rises and falls. Using this information, we recommend that the marketing team set up alarms or

notifications that let the production team know when to start ramping up or ramping down

production (increasing the workforce).

The second protocol would be for the marketing team to contact regular customers to

order products earlier. This would be done through various mediums such as email, social media,

and word of mouth. To incentivize customers to order earlier, we designed a rewards program. A

rewards program is a marketing tool that helps to increase customer loyalty (earlier orders in

Madera y Metal’s case). The idea behind this rewards program would be to give more rewards to

customers who consistently order ahead of time (Thomsen, 2019). This rewards program

provides customers with various benefits (with points being allocated with more purchases). The

benefits include: discounts, credits towards a future purchase, and company apparel. By doing

this, Madera y Metal can start orders sooner, and have them ready in advance of when the

customer needs it by.

28

4.3. Product and Order Prioritization

The late order survey was filled by Madera y Metal Sales Department. Surveys showed

that some orders were reported late because of the prioritization of orders. The new supervisor,

who has been collaborating with the team, will be in charge of filling in daily orders to calculate

the priority of customer orders as an initial run. The Supervisor is expected to be in charge of

communicating the order of production, based mostly on the outcome of the priority calculating

tool, further explained in Section 4.3.1. Although, the results of the prioritization order are not

set in stone and are subject to change in case of special circumstances, for example a

prioritization by customer loyalty (broad implementation of this idea was discarded by the team

because of scoring poorly in the effort-impact ratio).

To prioritize orders our team used a Multi-Attribute Decision Making (MADM) method

to rank customer’s orders according to priority relevance. The aim of MADM, is to find the most

desirable alternative or rank the feasible alternatives for supporting decision making as explained

in Ding et al. (2016). We created our own Multi Attribute Decision Making tool to aim in the

most desirable order priority for Madera y Metal. The three attributes (criteria) used to prioritize

orders are lead time, due date (both internal and customer), and orders currently in production.

Each criterion follows different rules. Orders with lower lead time will be ranked with a higher

priority. Customer due dates are used alongside lead time to predict if orders are going to be late,

orders that are going to be late have a lower priority since Madera y Metal prefers to have an

order which was going to be late to be slightly later, as opposed to more orders being delayed.

Finally, orders in progress are used as a tiebreaker; orders that are in progress have a higher

priority.

29

4.3.1 Multi Attribute Decision Making (MADM) Tool

With exception for orders with Lead Times shorter than a week, the hired supervisor

should fill in the spreadsheet on a weekly basis. In order to follow customer orders closely, Lead

Time is measured in hours. The data needed for the spreadsheet can be found on the Monday

Software used to calculate the internal deadlines through the revised rule of thumb. The data that

needs to be processed from the Monday Software are precisely the 3 attributes (criteria)

mentioned above. Lead Time, Due Dates (Customers and Internal) while Orders in progress will

be updated manually by the Production Floor Supervisor with a simple “SI” for orders that are

already in production. The initial score is taken by using lead time and =NOW formula in Excel

to approximate when the product would be finished, this is subtracted by the deadline given by

Monday Software. This gives us the amount of time available to fulfill the order on time. If

orders are already predicted to be late (negative initial score) they get a *10 weight, making their

score significantly higher. The lowest scores have higher priority, making an order already late a

lower priority, to avoid delaying other orders. In contrast, orders marked as already in progress

will be given a weight of (1/10) to decrease the score and increase priority. Following LEAN

fundamentals of waste reduction orders that are in production would waste time and resources

going back to the queue.

Figure 8 shows the Multi Attribute Decision Making Tool with mock data. Order tracking

numbers are to be inputted into the first column. The supervisor should fill in the lead time in

hours provided in the Monday System, in the second column. The third column converts lead

time to regular 8-hour shift days and calculates (using the excel function now and the lead time

(days)) when the order would be finished if begun immediately. The first score is given by

subtracting the customer due date (inputted by supervisor, calculated through the Monday

30

System) minus the minimum predicted due date that was automatically calculated. The due date

tiebreaker adds a weight of *5 and takes the absolute value if the order is marked as “LATE

ORDER” in the second to last column, decreasing the priority of this order. If the initial score is

negative orders are marked as “LATE ORDERS”. The last score is the order in progress tie

breaker that checks for a “SI” for orders that are in progress and gives them a weight of *(⅓)

increasing the order’s priority.

