Irving Tisuue - Team PK

8/6/2019 Irving Tisuue - Team PK

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Submitted To: Professor Benny MantinDate Submitted: December 6, 2009

MSCI 432: Applied Term

ProjectA case study on Irving TissueTeam PK

Prepared By: Team PKMatt Li - 20215251

Jung-Youn Moon - 20242261Winnie Phung20203794Martin Taylor - 2010111


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MSCI 432: Applied Term Project Team PK

Table of Contents

1. Executive Summary ............................................................................................................... 1

2. Goals and Objectives ............................................................................................................. 2

3. Project Scope ......................................................................................................................... 2

4. Company Overview ............................................................................................................... 2

5. Industry Analysis ................................................................................................................... 2

6. Customer Analysis ................................................................................................................. 3

7. Competitive Analysis ............................................................................................................. 3

8. Manufacturing Process........................................................................................................... 4

9. Process-Product Type ............................................................................................................ 510. Production Workflow Analysis............................................................................................ 6

11. Bottleneck Analysis ............................................................................................................. 7

12. Demand Forecasting ............................................................................................................ 9

13. Optimal Inventory .............................................................................................................. 16

14. Safety Stock ....................................................................................................................... 17

15. Overall Conclusions and Recommendations ..................................................................... 19

16. Appendix AWorkflow Diagram .................................................................................... 21

17. Appendix BForecasting Calculations ............................................................................ 22

18. Appendix COptimal Inventory Calculations ................................................................. 23

19. Works Cited ....................................................................................................................... 24


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Executive Summary

Throughout this report, we intend to address managements concerns regarding demandforecasting and the possibility of reducing inventory levels. By focusing on a tight project scope

of select Royale bathroom tissue products produced by the Toronto plant, we are able to applyconcepts from MSCI432 to provide recommendations for Irving Tissue going forward.

Given feedback that production lines in the Toronto plant are currently operating at around 95%capacity, a bottleneck analysis was conducted using limited data and found that a superiorwinding machine in a production line could better facilitate the production of SKUs with highersheet counts. By implementing this upgrade on one line and focusing all such productions there,resources could be freed up in the other lines.

Since Irving Tissue produces to next months forecasted demand, statistical forecasting wouldfit. By analyzing simple moving averages (SMA), exponential smoothing (ES), Holts andWinters methods of forecasting, we recommend that 6 month SMA be used for forecastingmedium-low level demand SKUs, and ES with an alpha of 0.8 be used for forecasting highdemand SKUs. While these methods outperformed the current method of sales force composite,they fared worse in aggregate forecasting across multiple SKUs, suggesting that sales forcecomposite is still more accurate at the higher level and for longer term planning.

Calculations of optimal Economic Order Quantity (EOQ) and Safety Stock (SS) showed thatexisting inventory levels are not far from optimal, and thus that there could be costs associatedwith forcibly reducing inventory levels in the future.

Goals and Objectives

Background analyses concerning the company, the industry, its customers, and its productionprocess are included within the report, along with a brief production bottleneck analysis. Thegoal of this project, however, is to address the specific issues of Irving Tissue regarding theirdemand forecasting and inventory management.

Irving Tissue is currently in the process of substantially revamping their production and supplychain management systems, with the hopes of improving inventory turnover from its currentlevels of 12 turns per year to a target of 18 turns. As part of this initiative, management hasidentified demand forecasting as a key area for improvement.

Contrary to intuition, it has been historically proven to be very tricky to accurately forecastdemand for bathroom tissue products, despite it being a very staple consumer product. Under thecurrent method of sales force composites, forecast accuracy tends to be around 60%, withsubstantial difference in accuracy levels between SKUs.

As part of the improvement process, management is considering implementing a complexforecasting system, and wishes to evaluate the effectiveness of general statistical forecastingmethods as a test of viability. This will be the focus of our applied project, along with otheranalyses related to the overall goal of increasing inventory turns.


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Inventory levels can be thought of as a combination of production cycle stock and safety stock.In order to reduce inventory and therefore increase inventory turns, this report will calculate theappropriate safety stock levels, as well as the optimal production size, which affects cycle stock.

Project Scope

The project will be limited to the activities directly involved in production of Royale bathroomtissue at the Toronto plant of Irving Tissue Canada. Furthermore, given more than 100 separateRoyale bathroom tissue SKUs and 5 complete lines of production in the Toronto plant, theanalysis will only cover a selection of SKUs, chosen primarily for having sufficient data and arerepresentative of different volume level products.

