LSCM 3403 Term Paper_Final1

Brooks Industrial Metals
LSCM 3403 Section: 006
November 23rd 2015
Contributors: Calvin Bissett, Justin Duenk, Chris Neely, Tarace Hannah,
Erik Queenan
TERM PAPER

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TABLE OF CONTENTS
Table of Figures (Part of but separate from appendix)........................................................................................1
Executive Summary .....................................................................................................................................................2
Introduction.....................................................................................................................................................................3
What BIM Offers ....................................................................................................................................................4
BIM Customers.......................................................................................................................................................4
BIM Inventory ........................................................................................................................................................4
Problem Identification .........................................................................................................................................5
The Initial Plan.......................................................................................................................................................5
Industry Analysis ...........................................................................................................................................................6
Literature Review ..........................................................................................................................................................7
Data Analysis...................................................................................................................................................................9
Background ...............................................................................................................................................................11
Data Model (BIM Inventory Production Model) ..................................................................................................12
In the final analysis..................................................................................................................................................13
Further Insights...................................................................................................................................................14
Recommendations .......................................................................................................................................................14
Term Paper Conclusion ..............................................................................................................................................15
References......................................................................................................................................................................16
Appendix ........................................................................................................................................................................17
TABLE OF FIGURES
Figure 1...........................................................................................................................................................................19
TABLE OF TABLES
Table 1...............................................................................................................................................................................4
Table 2.............................................................................................................................................................................12
Table 3.............................................................................................................................................................................18
Table 4.............................................................................................................................................................................18
Table 5.............................................................................................................................................................................18
Table 6.............................................................................................................................................................................19

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EXECUTIVE SUMMARY
This report is a compilation of four months’ worth of research into the intricacies of
cutting edge inventory management theory. An analysis using what we’d learned from
our research was applied to real inventory data provided to us by Brooks Industrial
Metals (BIM). The intent of the report is to inform the reader about the steps that were
taken to accomplish our eventual goals with justifications for why we chose to do it this
way.
The term paper is made up of the following sections:
Section Brief Description
Introduction Basic overview of BIM, and their issue; problem identification and proposed solutions
Industry Analysis Description of Steel Industry, its relation to inventory management, identifying important
aspects of the industry and w ith relation to logistics and supply chain management
Literature Review Breakdow n of inventory managements importance, using sources to prove how BIM’s SC can
be improved or take after previous studies
Data Analysis Review of Data sources, where data wassourced from, who, what data wassourced, and
w hy. Importance and relevance of data sourced and its significance to our problem
Data Model Model created by our group and how it assists BIM in improving there SC and inventory
management
Recommendations What BIM should do in the future to improve their SC/organization.
Conclusion Summary of findings and research
Our eventual goal was to apply what we’d learned about inventory management and the
ABC classification system to BIM’s entire product line. Our group eventually succeeded
in doing so.
After several meetings with BIM and their software providers Bayern Software we were
able to get an exhaustive set of inventory data that had never been seen in its entirety,
let alone used. We focused a great deal of our time on its acquisition before attempting
to classify anything.
After much ado, we received the data, appended our new classification system to
approximately 250,000 individual records. The class of all items in the current inventory
is readily available in the Model in the pivot table on the Worksheet entitled “Year over
Year” (see Fig 1 for picture) for BIM’s use at any time, at the click of a button.

