Eindhoven University of Technology MASTER Managing operational complexities and product portfolio through SKU rationalization and complexity quantification Yu, X.

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Managing operational complexities and product portfolio through SKU rationalization and complexity quantification

Yu, X.

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Eindhoven, August 2016

Managing operational complexities and product portfolio through SKU rationalization and complexity



Xuehan Yu

B.Eng. Traffic & Transportation—2014 Student identity number 0925658

in partial fulfilment of requirements for the degree of Master of Science

In Operations Management and Logistics

University supervisor:

Dr.ir. N.P. (Nico) Dellaert, TU/e, OPAC Dr. B. (Boray) Huang, TU/e, OPAC Company supervisor:

J. (Janko) van Elderen, The Kraft Heinz Company


TUE. School of Industrial Engineering.

Series Master Thesis Operations Management and Logistics

Subject headings: Complexity Management, Product Portfolio Management, SKU Rationalization




In the thesis, a complexity reduction exercise through SKU rationalization is conducted in cooperation with The Kraft Heinz Company. To resolve the problem of product proliferation, the thesis designs a process to select SKU candidates to be delisted and develops a model to quantify the complexity costs.

SKU family segmentation and characterization are applied to pinpoint the complex SKUs. Complexity costs are calculated based on complexity drivers and complexity costs drivers identified, then SKUs candidates can be compared under the same complexity measurement system. The operations perspective is integrated into the SKU rationalization process such that complex SKUs proposed by downstream sites will be taken into account and further evaluated by complexity quantifications. The thesis contributes to SKU rationalization process by revealing and extracting the complexity costs from the supply chain standpoint.




Product proliferation has become a widespread issue for manufacturing companies. In booming economy, companies benefit from consistent profitability and growth by continuously introducing new products to markets and expand the portfolio in order to meet diversified customer requirements.

As customer preferences switch and economy cools down, the problem of bloated portfolio becomes increasingly evident, as well as the amount of complexity costs driven by it. Many companies realized the importance and urgency to take actions and start pruning the product portfolio. For this purpose, Heinz has been initiating SKU rationalization on a yearly basis to clean up the existing portfolio and improve profitability and efficiency.

After the fact is agreed that the product portfolio is oversized, there are still obstacles confronted by the company to proceed. Firstly, the coordination and alignment between functions need to be ensured. Manufacturing managers tend to be supportive to the rationalization since redundant product variances only make their work more difficult, but sales teams are concerned about the revenue loss. Each function could propose well-reasoned SKU candidates to delist but the final decision can only be made when Operations, Finance, and Marketing& Sales are all on board. Secondly, the lack of measurable benefits from optimizing the portfolio. Without quantifying the benefits into clear and sound numerical results, it is hard for functions who are in favour of SKU rationalization to convince and reassure their counterparts. Thirdly, the difficulty to locate the real complex SKUs that mainly drive the complexity costs. To determine the right SKU candidate on the final delisting list, it is also important to evaluate to what extent they contribute to complexities, such as changeover, failures, scrap, unique material and packaging format, etc.

To tackle the problems above from multiple sides and ensure the consensus is reached among functions, the project SKU rationalization at Heinz involves two perspectives: commercial and supply chain (i.e. operations) (see Figure 5). The thesis aims at analysing and quantifying operational complexities from supply chain point of view, and further incorporate the results into considerations of the overall SKU rationalization process.

Foremost, the quantified complexity costs in this research are not extra costs above the total costs, but costs hidden in total costs. Therefore, if the net profit has already taken the total costs into account, the profitability of the SKU is determinate and independent of the amount of complexity costs estimated. Instead, the numerical results will be used for the comparison and prioritization among SKUs under the same complexity measurement system, in which complexities are disclosed in a qualitative way. Ultimately, the lost revenue should not be recovered by cost savings of the delisted SKU itself, it is compensated by increased sales of substituting SKUs (or branded SKUs to which the spare resources after rationalization could be allocated more), streamlined manufacturing footprint, higher efficiency and utilization rate, simplified and consolidated unique features and supply bases, better customer service and higher fill rate, etc.

The thesis project was conducted in two stages. The first stage consists of the qualitative complexity analysis and the process design, where existing operational complexities are identified and SKU candidate are located. The second stage focuses on quantifying the complexities and integrating the results as inputs into SKU rationalization.