Company Overview

Irving Tissue began in 1988 and since then has grown to become one of North America's leadingtissue manufacturers, with facilities in Dieppe, New Brunswick, Toronto, Ontario and FortEdward, New York. The company manufactures various paper tissue products such as bathroomtissue, facial tissue, paper towels and paper napkins, with all products made from 100% virginwood fibre. In addition, the company manufactures products under the Majesta and Royale brandin Canada. Irving Tissue has ample export experience, through various geographic markets,including Japan, Norway, Sweden, and Hong Kong.

The companys vision is To be the leading premium private label tissue supplier in NorthAmerica, the #1 consumer tissue company in Canada and have the #2 facial tissue brand in the

U.S. (Irving Tissue) Irving Tissue is extremely proud of their safety record and has beenrecognized both nationally and internationally for their practices and performance. As well, thecompany is committed to producing quality tissue products through responsible forestmanagement.

Industry Analysis

In general, toilet paper manufacturing is categorized under the Sanitary Paper ProductManufacturing national industry. The three most important categories for manufacturing costsinclude the cost of materials and supplies, the cost of energy, water and vehicle fuel, and

production worker wages. Manufacturing costs in the Sanitary Paper Product Manufacturingnational industry were dominated in 2007 by the costs of materials and supplies. When takinginto account that these costs are the major factor in its manufacturing activities, this industry isvulnerable to any fluctuation in the prices of materials and supplies. (Industry Canada) To thisend, Irving Tissue has achieved extensive vertical integration, from owning timberland toproducing pulp to synergizing with sister company Midland for logistics, which shields themfrom fluctuations in sourcing costs.


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Flexibility: As a result of the standardization of products, they have less variety andcustomization of products. Instead, they have the organizational capability of adjusting tounexpected changes in the demand of the product because they have control over the processfrom beginning to end.

Competitors in Canada

Cascades Inc(Cascades - Premium and Enviro Brands)(Horizon and North River)

http://www.cascades.com/_home

- Located in Montreal, Quebec- Known for its environmental practices, providing a greenfriendly manufacturing process using recycled materials- Serve the industrial market with jumbo and standardbathroom tissue as well as the consumer market

Kimberly Clark/Scott Tissue(Cottonelle and Scott Household Tissue)

http://www.kimberly-clark.com/

- Located in Dallas Texas- No Canadian Operations- The world's largest paper tissue producer- Purchased Scott Tissue Brands- Offer both a beauty sensitivity brand as well as ahousehold brand- Largely virgin producer, using the highest concentrationof non-recycled products

Kruger Products(Cashmere, Purex, Swan, Soft & Pure Premium)

http://www.kruger.com/index_en.html

- Located in Montreal, Quebec- Manufacturing capability of 250 000 Metric Tons ofBathroom Tissue- The Tissue Products business unit includes the fourKruger Products mills in Canada and one mill and threeconverting plants in the United Kingdom. The KrugerFamily interests include tissue mills in Venezuela andColombia.- Purchased Scott Paper Limited in 1997 through

acquisition

Proctor and Gamble(Charmin)

http://www.pgpro.com/Default.aspx?tabid=139

- Head office located in Toronto, Ontario- Produce one brand of toilet paper at different quality andselling points eg. ultra and basic- Manufacturing mainly in United States but havedistributing operations in Canada

Grocery Stores - Own proprietary brands- Usually value product offering

Manufacturing Process

Raw MaterialsToilet paper is generally made from new or "virgin" paper, using a combination of softwood andhardwood trees. Softwood trees such as Southern pines and Douglas firs have long fibers thatwrap around each other; this gives paper strength. Hardwood trees like gum, maple and oak haveshorter fibers that make a softer paper. Toilet paper is generally a combination of approximately70% hardwood and 30% softwood.
http://www.cascades.com/_homehttp://www.cascades.com/_homehttp://www.kimberly-clark.com/http://www.kimberly-clark.com/http://www.kruger.com/index_en.htmlhttp://www.kruger.com/index_en.htmlhttp://www.pgpro.com/Default.aspx?tabid=139http://www.pgpro.com/Default.aspx?tabid=139http://www.pgpro.com/Default.aspx?tabid=139http://www.kruger.com/index_en.htmlhttp://www.kimberly-clark.com/http://www.cascades.com/_home


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Process-Product Type

While Irving Tissue converts the paper into various tissue products, they use assembly line orflow shop to meet high volume demands of few major products, such as paper towels andbathroom tissue. Therefore, Irving Tissue is currently operating in the appropriate portion of thematrix.


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Production Workflow Analysis

See Appendix A for the workflow diagram.

Inputs needed for the production of bathroom tissue includethe tissue itself, as well as cardboard tubes for the inner coreof each roll, and glue in the production of said cardboardtubes, attaching the tissue to the tubes as well as sealing theend to prevent unraveling.