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In addition we built several charts and tables within our teams newly created “BIM
Inventory Production model” to drill down on product lines to identify and assess
successes and failures in their inventory, purchases and sales numbers.
Up until now this was completely impossible. We believe that with the model’s use, the
total savings and insight derived is incalculable but very likely into the 100’s of
thousands of dollars range based on our estimates. If in addition to the model we built,
more standalone models (Purchase and Sales) were also built, more insight could be
realized and total savings and profits would be further improved.
The model ties together 7 years’ worth of historical data ‘08-’15, so it’s legitimately
longitudinal, and also broad in that it covers sales, inventory and purchasing data on all
products.
Based on the insight derived from this model better decisions can be made in terms of
what items sell best, provide the most profit, and cost the least relative to price well into
the foreseeable future based on the hard evidence taken from the model. Moreover, BIM
can now decide based on actual sales and purchasing data which products should be
discontinued, and whose pricing should be adjusted in order to improve their bottom line.
However, two major grounding assumptions must be made to make these claims:
1. Data integrity must be verified. A stock count of every item must be done to
ensure the model’s accuracy.
2. Calculations in the model should be verified and deemed correct by BIM.
INTRODUCTION
Brooks Industrial Metals (BIM) is a steel service center and fabrication shop located in
Brooks, Alberta. The rapidly growing company is a family run business, and has been
serving Brooks and southern Alberta for over 49 years. First established by Joe Duenk in
1966, BIM started out as a one-man, radiator repair shop that sold small quantities of
steel to local farmers in the area. In 1996, John Duenk took over as president of BIM and
continued to grow the company on a larger scale, while still keeping the cores and
values of a family run business. Today, BIM has developed into a “One stop, steel shop”
that employees over forty-five workers and covers eleven acres of land.

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WHAT BIM OFFERS
Being a full steel service centre, BIM offers a wide variety of metal products and
fabrication services that include:
Table 1
Products: Services:
 Pipe  Flatbar  Bending
 Beam  Rebar  Molding
 Tubing
 Sheet
 Channel
 Angle Iron
 Plate
 Nuts, Bolts, Tools/tool parts, safety gear etc.
everyday metalw orking consumables.
 Cutting and Shearing
 Etching
 Rolling
 Assembling
*Materials: Steel, aluminum, and iron of varying carbon content.*Machineryincludes: CNC Plasma cutter, Laser cutter,
Hydraulic Brake, and Shears.*BIM also offers specialtyproducts such as customized fire pits, signs, and keychains for
customers looking for a unique/custom aesthetic
BIM CUSTOMERS
Over the past 49 years, the company’s target market has progressed from sale and
fabrication for local farmers to selling and fabricating steel for the larger oil and gas
(O&G) companies. BIM also serves variety of welding and machine shops in southern
Alberta. Although the majority of BIM’s customers are now O&G (≈80%), the company
places a high value on its everyday local/long-time customers, with specific emphasis on
creating and maintaining customer's lifetime value. Brooks Industrial Metals is proud to
offer a penetration pricing strategy, high customer loyalty, and flexibility in the types of
products they have. It should also be noted that they often exceed their customer's’
expectations and as a result they have grown substantially over the years. BIM is
certainly a “going concern” despite the recent downturn in the price of oil.
BIM INVENTORY
BIM’s growth has translated into a wider breadth of products and services, as well as
greater flexibility in terms of final product offerings. On the one hand, it offers customers
product flexibility, however, it presents a significant challenge for the management of
inventory. Therefore in terms of inventory, BIM must be diligent in making sure they can
meet customers’ demands in a timely manner and that they carry only what they can
realistically sell. Many of the products BIM carries are very heavy, large and awkward,
and usually have to be maneuvered by crane or forklift. They have implemented their

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own practices when it comes to storing what they have. However, it is in the opinion of
this group that more can be done.
Since 1997, and still today, BIM has been using an inventory software system called
“Steelplus” to manage the storage of their wide product base. Steelplus was developed
and sold to BIM by Bayern, which is a third party metal fabrication software developer.
The software system has a limited number of seats and the report outputs are
rudimentary but useful if used regularly as a part of a procedural norm. It has the ability
to be used to inform the BIM front office staff and management of every type of metal
that they have in stock by either weight, quantity, or area in square feet.
PROBLEM IDENTIFICATION
During ending year inventory counts, BIM has consistently written off inventory for
unknown reasons. The company's inventory software “Steelplus” counts do not match
the stock that is actually sitting in the warehouse! Whether by software glitches,
employee errors, theft, or any other reason, it's causing unnecessary losses of revenue
for the company.
As an example, a customer may come in and ask for two lengths of square tubing.
Although BIM’s inventory system may say that there are more than two lengths available
in stock, the shop employees go into the yard to load the material for the customer and
the material is unable to be located. This problem is essentially causing dissatisfaction
for customers when BIM does not have the required materials on hand.
As a response to its inventory issues, BIM management has been counting some of its
more popular inventory items every two months as a way of reducing written off
products. President of BIM, John Duenk, says that although a two-month periodic
inventory count alleviates these problems partially, it does not eliminate the issue
completely, and inventory management is still a concern for the company. Duenk also
states that “inventory issues are mainly arising from carrying too much stock”. He says
that this is caused by the combination of poor product management, and the change in
customer requirements over time, which leaves BIM stuck with inventory that doesn't
move.
THE INITIAL PLAN