The complexity evaluation system contains complexity drivers and complexity costs drivers. The distinction is that complexities are driven by a variety of complexity drivers, which do not necessarily drive complexity costs. For instance, symptoms such as “low-volume”, “low-rotation”, and “unique materials” do drive complexities but do not directly trigger complexity costs. In practice, low rotation rate items results in high inventory level and high write off risks, which are ultimately subject to complexity costs drivers. To connect with the goal of complexity costs quantification, all of the complexity drivers are categorized and elaborated into 4 complexity costs drivers: changeover, yield loss, write off, and inventory level.

Next to it, a process is designed to characterize SKUs and pinpoint ones which are potentially accountable for the most of the complexities. The process is composed of three steps: SKU family segmentation, SKU/SKU family characterization, and candidates selection. In step one, since handling thousands of individual SKUs can be unacceptably time-consuming, SKUs who share similar attributes are grouped into families. Step two is the core of the process, where SKU or SKU families are mapped in graphs and matrices based on chosen characteristics. In this way, the performances of all SKU families are displayed under the same characteristics set and it is straightforward to visualize and identify the underperforming SKU families. As the prime complexity driver, volume is set as one of the key characteristics for analysis. Besides, to involve operational considerations, demand variation, forecast error, annual write off costs, SKU family size are also included. While executing SKU characterization, Pareto Principal (single dimension), Matrix (two dimension) and Bubble Diagram (three dimension) are used to plot SKUs. In the final step candidates are able to be positioned after the “Remove” quadrant is partitioned on the matrix.

In addition to the process design, the mechanism of the identified complexity costs drivers are elaborated respectively. Excessive changeovers times is one of the main reasons of low production efficiency and R&P spoilage. While there are numbers of variations of SKUs in terms of recipes, formats, sizes, packaging and labels, the full speed long run of big batches will be interrupted by the possibility of stopping every period of time to accommodate changes. In the meantime, recipe changeovers generate yield loss due to the wasted materials remaining in the pipeline.

As for write off process, the direct reason is the expiration of trade BBE: all expired R&P and finish goods have to be disposed or destroyed. The indirect causes include demand variation, production planning, inventory policy and minimum order quantity (MOQ), which are related to operational complexities and SKU characterization. What is particular about write off process is its mutual relationship with SKU rationalization (Figure 21). In short term, SKU rationalization is a cost driver of write off: if the SKU is decided to be discontinued, all of the corresponding raw and packaging materials, finished goods stock after the exit date have to be disposed. However, in long term, if the write off is caused by the intrinsic complexities of the SKU itself (e.g. demand variation), it is highly possible that the same risks will come over year after year. Given the short-term costs are one-off, the better solution might be to simply eliminate the problem from having to be solved.

In the second stage, the model calculating the complexity costs is developed upon the exploration of the mechanism of complexities costs drivers above. The changeover model calculates the total annual changeover time and cost of the SKU on a specific line. The input parameters are the changeover cost per time and total changeover annual times of SKU under each type (Table 13). Write off risk forecast is mainly based on parameters of sellable period, MOQ and demand forecast (Figure 25). The



production volume is constraint by MOQ, implying that under the condition of forecast demand lower than MOQ, every time the SKU is produced, the difference has to be written off. The output is the total predicted write off value of the SKU if no actions are taken to prevent the risk, thus the actual value will always be smaller.

Inventory costs are partly caused by complexities—SKUs with high demand variation or low rotation rate may hold relatively higher (safety) FG stock level. The inputs include on-hand inventory, demand rate, lead time and order quantity (in dataset PSI), the overall inventory level of an example SKU is shown in Figure 34 and Table 25. Then the inventory costs are measured in two aspects: holding cost and cash frozen in safety stock. The calculation model uncovers and extracts complexity costs from the standard costing system, which enables the comparison among SKU candidates and the measurement of complexity level. In the end of the stage, the model was tested in a case study of Worcester line 1.

Lastly, there are two places to integrate the results into the rationalization process: in the beginning, operations functions still propose complex SKUs to the project team. Since complexity costs do not affect the overall profit of the SKU, all candidates will be filtered by financial thresholds. Later on, supply chain candidates selected will be translated into complexity measurement systems, based on which the SKUs could be prioritized and consolidated.

Companies cannot beat complexity by only cutting low-rotating SKUs in an attempt to accelerate their portfolio, nor eliminate painful-to-produce SKUs in an attempt to reduce costs, basically they cannot beat complexity from only one side of the company (Faelli et al., 2012). The decision making relies on a thorough and holistic research of the context and product portfolio, to which this thesis could contribute from operations standpoint.