Figure 8: Multi Attribute Decision Making Tool

4.3.2 Tool Limitations

There are limitations to the tool created that should be acknowledged. For example, the

calculated lead time and the internal due date calculations are not dynamic, meaning that they do

31

not consider the current/scheduled amount of work on the production floor. The tool also needs

to be manually updated, deleting finished orders, inputting new orders and for identifying orders

that have begun production as in progress. Additionally, the supervisor should also update lead

times and customer due dates.

4.3.3 Tool Benefits

The Multi Attribute Decision Making tool automates the conversion of lead time hours to

lead time of available hours per day. The conversion helps automatically predict the date it

would be finished if order began immediately. The tool uses this date to calculate available lead

time and if the order is late, automatically gives it a lower priority. If the company keeps track of

the orders in the Monday scheduling program successfully, the lead time and internal due dates

can be easily accessed in the program, although we recommend using customer given deadlines

instead of internal deadlines given that customer deadlines aren't as tight as the internal ones and

this allows a more accurate prediction if the orders will actually be late or not. Using internal

deadlines might lead to reducing the priority of orders that are not necessarily going to be late to

the customer. Finally inputting a textual yes to the question ‘Has order begun production?’ takes

little time from the supervisor, who has the responsibility to keep close track of orders. Keeping

close track of orders by using this tool and the Monday Scheduling program will help the

supervisor and Madera y Metal have cleaner data for future analysis.

32

4.4. Improved Due Date Estimation

Our team found out that there was no specific method for calculating internal production

due dates. Whenever an order was placed by a customer, the sales assistant that took the order

created an internal deadline for the order based on their current rule of thumb.

After analyzing this rule of thumb for creating internal deadlines, we concluded that the

deadlines that emerge from this approach do not represent the real time needed for the

production of orders very well, since they are using only empirical (experience-based)

knowledge for approximating the time it will take to fabricate the demanded products. We

thought that the company would substantially benefit from taking a more data-oriented approach;

thus, we designed a system to track the time it takes to manufacture several orders comprised by

the products of interest (trophies, cups and medals), and then we extrapolated durations for the

overall fabrication process for each product. Using these standard lead times, we transitioned to

more evidence-based internal deadlines.

For setting the internal deadlines, we used Monday, a project management-oriented

software that will allow the sales department to assign time lapses to the production department

and keep track of the manufacturing process progress for reach order in real time.

To estimate the time it will take for any given order placed by the customer to be

produced, our team proceeded to measure the production time for the three products with the

highest demand (medals, trophies and cups). We made three measurements for each product and

calculated the averages of those measurements, which we used as the standard lead times for

each category. Using a project management tool (Monday), we created templates with the

standard lead times for the three products; by inputting the specifications of any given order

33

(product type and product quantity), the software multiplies those variables, resulting in the

actual lead time for the order.

4.4.1 Unitary Production Calculation

For each product of interest (medals, trophies and cups), the associated lead times are

calculated as mentioned above, with the addition of a cushion time of 5% of the total lead time

for that specific order. The specifications of each order are also introduced in the project

management tool as mentioned above. However, in the case that an order cannot be finished

before its deadline, the sales department will receive directions to contact the customer and

reestablishing a new deadline that fits the customer’s needs.

4.4.2 Limitations of New Due Date Estimation Method

In our new due date estimation methods, we found that we have two main limitations:

WIP and production methods. We do not account for work that is already being produced or

scheduled prior to any incoming order. This might have a big impact in the case that the

company is working at a high capacity or over capacity. Also, the Monday software accounts for

unitary production time of products multiplied by the quantity of the products in the order.

However, with this we are not taking into account the fact that at some steps in the actual

manufacturing process, the products are produced in batches; this dramatically decreases the

theoretical rate at which products can be produced using the software, when compared to the

actual achievable production rate when producing in batches. In other words, measurement of

lead times is considered in unitary production, disregarding the benefit of production in batches.