The process begins when parent rolls (see right) arrive fromthe lumber and pulp processing mill in Dieppe, NB, and arestored on-site to be used in the production of bathroomtissue.

Being large single ply rolls of tissue at this stage, it is often the case that some of these delicaterolls become damaged during transit. Prior to production, a visual inspection is made, and wherethere are tearing or otherwise damage to parent rolls, the superficial layers are peeled off untilonly undamaged sheets remain, and the damaged sheets are reprocessed onsite into pulp.

The pulp is then used to create cardboard strips, which are fed into a machine that glues andwinds them into continuous tubes later to be cut into 65 logs. These logs in turn will be used asthe core of the toilet paper rolls.

When the time comes for production, parent rolls are loaded in preparation for the beginning ofthe automated process via forklifts and special machinery. The machines are set up to handle the

production of at least 2 ply (2 parent rolls loaded concurrently) bathroom tissue, with some linescapable of producing 3 ply SKUs.

Once properly set up, the winder feeds both parent rolls into an embossing machine whichpresses the 2 layers together while setting the Royale imprint pattern onto the paper. This is thendirectly fed to a winding machine which glues the paper onto the cardboard tubes and thenrapidly spins these logs until the specified size of the roll has been reached. A cutter then cutsthe roll off from the winder, and moves the log through a conveyer system which also seals theend of the roll with glue before delivering it to the log saw.

There, a circular saw cuts up the logs of toilet paper into the standard 4 rolls, and then passes

them through a filter where the ends of the logs, or any rolls thinner than 4 are rejected andcollected to be turned back into pulp, which would then be used to create additional cardboardtubes. It is estimated that around 5% of each log would be rejected at this stage.

Now in the standard sizes, the rolls are collected into predetermined sizes depending on theSKU, and are automatically packaged with the corresponding transparent plastic packaging. Ifapplicable, they are then further packaged into cases before exiting this stage.


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Finally, the conveyer system drops them off to be manually loaded onto shipping pallets, and theproducts are then ready for delivery. The only manual steps in the process are the loading of theparent rolls and the final pallet packing.

Bottleneck Analysis

Analysis MethodologyUnfortunately, we are unable to obtain the capacity of each stage of production, which makesaccurate analysis of bottleneck capacity difficult, but we were able to obtain the throughputspeed of each SKU on each production line. Thanks to the homogenous nature of bathroomtissue as well as the highly mechanized process, we can analyze the difference in throughputspeeds between producing different SKUs on the same machine line. The 2 variables that we cananalyze are sheet count (how thick the rolls are) and roll count (how many rolls are in a SKUpackage). For the sake of comparability and simplicity, only the throughput rates of line 156 will

be examined here.

Roll CountSince the roll count per SKU package only becomes a factor at the packaging stage, its a goodidentifier for whether the packaging stage is a potential bottleneck. Our analysis assumes thatpackaging throughput speed is directly related to the number of rolls per SKU, and the smallerthe number of rolls, the more SKUs that have to be packed for one case of the same size to befilled. To clarify: the number of rolls inside a case does not actually change, but the number ofpackaging per case increases with smaller package SKU size (more small packages versus fewerlarge packages), thus theoretically leading to slower throughput speeds per case.

Data Analysis

Table 1

As shown in the relevant data in Table 1, a change in SKU roll count (from 12 to 24 in the firstpair, from 24 to 32 in the second) does not actually have any impact upon the throughput speed(180 cases/hr). We can therefore conclude that for line 156, the packaging stage is not abottleneck in the selected range of 12-32 rolls, or put in another way, requiring 3-8 separatepackagings per case.

Sheet CountAll other variables kept equal, a change in sheet count (resulting in a thicker roll) would indicatethat it takes longer on the winding stage. If the winding stage is a bottleneck, throughput rates

Product # Description 155 Line 156 line 143 Line

60383-81727 PRE BRT 140 12R 2PLY 8/CS 179 180

60383-81728 PRE BRT 140 24R 2PLY 4/CS 179 180

55742-33957 COM BRT 198 24R 2PLY 4/CS 180

55742-33958 COM BRT 198 32R 2PLY 3/CS 180

LINE SPEEDS (CPHr)


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should drop as sheet count doubled, possibly by as much as 2 times if the lower sheet count wasalready presenting a bottleneck.

Data AnalysisUnfortunately, there are not many SKUs which keep the roll count the same while changingsheet count (same number of thicker rolls would increase case size), but an example can befound in Table 2 below:

Table 2

Here it seems that from a 140 sheet count SKU to a 280 sheet count SKU, there appears to be adrop in throughput rate consistent with our hypothesis of slower speed for higher sheet count.