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Initially as stated in our proposal, our plan was to focus on the SteelPlus inventory stock
points from a small set of front desk consumables like gloves, bolts, and welding
equipment etc. Since then, the plan evolved as a result of the fact that our initial data
drops were relatively small compared to the data we ended up receiving later on.
After discussing strategy with management at BIM, our team has decided to research
the topic of inventory management, and how a new, fully utilized, or improved inventory
system might be of great use to them. Our team will be categorizing the entire inventory
based on seven years of data, by using a modified ABC classification system. This is
laid out and discussed in detail in the Data Analysis section of this report.
INDUSTRY ANALYSIS
The steel manufacturing industry has always played a major role in Canada’s
manufacturing sector. More than 14 million tonnes of steel was manufactured in 2012,
and it accounted for $6.8 billion dollars of Canada’s total exports (Canadian Steel
Producers Association, 2012). Its total sales amounted to over $12 billion for the year of
2012 (Canadian Steel Producers Association, 2012). According to the Canadian Steel
Producers Association, steel is the more recycled material in the world and Canada
alone recycles 7 million tonnes annually, making it a highly renewable resource (2012).
The United States is the largest trade partner for steel exports and imports in Canada,
making it the most important trade partner in the industry. Canada’s steel industry
suffers from a trade deficit of $2.6 billion (Industry Canada, 2015).
Industry Canada evaluates the steel product manufacturing industry in terms of value
added (2015). Value-added calculates the overall increase in value of a product as it
continues to be processed and manufactured. This method is best applied in the
manufacturing process because there are often several processing variations across
different business and industries. Value added provides a way to track the contribution
made by the sub-sectors of the steel industry (Industry Canada, 2015).

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Most of the businesses that specialize in the steel manufacturing industry are considered
to be small enterprises. In fact, the majority (41.8%) of them are categorized as micro
businesses, meaning that they employ 5 or fewer workers. This can be a result of market
saturation, a decrease in demand, geographical location, or the use of technology as a
replacement for employees (Industry Canada, 2015).
NAICS codes were used to identify the different, more specific industries involved in the
production and manufacturing of steel. The industry identified for the purpose of the
project is laid out in NAICS code 332999, and is classified as “all other miscellaneous
fabricated metal product manufacturing,” (Statistics Canada, 2012).
This industry is responsible for the manufacturing of several products, using different
types of material, examples include, chests, fireplace fixtures, safes, pipe and pipe
fittings, tubing, and flexible metal hose (Statistics Canada, 2012).
LITERATURE REVIEW
This Literature review will explore the risks of ineffective inventory management as well
as the benefits of effective inventory management. It will then look at the importance of
inventory classification, specifically the ABC classification method.
Inventory is important because not only can it represent a large portion of a company’s
assets but also costs the company in storage costs (Stevenson, W. J. 2015, pg. 502).
Trying to manage inventory can be a balancing act between over and under stocking.
Under stocking can result in a slowing or stopping of production, late and missed
deliveries, not to mention loss in sales and dissatisfied customers (Stevenson, W. J.
2015, pg. 463). When looking at the risks associated with under stocking a logical
conclusion would be to simply over stock. While there is some truth to this the cost of
over stocking can be overwhelming for companies (Stevenson, W. J. 2015, pg.
463). Looking at the worst-case scenarios for both extremes it is easy to identify the
importance for business to properly manage its inventory in order to remain competitive.
When a business has a tight control of its inventory not only does it mitigate some of the
issues already discussed but it can also provide a variety of benefits to the overall
operations of the company. Proper inventory control means that there will be improved