34

5. Conclusion, Recommendations, and Reflection

5.1 Conclusion

The manufacturing company, Madera y Metal, is seeking to improve their on-time

delivery rate. After collecting data and analyzing it, several different countermeasures were

developed. The current project was done with a limited set of data and information which

resulted in a more simplistic approach to the solution design. Although the solutions are not as

complex as they could be, they still can provide value to the relatively small company.

Our countermeasures focused on three specific problem areas within the company:

responding to seasonality, the prioritization of orders, and the estimation of order due dates. Each

of these problem areas was determined to be a factor in the bigger problem of poor on-time

delivery performance. Based on our understanding of the data, and the design of each solution,

the rest of the chapter provides several recommendations for Madera y Metal to consider as they

continue to improve their processes.

5.2 Recommendations

Our first recommendation is for Madera y Metal to implement the designed protocols in

response to seasonality and increase in demand. By utilizing these protocols, Madera y Metal

will be prepared to meet a sudden increase in demand and can improve their on-time delivery of

products. Our second recommendation is for the Madera y Metal sales department to use

Monday Software to track orders. By inputting the product type (trophy, medals and/or cups) and

volume (amount of product/order), they will automatically have estimated lead times, by using a

unitary production measure, which will provide information on the minimum suggested due date.

35

Finally, our last recommendation is for the new production floor supervisor to keep track of data

in Monday Software. This will allow them to be able to input data into the Multi Attribute

Decision Making Tool that will help guide the priority of customer orders based on the order’s

lead time, and if the order will be late or is already in production. A second role the new

supervisor should play is focused on eliminating the communication gap between the sales

department and the production floor. The software and tool implemented will help the entire

organization be more aware of what is in production and have cleaner data to keep track of

orders.

5.3 Reflection

5.3.1 Design Reflection

The design process is used to develop systems or products that meet company or

customer needs. There are many different variations of a design process. The method our project

follows is that of an A3. The A3 model first starts by defining the problem, then moves onto

background information on the current conditions within the company, identification of root

causes, development of countermeasures, implementation, and evaluation of results. Instead of

creating physical machines or products, industrial engineers design processes, techniques, or

models.

To design the countermeasures used for this project, data collection, analysis, and

background research was used. After each countermeasure design, our project team, advisors,

and members of Madera y Metal provided necessary feedback. The feedback was used to help

finalize the countermeasures.

36

5.3.2 Design Constraints and Impact

An important consideration in the design process is considering various constraints. In

constructing our countermeasures, we were limited in the amount and type of data to be used. A

large reason for this was the lack of current database upkeep and integration within Madera y

Metal. Therefore, our countermeasures are approximations of the actual situation, and may not

capture the best picture. Thus, our countermeasures must be monitored and continuously

improved to ensure they provide the best result to the company.

If Madera y Metal were to consider and implement some of our recommendations, it

would greatly improve their relationship with their customers. By improving their on-time

delivery, customers will appreciate the reliability and timeliness of the company.

Looking at the bigger picture, sports in Paraguay are very popular. Soccer is a large part

of the culture in Paraguay (Wood, 2014). With an improved reputation in the community,

Madera y Metal can look to be a major contributor to both recreational and professional soccer

tournaments.

5.3.2 Lifelong Learning Reflection

Working on this project allowed our team to apply what we have learned in the classroom

to a real-world problem. We were able to get first-hand experience working with an external

organization and were responsible for meeting the expectations of our university and our

sponsor. The data collection, analysis, and countermeasure design were beyond anything we

have done on in-class work. The variety of topics that this project covered required further

understanding and learning of familiar and new concepts.

37

Over the course of this project, our professional speaking skills have improved

significantly. Each one of us has become more comfortable presenting to an audience, as well as

deep discussions with professionals. These skills will be a great benefit for us after graduation.

When we started working on this project, the company had very little put in place to

combat on-time delivery. However, the desire to improve was evident, and the company itself is

hoping to take even more initiative in developing their processes. The work we conducted with

this project has helped to provide potential solutions that will help in the company’s future

aspirations, but they are not limited to just these solutions.