Note that in Table 1, throughput rate remained at 180 even for SKUs having 198 sheets. Thiswould lead us to conclude that given our data, somewhere between 198 sheets and 280 sheets,the winding stage has become the bottleneck of the process for line 156, with a capacity rate of108 cases per hour at 280 sheets per roll.

Higher Sheet Count Offset by Smaller CaseThrough the course of our data analyses, we were able to find a circumstance where from onerelated SKU to another, a doubling of sheet count was combined with only half of the roll countper SKU. This would lead to a case where, assuming the winder operated at the same efficiency,the twice as long time to wind a log would be offset by the case only containing half as manyrolls, therefore theoretically taking the same time for both SKU cases. Since weve establishedthat the packaging stage is not a bottleneck between the ranges of 3-8 packages per case, the onlystage affected is the sawing stage, which only has to saw half as many logs with the highersheet count SKU.

Data Analysis

Table 3

As the pair of SKU data point out in Table 3, what was previously perceived to be the fastestthroughput rate of 180 increased to 225 cases per hour when the number of saw cuts werereduced. As shown in Product # 60383-81727 in Table 1, there are other SKUs with 12 rolls perSKU, but for that SKU there was 8 SKUs per case, a case required a cutting up of a total of 96rolls (12*8). For Product # 57315-13532, a case requires only a cutting up of 48 rolls. From this,we can conclude that at 96 rolls per case the sawing stage becomes a bottleneck with a maximumcapacity of 180 cases per hour.


63435-64048 GRE BRT 140 24R DRP 2PLY 179 180

63435-64052 GRE BRT 280 24R DRP 2PLY 108

LINE SPEEDS (CPHr)


57316-13531 COO BRT 176 24R 2PLY 4/CS 179 180

57316-13532 COO BRT 352 12R 2PLY 4/CS 208 225

LINE SPEEDS (CPHr)


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Implications of our FindingsWith our limited data, we can ascertain that of the four key stages of embossing, winding,sawing, and packaging, depending on the SKU being produced, different stages can becomebottlenecks. The embossing and winding stages have to go at the same speed due to one feedingdirectly into the other, so it is hard to determine if the maximum capacity of SKUs coming out ofthese two stages is due to the maximum capacity of the embossing section or the windingsection.

Nevertheless, it appears that due to constraints either in the embossing or winding machine, thewinding stage for line 156 has a bottleneck capacity of 108 cases per hour when producing SKUswith sheet counts of 280. If this bottleneck is increased by producing SKUs with lower sheetcounts, the throughput speed increases to 180 cases per hour, which appears to be the bottleneckspeed of the sawing section. If this is further lifted to reduce the number of sawing needed, itcould go up to 234 cases per hour. Due to limitations in data, we cannot ascertain whethersawing is still a bottleneck at 96 rolls per case and 234 cases per hour, or another stage has

become the new bottleneck.

In terms of recommendations, if there are considerable demand for SKUs with high sheet counts(so called double roll SKUs), upgrade a line with superior embossing and winding machinery,and produce all high sheet count SKUs on that line to best free up time elsewhere. Similarly,more advanced sawing equipment could be procured if the cost-benefit analysis is favorable toraise capacity at that stage.

Demand Forecasting

Current Forecasting MethodIn talking with managers at Irving Tissue, they indicated that the primarily means of productionforecasting was done by their sales department. This forecasting method primarily relies ofqualitative sales information and experience, or more specifically a sales force composite methodof forecasting. This method did an adequate job tracking the trend, but had a tendency tomisestimate some months drastically. The month of May 2009 demonstrates this effect.

Purpose of Improving MethodThe purpose of forecasting is to minimize both holding and setup costs. When demand is

carefully matched to production the greatest level of profitability can occur as the optimumordering size is realized. In our proposal, we indicated that we would attempt more objectiveforecast methods to determine if we could improve on their subjective methods. Management hasindicated in the past that they are now looking into alternative ways to forecast but are wary ofthe actual results when compared to the costs that are involved.


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Data AvailableData that is available for this project includes actual monthly production quantities between theperiods of January 2007 and June 2009. We have also obtained sales forecasts for the year 2009up to July. Going forward, we will calculate how accurate their forecasts have been in the year2009, and compare them against a number of measures to see if any seasonal or objectivemethods perform better. We will also test these forecasting methods against 2008 data (wherepossible) to further assess the viability of these methods. Furthermore, we will be testing both ahigh demand product (24 rolls) and a low demand product (16 rolls), as well as performingaggregate forecasting on the entire Royale line to get a truer sense of the viability of ourmethods.