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inventory accuracy that will help purchasing managers make smarter decisions when
looking to reorder (ACCU-DART, 2009). Using an effective inventory control system will
help employees simplify work processes and practices making their work more effective
and productive (ACCU-DART, 2009). Perhaps the greatest and most meaningful benefit
to properly managing inventory is the saving of time and money (ACCU-DART,
2009). The savings come from a higher level of efficiency that comes from laid out
processes that reduce the total work hours as well as having the potential to reduce
waste (ACCU-DART, 2009).
In order to properly manage its inventory in an efficient way “companies should first
classify their inventories” (Rezaei, J. and Salimi, N. 2015, p. 1944). The ABC
classification method is a traditional method that categorizes a company’s inventory into
three classes: A- inventory of extreme importance, B- inventory of average importance,
and C- Inventory of low importance (Rezaei, J. and Salimi, N. 2015, p. 1944). Usually
when using ABC classification Patero’s Law is applied to help businesses determine the
where each control level will be placed (Rezaei, J. and Salimi, N. 2015, p. 1944).
Patero’s law says that approximately 10-20% of the total inventory for a company will
make up between 60-80% of the companies total inventory value, this group of inventory
would be classified as class A. Class B, according to Paetro’s law makes up about 30%
of the total inventories while accounting for 25-35% of the total value. Class C will make
up the remaining 50-60% of total inventory and making up only 5-15% of the total
inventory value (Rezaei, J. and Salimi, N. 2015, p. 1944). The ABC method is relatively
easy to understand and apply. The ABC system traditionally takes into account 2
different parameters for each item: annual demand, and item price (Rezaei, J. and
Salimi, N. 2015, p. 1944).
The ABC classification system has many benefits, yet is not without its share of
drawbacks. It is important to look at what some of the disadvantages of the system are
in order to try and circumvent problems when using it. One of the most obvious problems
with the ABC classification system is that inventory is classified on the monetary value of
each item and it may ignore some of the other important factors (Chand, S. 2015). There
is also a risk of using this system if there is not proper standardization of materials that
make up the inventory; this includes having inventory properly codified when being
added to the inventory (Chand, S. 2015). While there are drawbacks there are many

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positives that make this a popular classification system (Chand, S. 2015). The ABC
system helps keep a greater level of control over the most expensive items of inventory
which company has invested a considerable amount of resources as well as reducing
clerical costs due to the processes that are developed (Chand, S. 2015).
DATA ANALYSIS
In the Steel Production and Fabrication Industry, inventory management is key to the
success of any operations management department as it is tied to profit. BIM is no
different. Maintaining a smooth SC requires hardcore analysis. So that’s what we did. To
assist in improving BIM’s SC and inventory management actionable intelligence is
required, that’s why we built a model of our own using actual numbers from the
SteelPlus database from the past seven years. The raw data in this report was exported
by Bayern Software a Third Party Company, as well as from BIM staff.
The large data set we worked with allowed the team to do a thorough analysis on the
current inventory situation and allow identification of problems and formation of
recommendations on how to improve the current SC situation.
The focus of our team was to assess key areas related to the supply chain specifically,
stock levels (intervals), classification/categorization, forecasting, with the overarching
goal of getting the full use of BIM’s current software.
To assist in identifying current SC issues an internal purchasing data model containing
purchasing and spending intelligence was created. Among the various areas analyzed
chief among them were: actual sales, contribution margins, and amount purchased.
Aberrations between amount purchased and sold were identified. We also highlighted
using charts and Pivot Tables where we believe products could stand to be more
profitable.
While investigating BIM’s purchasing data model, we discovered that there was no
formal process point to order/re-order at. Historically it seems, after discussing with
BIM’s staff, decisions about what, when and how much to order depends largely on their
years of experience trading in the market, and the “intuition” of staff to order stock
correctly. We wondered if this would hold up to analysis when we looked at the actual