5.3.4 Project Teamwork

Our team met on a weekly basis with our advisor, another time with our sponsors and

another by ourselves in order to guide our project, to make sure that the teams’ objectives and

our sponsors objectives are aligned. Each team member took different equally important

leadership roles in organizing data, material and information, in defining the problems and

countermeasures, and another role in cross functional communication between our team and our

sponsor. All members of the team contributed with ideas and worked on the implementation on

the different countermeasures. Everyone's voice was heard, and all members contributed in

defining goals while having Madera y Metal’s best interest in mind. When planning the work

everyone contributed and was able to bring their own strengths to the table. Team members were

always available to help each other fulfill the objectives and did not limit their responsibilities

upon the aspects of the project they took leadership on.

38

References

Cholette, (n.d.). Forecasting and Aggregate Planning [PDF File]. Retrieved from

http://online.sfsu.edu/cholette/SDCM/ppt/SCC-chapters8-9.pdf

Ciocoiu, C.N., & Ilie, G. (2010). Application of Fishbone Diagram to Determine the Risk of an

Event with Multiple Causes [PDF File]. Retrieved from http://mrp.ase.ro/no21/f1.pdf

Ding, T., Liang, L., Yang, M., & Wu, H. (2016). Multiple Attribute Decision Making Based on

Cross-Evaluation with Uncertain Decision Parameters. Mathematical Problems in

Engineering, 2016, 1–10. doi: 10.1155/2016/4313247

Hyden, D. (n.d.). Case Studies/Results. Retrieved from

http://www.tpslean.com/resultsall.htm#sheetmetal

Icasas, P. (2017, May 18). Project Management 101: What is a PICK Chart? Retrieved from

https://explore.easyprojects.net/blog/project-management-101-pick-chart

Lead Time - Overview, Components, and How to Reduce LT. (n.d.). Retrieved from

https://corporatefinanceinstitute.com/resources/knowledge/other/lead-time/

Orbit. (2014, January 15). Maintenance Record Form. Retrieved from

http://www.inpaspages.com/maintenance-record-form/

Pojasek, R.B. (2000). Environmental Quality Management [PDF File]. Retrieved from

http://faculty.washington.edu/rsmcpher/Class%20Cases%20and%20Assignments/5%20

Whys.pdf

Quality-One International. (n.d.). A3 Problem Solving. Retrieved from https://quality-

one.com/a3/

39

Rahani, A., & Al-Ashraf, M. (2012). Production Flow Analysis through Value Stream Mapping:

A Lean Manufacturing Process Case Study. Procedia Engineering, 41, 1727–1734. doi:

10.1016/j.proeng.2012.07.375

Roser, C., patel, D., Roser, C., Stekelenborg, R. van, Podziewski, B., & Mall, A. (2018, April

16). How to Prioritize your Work Orders – Basics. Retrieved from

https://www.allaboutlean.com/how-to-prioritize-work-

basics/?fbclid=IwAR19nZEKBgLeXUgAb32p4XChO_GZTDzTQX76dBQUSzAnp0jyx

WxIxDTfqtY

Snapp, S. (2019, November 30). Shaun Snapp. Retrieved from

https://www.brightworkresearch.com/supplyplanning/2017/01/24/how-to-best-calculate-

lead-times/

Thomsen, R. B. (2019, October 30). 7 Proven Customer Loyalty Programs That Work. Retrieved

from https://sleeknote.com/blog/customer-loyalty-programs

Vinas, T. (2018, May 6). Knife Company Hones Competitiveness by Bucking the Status Quo.

Retrieved October 10, 2019, from https://www.lean.org/common/display/?o=811.