The following forecasting methods will be employed:1. Moving Average (3 month, 6 month, 12 month)2. Exponential smoother (Moving Average (3 month, 6 month, 12 month)3. Exponential smoother ( adjusted)4. Holts Method (initialized through linear aggression)5. Winters Method (using seasonal data from 2007 and 2008 to predict 2009)

Measuring ErrorSince our goal is to minimize the amount of additional units produced, we will use MeanAbsolute Difference (MAD) as our primary basis of measuring error. We will also considerMean Absolute Percentage Error (MAPE) to get a sense if some methods lend themselves betterto low and high demand products. The results of our methods can be found below:


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Low Demand (Royale 16 Rolls):

*For a detailed explanation of method calculation, please see Appendix B

Jun-09 May-09 Apr-09 Mar-09 Feb-09 Jan-09

Actual 17,790 17,318 18,135 26,876 16,276 13,315

Sales Force Composites

Forecast 28,000 40,000 18,000 31,000 22,000 25,000

Error 10,210 22,682 135 4,124 5,724 11,685

MAD 9,093

MAPE 54.6%

MA MethodMA (6 month) 17,442 18,252 17,045 20,532 19,069 20,113

Error 349 934 1,090 6,344 2,793 6,798

MAD 3,051

MAPE 17.5%

ES Method

ES (0.4) 18,686 19,597 20,572 16,370 16,433 18,511

Error 896 2,279 2,437 10,506 157 5,196

MAD 3,578

MAPE 18.5%

Holts Method

(=0.2, =0.2) 17,136 17,496 15,794 16,870 18,925 21,360

Error 654 178 2,341 10,006 2,649 8,045

MAD 3,979

MAPE 21.9%

Winters Method

(=0.2, =0.2, =0.1) 22,255 25,331 28,862 23,838 32,174 21,410

Error 4,465 8,013 10,727 3,038 15,898 8,095

MAD 8,372

MAPE 50.0%

Royale (16 Rolls)


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High Demand (Royale 24 Rolls):



Actual 132,774 120,313 63,379 70,071 46,502 37,040

Sales Force Composites

Forecast 75,000 95,000 80,000 85,000 82,000 83,000

Error 57,774 25,313 16,621 14,929 35,498 45,960

MAD 32,683

MAPE 52.1%

MA Method

MA (12 month) 74,407 69,742 70,824 69,937 73,255 77,135

Error 58,367 50,571 7,445 134 26,753 40,095

MAD 30,561

MAPE 44.0%

ES Method

ES (0.8) 109,005 63,775 65,360 46,517 46,576 84,718

Error 23,769 56,538 1,981 23,554 74 47,678

MAD 25,599

MAPE 38.4%

Holts Method

(=0.1, =0.1) 86,256 87,368 87,595 90,271 93,809 91,269

Error 46,518 32,945 24,216 20,200 47,307 54,229

MAD 37,569

MAPE 62.9%

Winters Method

(=0.2, =0.2, =0.1) 100,695 66,005 65,449 62,397 77,534 67,869

Error 32,079 54,308 2,070 7,674 31,032 30,829

MAD 26,332

MAPE 38.9%

Royale (24 Rolls)


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Evaluation of Sales Force MethodLow Demand- As mentioned above, this method does an adequate job tracing the trend of lowdemand tissue paper. Results in May, however have severely skewed the measure of error. Intalking with managers, what happened in the month of May was a promotional price offeringestimated to drive sales. However, actual results did not perform as well as expected. We believethat this may be due to lurking variables involved in the tissue industry. In particular, we havedetermined that this underperformance was due to a similar lower price offering which causedthe sales to fall below expectation.

High Demand - In the month of May, it appears the opposite has happened in the high demandmarket compared to the low demand. During May, the sales department has underestimated theeffect of the promotional price. This also suggests that other lurking variables are influencingtheir accuracy. In addition, salespeople may be unable to accurately quantify the relationshipbetween sales and promotions. Finally, they give a static forecast indicated potential laziness.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09

Lower Demand: 16 Roll Royale

Actual

MA (6 month)

Sales Force

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09

Higher Demand: 24 Roll Royale

Actual

ES (0.8)

Sales Force


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Results of Forecast Methods for Low DemandAs you can see from above, the 6-month moving average method gives the lowest error for thelow demand product. This suggests that there is no prevalent trend in this product. This may bebecause the product is usually bought by a niche market who does not buy in bulk for costsavings or in low amounts for storage convenience or to control for perceived ware.Interestingly, as we expanded the measure to include forecasting for 2008, we found expandingthe forecast to the 12-month moving average generated more accurate results. In addition to ourfindings, we found that this product is less subject to seasonal forecasts methods like winters.This further suggests that there is no seasonal trend and demand is relatively static, with theexception of shock promotional pricing. Holts method performed particularly bad, indicating nogeneral linear trend.