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numbers. Obvious holes in the system in regards to the amount of stock purchased and
stock sold have incurred lost revenues in a wide variety of products. This is especially
evident in the Purchasing, Sales Charts, and the Year over Year Charts from the Model.
At times BIM would stock out, and have demand for products they no longer had in
stock, at other times BIM would be left with hundreds of pieces of useless stock. Also
this created a situation where BIM would buy products that they were unknowingly
making a loss on. Without proper inventory records BIM was making that could be easily
avoidable.
The purchasing worksheet of the model we built highlights what BIM stands to gain from
instituting a legitimate a forecasting tool based on a standalone purchase forecasting
model. It would help to understand current demand and in turn order more accurately.
Also adding purchase points to the company process; so as to continue the flow of
goods in demand throughout the year. This in addition to a sales forecasting model is
certainly justified based on the insight derived from our model. This model could be a
stepping stone to the next two models: sales and purchase forecast models.
In addition to our general insight based on the data collected, our group’s main focus
was in relation to the scope of the LSCM class. We implemented a total classification
overhaul on their current system to correctly class each product based on actual sales,
and contribution to operating income. We used two separate five tier classification
codes: (A,B,C,D,E) and (1,2,3,4,5), the first based on total sales and the second based
on contribution to operating income using the cost of goods sold and the total revenue.
Products are placed in groups based on “percentile ranks” based on a normal Z
distribution normal curve. This creates a situation where BIM can view the top 20th
percentile, 20th
and 40th
percentile or any combination depending on the preference of
the model user. If the user wanted to view only product line that has the highest level of
sales in tandem with their contribution to operating income, and conversely the bottom
percentile range they can do so at the click of a button (see Fig.1). The actual pivot table
is located in the “Year over Year” worksheet in our model, to the right of the charts. The
difference between our system and a normal ABC classification system is that the
classic design focuses on unit costs as the classification determinant. Our group decided
that this is an irrelevant factor in comparison to total sales and contribution to operating
income i.e. what does the cost of a product matter? If the product does not sell, and if

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the selling price is not also at least slightly above cost, then there is no point in having it
at all. Thus identifying and tracking these product based on our criterion was deemed
better.
Total sales is the most important variable in such a classification system because sales
it takes into account the total quantity sold and the unit price. Contribution to operating
income, on the other hand, is a great determinant as it factors in the amount of profit
each product generates in real profit after cost, and before taxes. We believe this
system is better than the conventional ABC classification system as it focuses more on
revenues and profits rather than on the unit costs of the product. Knowing this
information at a whim will certainly help BIM.
BACKGROUND
In the beginning there was very little data provided to us from BIM in terms of what items
they currently stocked, there relative price, and unit cost. Information on the purchase
price and price those items were sold at was by and large unknown. All we received for
the initial “data” is in Table 3.
After talking face to face with the Operations Manager Cole Hauck we were provided
with a list of ten products and information on their total selling price for the past 5 years.
At the time, he had compiled this list by running .pdf reports and manually enter
information into excel by hand. At that point in time there was no way to get at the
information from which Steelplus drew its reports. A conversation with BIM about how to
get access to the relational database under Steelplus and involving Bayern seemed to
be the next prudent step.
We made contact with the relevant parties at Bayern Greg Bayer and one of his data
analysts Jason Roybal in order to see if it was even possible to export the relevant fields
into an “.xls” file so we could do a more thorough job of analyzing the entirety of their
inventory data. After a few discussions with the CEO John Duenk at BIM we got
authorization to spend some money on getting Bayern Software to export the data using
a software called “relativity”. In total over two days we received four data sets.

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DATA MODEL (BIM INVENTORY PRODUCTION MODEL)
* For the purposes of this report a full explanation of the intricacieshowthe BIM data model was created please consult
Calvin Bissett. It’s not necessaryor pertinent to this operations class, however the insights drawn from the model are of
paramount importance and critical to the project’ssuccess.
After receiving the four data sets in Table 6 we used a combination of Microsoft Access
and Microsoft Excel 64 bit to parse, append, reconfigure and calculate several different
fields in each “calculated table”. A more thorough explanation of what was done to the
data can be found in a brief, but not exhaustive, walkthrough document included in the
folder with the model. The model went through over 7 separate (Dev or Development)
iterations before arriving at the one presented with this report. This is the official “BIM
Inventory Production Model”.
Table 4 illustrates an interesting problem that we addressed was in applying the correct
formula for calculating the cost or price of a unit. We had the data, but we had to build
several functions that work sequentially to leverage excels existing functionality to
decide which formula to apply to each item based on their newly appended and created
cost code (also created by CB) in order to calculate its cost or price correctly. In some
cases the purchase price of an item could be calculated based on weight, however when
sold it could be based on length, or length and width (square-footage) etc. This was not
an easy task.
After this, we could move onto creating some useful pivot table’s and charts to begin to
get some insight into the problematic areas at BIM with regard to Inventory (see section
“In the final analysis”).
The workbook is broken down into ten separate worksheets see Table 2:
1. First set of 8:
Table 2
Calculated Tables Related Pivot Charts and Pivot Tables
Historical_Data_2008_2014 Historical Data
Currrent_Inventory11_11_2015 Inventory
Purchases_2015 Purchases