Wood, R. (2014). Sport in Paraguay. Retrieved from

https://www.topendsports.com/world/countries/paraguay.htm

40

Appendices Appendix A: Process Map

41

42

Appendix B: Case Studies

Lean in a Knife Manufacturing Company

Tonya Vinas describes the application of lean in a knife factory. Reading this case study,

we discovered a couple of important aspects that we should consider for our project (Vinas,

2018). First is that the price of metals can be volatile and therefore in order to consider real cost

and not those estimated when models were made with a lot of assumptions. In our case it is even

more important to update to real costs since most customers make their own designs, so products

cannot be catalogued priced exactly since the prices of products are subject to change depending

on the design and the actual cost of the materials. Furthermore, another thing that was useful to

find out in this lean implementation case study was how they tackled the inconvenience of

having an uneven demand. To deal with this the following was implemented and can be taken

into consideration; a variety of Kanban systems, reducing the inventory of finished goods (since

in our case it is already a demand pull system this is not a big concern), level loading production

planning for biggest clients, which makes it easier to react for a situation in where material

inventory is running short. Finally, the 5S philosophy was also implemented, where the 5S

stands for: separating needed and unneeded items, neatly arranged what is left, clean and wash,

regularly repeating the first three steps to maintain cleanliness, and finally discipline. It is

important to implement good visual control so that this philosophy can be followed and used as a

LEAN implementation.

43

Value-Stream Mapping in Malaysia-Japan Automotive Industries

Another method used in lean manufacturing is Value-Stream mapping. An automotive

corporation located in Asia had been experiencing problems with daily production forecasts for a

specific disc assembly (Rahani, 2012). Upper management took action by proposing a lean

approach to solving the issue. The goal of using lean was to eliminate waste, maintain inventory

control, and improve product quality.

Using mapping techniques and GEMBA walks, the corporation was able to identify

bottlenecks within the production system. The mapping techniques that were used included a

current state map. The current state map was created by walking along the process flow and

interviewing the operator at that process. This technique allowed them to also collect data on takt

time and cycle time.

Takt time and cycle time can both be used to determine if there are any bottlenecks or

constraints in the production line. When analyzing the data, if the cycle time is greater than takt

time, that process is a potential problem area.

By using VSM, the company was able to eliminate wastes and reduce causes. Along with

VSM, the company used cycle time evaluations to pinpoint areas that needed to be changed

(Rahani, 2012).

44

Sheet Metal Stamping, Forming, and Painting

Problems: Pressure from their customers for shorter lead times and improved on-time delivery

performance was causing this privately held company severe difficulty. They faced a very real

risk of losing some critical accounts. Key customers were demanding “next day” shipment. They

expected to be able to place an order today and pick up or have their order shipped first thing the

next day. The company had spent many months attempting to make the transition to lean on their

own, with minimal success. Lead times were still too long, and delivery performance was

unacceptable.

Lean Solutions: In addition to the more traditional lean techniques such as instituting Kanban

controls and improving change-over times, this client required some more fundamental

disciplines. Step one was to immediately change the attitude regarding schedule attainment. Brief

all-employee meetings were held on all shifts. The need for absolute schedule adherence was

explained and personalized: “A commitment date to a customer is a ‘promise’. How do you feel

when someone breaks their promise?” We instituted a simple policy: The day ends when the

schedule is complete, … NOT the other way around. Overtime was authorized and basically

automatic if needed to attain the daily schedule. The next step was to “stagger” the shifts so that

each shift could be held accountable for schedule attainment. Improvement curves (goals) were

set by the employees and monitored daily. The next problem was in creating flexible capacity

(the “rubber” factory). Like most companies, most of their work force was on the first shift. This

posed a problem: Many orders were received late in the day, for next day shipment. They

obviously needed to change the balance of the shifts. Goals were set to gradually move toward a

45

50/50 balance of the day and night shifts. This was accomplished through volunteers,

replacements for attrition, advancement opportunities, and rate adjustments.

Impact/Results: Within weeks, on-time delivery was at or near 100%. Average lead times were

cut from two weeks to three days. Set-up times and lot sizes were cut by 75%. Total inventory

was reduced by 50%, and one entire building was freed up for new product introduction (Hyden,

n.d.).

46

Appendix C: Late Order Form

47

Appendix D: Order Tracking Sheet

48

Appendix E: Monday Software

Screen shots of Monday software.

49