The end result was a reduction in MAD of 6,042 units simply by applying this low cost method.

Results of Forecast Methods for High Demand

In forecasting for high demand products like the 24-roll, we found that the ES method with asmoother of 0.8 performed the best. In discussion with management and through personalobservations, we believe that this may be due to the high demand for the 24-roll products. Thepromotional effects of the sales are likely to last longer as consumers demand greater amountsand stock up over the promotional time period. This means that the ES method is able to catch upto the high demand for the product as it is one-step behind and captures the increase in demand.Interestingly, seasonal forecasting did perform better than in the low demand market. This islikely because of seasonal bulk buyers who are focused on cost savings. Consumers who oftenswitch based on price are more likely to buy this product during promotional spikes creatingsome seasonal trend if promotional periods are similar during the year.

The end result was a reduction in MAD of 7,083 units simply by applying this low cost method.

Solving Lurking Variables and Offering an AlternativeThe main problem with our results is that our selected methods are one-step behind. The salesforce method has some benefits in tracking the trend and spikes. However, because it is based onsubjective judgment, we believe that it can be refined by quantifying relationships betweendemand and lurking variables. One such lurking variable above is the price of competing brands.Advanced statistical software is one option, but is more expensive than our low cost alternatives.Our low cost alternatives do however reduce the amount of error in estimating demand thereforeimproving plant efficiencies. A true forecast statistical software could be employed, but at ahigher cost. Other variables that could be measured could include unemployment, inflation,

overall supply and marketing.


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Aggregate FindingsOne of the main difficulties in forecasting for the aggregate quantity is that SKUs changeannually and are not measured in some years. As such, we have decided to isolate a number ofSKUs from the Royale line that are comparable across years. Our result demonstrated that thesales force method is currently the best method of estimating demand. In talking withmanagement, the difference between the results of the aggregate and individual forecasting islikely because they first estimate the aggregate then make random adjustments to make it fitamong the individual SKUs. This suggests that the sale department does have relevantknowledge of the aggregate demand. The results of our findings are posted below:



Actual 430,386 354,206 222,801 458,393 237,005 160,671

Sales Force CompositesForecast 407,500 305,200 275,400 348,300 320,300 277,000

Error 22,886 49,006 52,599 110,093 83,295 116,329

MAD 72,368

MAPE 29.1%

MA Method

MA (12 month) 244,129 237,663 244,496 224,607 226,474 229,532

Error 186,257 116,543 21,695 233,786 10,531 68,861

MAD 106,279

MAPE 30.7%

ES MethodES (0.8) 335,452 260,434 410,964 221,249 158,227 148,450

Error 94,934 93,772 188,163 237,144 78,778 12,221

MAD 117,502

MAPE 37.6%

Holts Method

(=0.1, =0.1) 204,581 204,142 177,684 176,004 183,297 192,204

Error 225,805 150,064 45,117 282,389 53,708 31,533

MAD 131,436

MAPE 36.5%

Winters Method(=0.2, =0.2, =0.1) 295,851 240,877 294,631 235,656 273,139 215,568

Error 134,535 113,329 71,830 222,737 36,134 54,897

MAD 105,577

MAPE 32.2%

Royale (Aggregate)


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Implications of our FindingsAs you can see from our results, the sales department is successful in estimating the aggregatelevel of production. Since the production process requires similar materials across SKU's, thisindicates that this is a sufficient measure. However, there is some difference in materials withlevels of premium materials added to premium brands. In this case, at a minimum, it would bebetter to utilize the methods proposed above. The MAD through their method is still significantat 72,368 units of a total of 430,386 units sold. This indicates that the process has room to beimproved. The suggestion above employing a combination of sales knowledge of variables andstatistical analysis, can improve the demand numbers. This would mostly affect holding andordering costs as with a fill rate of 99%, it is only the additional holding and order costs thatwould be saved, but shortages can still occur.

Therefore, we recommend a statistical software method be used in collaboration with the salesforce. For product level forecasting, we recommend switching to the ES and MA methods if thesoftware is incapable of scaling down as it is a more affordable option and is more accurate inestimating individual demand.

Limitations with Forecasting- Small amount of data from 2007 on increases error- Only sales force forecasts for 2009 (JanJune) available, could have been a biased year- Aggregate limited as difficult to isolate every SKU as they change over time- Only Royale line measured

Optimal Inventory

Each company has an optimal level of inventory they wish to keep on hand to face uncertainsituations. Having an inventory level below the optimum is dangerous because the company willresult in lost revenues and damaged reputation. However, having an inventory that is too high isa waste, and it incurs large storage costs. In this project, weve estimated the optimal inventorylevel using the Economic Order Quantity (EOQ) method. Economic order quantity is the level ofinventory that minimizes the total cost associated with the purchase, delivery and storage of theproduct.