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Sales_2015 Sales
2. Second set of 2:
 Year over Year: Some fields from all the above tables are included in the Pivot
Tables on this Worksheet
 Relationship: Ties BIM inventory production model together
In all cases, it seemed prudent to include date as a relevant filter field. The following
table includes the possible filters for the data you can apply to the charts to only look at a
certain segment of information for greater accuracy or conversely look at the broad
strokes over a longer time span.
We then proceeded to classify the products based on their YTD sales numbers and their
relative contribution to operating income.
IN THE FINAL ANALYSIS
Some of the problematic areas apparent when using the model:
1. Contribution Margins
a. Too high
b. Too low
c. Negative in some cases
2. Quantities purchased:
a. High frequency, low volume purchases (higher purchasing costs), this is
particularly problematic for products with a high inventory turnover.
One example of each of these issues respectively using the model:
1. “BEA” category in 2015 has cost BIM $140,000.00 year to date (Fiscal 2015)
because it has a negative contribution margin.
2. Is there a cost savings that could be realized by buying a storing more of the
BEA product line if in addition to adjusting the price in order to compensate for its
high unit costs this Fiscal year 2015.

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Obviously, even if this group wrote down all of these issues in bullet points the potential
changes that could, and potentially should be taken (like the example above) it would
exceed the page limit on this report. It is up to the reader of this report and the user at
BIM to look to the model to draw these insights themselves.
FURTHER INSIGHTS
It is in the opinion of this group that the newly built and fully functional data model is the
ultimate tool for BIM to see anything and everything there is to know about their current
and historical inventory data from 2008 to 2015. Depending on how the charts are
filtered and modified by the users the insight that can now be drawn at the “click of a
button” is incalculable, but likely very large in terms of real cost savings. This model also
has the potential to help BIM to make well-founded and data-driven business decisions
in real time. All of the information in the tables can be pulled into the Steel plus software
as all of the fields were appended with unique identifiers. The source data remains intact
and unchanged it’s only linked electronically to this standalone model. This way the
integrity of the initial data remains intact and unsullied.
RECOMMENDATIONS
1. Use the Data Model to gain valuable insight.
2. Hire a Data Analytics Team to update and maintain the inventory model on an annual basis.
3. Build a sales forecasting model (referencing the BIM inventory model)
4. Build a purchase forecasting model (referencing the BIM inventory model)
5. Identify products with negative contribution margins.
a. Discontinue these products
b. Increase your price where possible
c. Lower your costs where possible
6. Identify products that are not selling and discontinue or increase price or lower costs.
7. Reorient facility layout based on higher volumes products purchased or sold as result of the
fact that they are the products with the greatest levels of activity (moving around the facility,
being exchanged, packaged etc…)
8. Rethink the company organizational chart to give employees and managers definitive roles
and responsibilities.
a. Consider segmenting employees based on their relation to the different components
of the model: Sales, Purchases, and Inventory.