Basic assumptions to compute EOQ for 24-Rolls product:Arrivals per year = Cases per month x number of months in a year= 78,347 x 12

= 940,164 cases per yearInterest Rate: Assuming 10% annualUnit Cost: $10/caseSetup Cost: $200

Q* (2CD/H)


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= [(78,347 *2*2000)/(.10*10)]Q* = 19,392

Please see Appendix C for complete optimal inventory calculations for the 24-Rolls product and16-Rolls product.

Optimal Cycle Time for 24-Rolls product:Cycle time=Q*/D

Optimal Cycle Time Calculation

In Years 0.02

In Months 0.25

In Days 7.53

Comparison of Data

Product DescriptionOptimal Quantity

(Calculated)

Irving Tissues Actual

Quantity

16-Rolls (Royale) 9,368 10,721

24-Rolls (Royale) 19,392 15,268

Implications of our FindingsThus, the results of the optimal quantity (calculated) and the Irving Tissues actual quantity forthe 16-Rolls product is fairly close. However, for the 24-Rolls product, there is a bit of adiscrepancy, as the optimal quantity is implied to be 19,392 cases and the company is onlycurrently producing 15,268 cases. However, there are a few drawbacks when using thissimplified EOQ model. It is important to note that although this value of Q minimizes the yearlyholding and setup costs, it could be infeasible for the company, as they may not have sufficientspace to store these cases and hence could result in additional storage costs.

Therefore, management should be aware that modifying inventory levels could incur extra costs.

Safety Stock

Irving Tissue is highly adamant on maintaining a service level of 99%, which would lead tocarrying safety stock equivalent to 2.33 standard deviations of historical demand for the product.As part of our analysis regarding inventory, weve selected 2 SKUs to check their current targetsafety stock levels against our calculated levels using the method learned in class.


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Key Information Needed Data of demand Mean yearly demand Standard deviation of the yearly demand Lead time Service Level

Data of demand: We gathered the following 3-year data about demands for 16 and 24-Rollproducts:

Mean yearly demand: It is computed based on the demands for recent 3 years and the averageis taken.

Mean Demand = Sum of Demands / Number of data of demand

Standard deviation: It is computed based on formula using the computed mean yearly demand

and the actual data of demands.

Lead time: In the multi-echelon structure of Irving Tissue, they view the lead time as the time ittakes for the plant in Toronto to supply the regional warehouses, from which lead time tocustomers would not exceed 2-3 days depending on location. We were told that because of theway production is sequenced, it would be potentially costly to produce a SKU on a short noticedue to the excessive setup costs and time involved, so that we should assume a worst-case

scenario of only producing one run of a SKU every two 14-day production cycles. Therefore, ifdemand was greater than expected, it could be up 28 before another run is scheduled, and thus 28days would be the lead time for this calculation. Due to the excessively conservative parametershere, the loading & delivery time of 1-2 days from plant to warehouses are left out.

Service Level: It is set as 99% service level by Irving Tissue. Only 1% of failure of meeting thecustomers demand is accepTable From Table A-1 in the textbook, we know that z value for99% service level is 2.33


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Safety Stock: It is computed by using the following formula:

Safety Stock = SQRT (28/365) * Standard Deviation * 2.33

ProductDescription

Mean

Yearly

Demand Std. Dev. Safety StockCurrent

Irving Tissues

Safety Stock

16 Rolls (Royale) 268,374 7889 5,901 11,02924 Rolls (Royale) 905,526 60,896 39,299 39,694Based on the gathered information and calculations, we found that the required safety stock for

99% service level is 5,901 for 16-Roll product and 39,299 for 24-Roll product.

Implications of our FindingsIn analyzing our calculated values compared to current data, we conclude that Irving Tissue isunnecessarily holding too much safety stock for the 16-Roll product. Irving Tissue couldpotentially be able to reduce approximately half of current safety stock holding costs bydecreasing the safety stock from 11,029 to 5,901.It is important to keep in mind here that the safety stock calculation method which was learned inclass may not be the most applicable in this case, as it does not take into account the forecastaccuracy. The wide variation between our calculated value for the 16-Roll product and thecurrent target could very likely be represented by Irving Tissues use of a different calculationmethod which accounted for forecast accuracy. For this reason, we would simply recommendthat they check their current safety stock target levels to ensure that theyre intentionally carryinghigher levels than what we expect, due to factors such as inaccurate forecasting.

Overall Conclusions & Recommendations

Production ProcessesAs a result of our analysis, a possibility exists of having a dedicated line for the production ofhigh sheet count SKUs. If double roll bathroom tissue products become highly demanded in

the future, this option should be more closely examined at that time.

Demand ForecastingAs a result of our analyses, we believe that current forecasting methods could tangibly benefitfrom the implementation of statistical forecasting methods for production planning purposesgoing forward. While sales force composites outperformed current statistical methods inaggregate forecasting, because the aggregate is across SKUs it would not be beneficial from aproductions perspective to overproduce one SKU while another is sold out. Therefore, while we


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believe that sales force composites will retain its uses in longer term aggregate planning, thenext-period production planning process would benefit from statistical forecasting.

Inventory ManagementIt is our understanding that upper management of Irving Tissue is keen to reduce inventoryholdings in order to increase their inventory turnover metric. This report has shown that givenvery limited data selections, the current inventory level looks to be near the optimal point interms of EOQ, and the safety stock levels appear to be appropriate for the high volume SKUanalyzed. While the lower volume SKU analyzed showed it is potentially holding too muchsafety stock, inaccurate forecasting, which is not factored into our calculations, would justify thereasoning behind higher safety stock levels for that SKU.In summary, this means that opportunities to easily reduce inventory are currently limited. Uppermanagement needs to be educated on the concept of EOQ in particular, and how deviations from

the ideal production size will result in higher costs, so that they are not hurting the health of thecompany for the sake of being able to say they met their target metric.


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Appendix A - Workflow Diagram


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Appendix BForecasting Calculations

Moving Average

= =

3-month, 6-month, 12 month for N used in calculation.

ES Method = + Smoothers used include 0.8, 0.6 and 0.4.

Holts MethodHolts method is designed to track time series with a linear trend. It requires the following twosmoothing equations:

= + 1 1 + 1 = 1 + (1 )1These are then substituted into the forecast to find the one step ahead:

,+ = + To initiate the formula, we require both 00using linear regression. We used excel togenerate the statistics. Below is the output for the low demand.

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.398735

R Square 0.15899

Adjusted R Sq -1.2

Standard Erro 23807.1

Observations 1

ANOVA

df SS MS F ignificance F

Regression 12 1.07E+09 89289380 1.890462734 #NUM!

Residual 10 5.67E+09 5.67E+08

Total 22 6.74E+09

Coefficientsandard Err t Stat P-value Lower 95% Upper 95% ower 95.0 pper 95.0

Intercept -5E-299 4.9E-299X Variable 1 0 0

X Variable 2 -5E-299 4.9E-299

X Variable 3 0 0

X Variable 4 2.2E-282 2.2E-282

X Variable 5 7923.755 7923.755

X Variable 6 -2E-302 2.1E-302

X Variable 7 -5E-299 4.9E-299

X Variable 8 -5E-299 4.9E-299

X Variable 9 0 0

X Variable 10 -511429 511429.2

X Variable 11 50787.05 14652.26 3.466159 0.006059943 18139.78 83434.31 18139.78 83434.31

X Variable 12 2737.301 1990.85 1.374941 0.199170606 -1698.59 7173.19 -1698.59 7173.19


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Winters Method

Winters method is a type of triple exponential smoothing employing the following forecastformula:

,+ = ( + )+ Smoothers Include:

= (/) + 1 1 + 1 - Series = 1 + (1 )1 - Trend

Updated Seasonal Factor:

= + 1

Seasonal trends were based off 2 years of data, with each month being its own season.

Appendix C - Optimal Inventory Calculations

SKU# 63435-70085 63435-70086

Description - No. of Rolls 16 Rolls 24 RollsAverage number of cases/month 18,285 78,347

Number of months a year 12 12

Arrivals a year 219,420 940,164

Unit Cost $10 $10

Interest Rate 10% 10%

Ordering Cost $200 $200

Optimal Quantity 9,368 19,392

Optimal Cycle Time (in years) 0.04 0.02

Optimal Cycle Time (in months) 0.51 0.25

Optimal Cycle Time (in days) 15.58 7.53


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Works Cited

Greenpeace. Ancient Forest Friendly Tissue Products. 04 Dec 2009.

Hoover's. Irving Tissue Inc, Company profile from Hoover's. 2009. 03 Dec 2009.

Industry Canada. Canadian Industry Statistics. 2009. 01 November 2009.

Irving Tissue. Vision & Values. 2003. 01 Dec 2009.

Kaufman, Leslie. "Mr. Whipple Left It Out: Soft Is Rough on Forests." New York Times 25 Feb2009.

Documents

Irving Tisuue - Team PK