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b. Identify realistic and attainable goals for different teams. Identify KPI’s based on
for these sections using the model as a guide.
c. Track performance on a regular basis (assuming the model is maintained and
updated)
d. Measure Performance based on KPI’s.
9. Forecast and identify products with the highest turnover/sales numbers using the model.
a. Make consolidated product purchases less frequently and in bulk to realize better
cost savings by way of greater economies of scale.
10. Forecast sales, purchases, inventory numbers for the next year. In the future, using the
model, BIM can now take more calculated risks to realize greater gains while using the
actionable intelligence to ground their predictions.
11. Need to integrate lead times into the data to estimate the EPQ and HOC information.
12. To ensure data integrity, an inventory stock count would have to be completed, and the
model would have to be updated to reflect any discrepancies between the database and the
actual inventory.
TERM PAPER CONCLUSION
BIM is involved in an industry which requires high levels of inventory and products. The
steel industry demands a large amount of different products to meet customer needs.
With such requirements keeping track of inventory and having relevant data for inventory
management are paramount. Inventory management at BIM could be improved with the
tools we’ve created. At this point we can say based on the data BIM is losing revenues
because of low/poor nonexistent contribution margins. What it boils down to is that
they’ve lost the critical visibility into their purchasing and product line. BIM now has the
tools to track inventory information and manage their inventory correctly. Using the BIM
Inventory Production Model we created BIM can better assess key metrics on their
broad product line. This model will require maintaining, through adding and appending
new data. However, the benefits to BIM are huge and will support a much smoother
inventory management and purchasing decision process. Through inventory
management BIM can streamline their company, purchasing decisions, and product line;
reducing waste and cutting inefficiencies. Through analyzing real sales and by our
classifying their products, BIM has the opportunity to provide never before realized
visibility into and of their product line, how it is replenished, whether the product is
profitable, and in turn make more educated purchasing/sales and forecasting related
decisions.

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REFERENCES
ACCU-DART (2009) How Inventory Control Can Benefit Your Business: an ACCU-
DART White Paper Executive Concepts Inc. Retrieved on November 5th
from http://www.adssglobal.net/docs/ACCU-DART_White_Paper.pdf
Canadian Steel Producers Association. (2012). Steel facts. Retrieved November 13,
2015, from http://canadiansteel.ca/steel-facts/
Chand, S. (2015) The A.B.C. Method of Inventory Control System: Advantages and
Disadvantages Your Article Library retrieved on November 13th
2015
from http://www.yourarticlelibrary.com/inventory-control/the-a-b-c-
method-of- inventory-control-system-advantages-and-disadvantages/26150/
Industry Canada. (2015). All other fabricated metal product manufacturing. Retrieved
November 13, 2015, from
https://www.ic.gc.ca/app/scr/sbms/sbb/cis/definition.html?code=33299&lang=eng
Industry Canada. (2013). Canadian primary metals industry. Retrieved November 13,
2015, from http://www.ic.gc.ca/eic/site/pm-mp.nsf/eng/h_mm01908.html
Rezaei, J. and Salimi, N. (2015) Optimal ABC inventory classification
using interval programming, International Journal of Systems Science, 46:11,
1944-1952,
Statistics Canada. (2012). All other miscellaneous fabricated metal product
manufacturing. Retrieved November 11, 2015, from

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http://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD&TVD=118464&CVD=
118471&CPV=332999&CST=01012012&CLV=5&MLV=5
Stevenson, W. J. (2015) Operations Management, Fifth Canadian Edition. Canada.
McGraw- Hill Ryerson.
APPENDIX
1. BIM Data Model
2. BIM Organizational Chart
3. Purchase Orders
4. Income Tax Capital Cost Allowance Documents
5. Steel Price Index
6. Work Orders
7. Video content from visitto BIM’s fabrication shop

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Table 3
Table 4
Total Sales Contribution to Operating Income
Percentile Class Code:Based on Sales
Percentile
Class Code:Based on Contribution to Operating
Income
Percentile
1st-20th A 1 1st-20th
21st-40th B 2 21st-40th
41st-60th C 3 41st-60th
61st-80th D 4 61st-80th
81st-100th E 5 81st-100th
Table 2: *Note that a product can now be for example, A2, or A3 or B4, B5 or any other combination A-E 1-5.
*Illustrates the modified classification system we appended to the current inventory. See Model worksheet entitled “Year
over Year” (Pivot Table)
Table 5
Unit of
Measurement
Excel
“Code”
Cost Determining
Factor
Purchase Worksheet Sales Worksheet
Product
Stock
Length
Current
Stock On
Hand
(PCS)
AVG
Monthly
Sales Last
3 Months
(PCS)
AVG
Monthly
Sales Last
6 Months
(PCS)
Purchased
2015 (PCS)
Purchased
2014 (PCS)
Purchased
2013 (PCS)
Purchased
2012 (PCS)
Purchased
2011 (PCS)
Purchased
2010 (PCS)
Sold 2015
(PCS)
Sold 2014
(PCS)
Sold 2013
(PCS)
Sold 2012
(PCS)
Sold 2011
(PCS)
Sold 2010
(PCS)
1 1/8" x 2" Steel Flat 20' 256 16 21 0 705 470 705 705 915 480 659 528 693
2 3/16" x 1" Steel Flat 20' 259 15 14 310 0 0 620 310 310 177 484 266 294
3 3/16" x 2" Steel Flat 20' 263 102 91 310 2473 1083 2486 2325 2015 1593 1952 2236 1867
4 1/4" x 1" Steel Flat 20' 340 39 52 720 1110 0 480 720 720 270 306 378 261
5 1/4" x 2" Steel Flat 20' 211 69 69 480 1680 1800 1800 2160 1440 1402 1548 1823 1232
6 1/4" x 3" Steel Flat 20' 76 23 21 80 480 480 480 640 640 416 399 572 420
7 1/2" x 3" Steel Flat 20' 15 6 7 39 78 156 98 117 117 103 94 85 103
8 1/2" x 4" Steel Flat 20' 76 8 6 60 177 240 120 210 150 176 116 171 120
9 1/2" x 6" Steel Flat 20' 13 6 5 60 60 104 168 100 108 69 129 85 75
10 3/4" x 6" Steel Flat 20' 28 19 20 180 237 237 56 144 236 183 73 87 277

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Product YTD_Sales YTD_COGS Quantity
A 161,012,300.11$ 122,120,258.33$ 101,253
1 154,488,406.01$ 116,824,327.07$ 98,203
2 6,523,894.10$ 5,295,931.26$ 3,050
B 33,637,268.12$ 23,077,948.08$ 45,982
1 4,278,810.78$ 2,785,346.34$ 2,859
2 27,690,279.05$ 18,981,651.08$ 40,849
3 1,668,178.29$ 1,310,950.66$ 2,274
C 9,527,892.78$ 6,479,751.10$ 34,766
2 1,061,046.07$ 499,705.78$ 3,090
3 7,749,461.59$ 5,402,949.86$ 29,581
4 717,385.12$ 577,095.46$ 2,095
D 2,361,577.12$ 1,438,735.65$ 29,847
3 353,972.50$ 159,705.76$ 4,331
4 1,963,327.93$ 1,238,344.20$ 24,649
5 44,276.69$ 40,685.69$ 866
E 171,667.95$ 98,744.09$ 20,672
4 26,756.97$ 12,495.96$ 1,534
5 144,910.98$ 86,248.13$ 19,139
Grand Total 206,710,706.08$ 153,215,437.25$ 232,521
Figure 1
PC 80 Number of
Pieces
Number of Units
Purchased*Unit Cost
Number of Units Sold*Unit
Price
PCS 80
FT 70 Length of Piece Length of Item*Unit
Cost*Number of units
Purchased
Length of Item*Unit
Price*Number of units Sold
SQFT 83 Square footage
of product
Length*Width of item*Unit
Purchased
Length*Width of item*Unit
LB 76 Weight Total Weight of Item*Unit
Purchased
Total Weight of Item*Unit
LBS 76
CWT 67
TON 84
Table 6
Short Description Number of
Row s
Number of
Columns
Appendix Reference
Sales Data Sales Data April-Present
Fiscal 2015
42,223 16 1: BIM Data Model
Sub-Folder: “Sales”
Inventory
Data
Inventory April-Present Fiscal
2015
Sub-Folder: “Inventory”
Purchase
Data
Inventory April-Present Fiscal
2015
Sub-Folder: “Purchases”
Historical
Data
Fiscal Data 2008 to 2015 65,535 9 1: BIM Data Model
Sub-Folder:
“Historical_2008_2014”
Grand Total 249,903 46 N/A

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Classification of the entirety of the current inventory is shown above in a picture of the condensed pivot table on the Year
over Year Worksheet of the BIM Inventory Production Model can be seen here

LSCM 3403 Term Paper_Final1

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