Finally, recommendations for SKU rationalization have been made based on limitations of the research.

During the project, there were confusions and even failures led by the wrong data and it took time to make clarifications. The lesson learned is to turn the process disciplined where all parties involved are fully aware of the objective, timeline and details (e.g. the pre-defined complexity filters) so that double work could be avoided and data quality could be improved. Moreover, since SKU rationalization requires a cross-functional structure, not only the project managers should be motivated, the rest of the team members should also be mobilized. As we experienced, this project is not prioritized on team members’ task list and they are reluctant to spend time on it and response promptly. One possible way to encourage concerted effort is to link the project to employees’ KPIs and make the benefits visible.

As for the cross-functional structure, the marketing perspective, which is out of the scope of this research, is also an essential aspect for the entire project. One consideration is the SKU’s potential room of growth concerning whether it fits the future market and company’s strategies. Feedback from downstream (e.g. customers, channel partners, etc.) should be collected. Examples include the substitutes of the to-be-delisted SKUs in the remaining portfolio and their potential increasing sales;

how important to maintain the shelf space and the completeness of the product range; how hard SKU rationalization could possibly harm shopper appeal and customer interest, etc.

Another limitation of the thesis is that it does not cover all complexity areas identified (Table 5) and focuses merely on in-house manufactured SKUs. The future research direction is to extend the



estimation to other complexity areas such as R&D, Procurement, Marketing and Sales. Besides, measuring systems could be developed for SKUs produced by copackers or affiliate factories. The expected results are obtained in Worcester’s case study and the datasets used are available for all factories. Theoretically, the process designed in Chapter 4 is replicable to other factories. The problem is that since the data are not collected for the convenience of SKU rationalization, a lot of work needs to be accomplished manually. A possible solution is to initiate a new project developing a complete complexity costing system, looking to quantify the real complexity costs in all areas. Toth et al. (2015) have created the Square Rooting Costing method to quantify all complexity costs on top of the conventional costing construct, which is dedicated for complexity management.

Another recommendation is to keep monitoring the results of complexity reduction, comparing the difference between the realized benefits and the expected benefits. The causal relation between complexity and various KPIs has been empirically tested by Bozart et al. (2009), thus the results of complexity reduction can also be monitored through existing KPIs. Since benefits from SKU rationalization are difficult to estimate, reviewing sessions should be hold to incorporate lessons learned in future plans. Furthermore, to prevent the portfolio size from getting inflated, net “in-and- out” process is required as well: hurdle rates are in place to control the NPD program and SKU rationalization is responsible for the optimization effort to pare back the portfolio.




The last two years as a student of Operations Management & Logistics at TU/e is like a wonderful adventure for me. The moment I stepped on this land, everything was new and fresh. It is always amazing to experience so many differences from the education, the culture, and more importantly, the people. There have been countless times was I impressed by the passion and warmth of the Netherlands. It is hard to describe how grateful I am when everyone around is supporting me and trying to help me out in moments of frustration. I also appreciate the free and equal environment of TU/e, where all students are encouraged to express their opinions and share their ideas without being worried about what others think. I am thankful for the decision made two years ago to become a Master student at TU/e and I would like to use the acknowledgements to show my gratitude to my supervisors, colleagues, friends, and family.

First and foremost, I would like to thank my academic supervisor, Nico Dellaert. Nico has provided great support during classes and the final Master thesis project. When my approach was terminated in the middle of the project, his encouragement and recommendations regarding the new directions and the time planning have helped me to accomplish the report in time. His guidance and feedback are always clear and structured. I enjoyed all of our meetings and admire his patience and positive attitude. I would also like to thank my second mentor, Boray Huang. Although I was late searching for the second mentor, I was welcome and accepted by Boray and he also provided important instructions in supply chain finance field.

Secondly, I would like to thank my company supervisors, Janko van Elderen and Nienke van Dijk.

Without Janko’s trust, I would never have this great chance to work at The Kraft Heinz Company. I learned a lot from such an motivated and organized working environment, which makes me realize how important it is to be efficient and proactive in a business context. He is able to discover the root cause of my mistakes and offer constructive feedback which helped me to cultivate good habits. I also owe a thanks to Nienke, I would not have been able to finish this project without her support. Nienke always found time on her tight agenda to answer my questions and encouraged me when I had difficulties. Thanks to all colleagues at Heinz for their care and help.

Finally, I need to thank my family and friends for respecting my decisions and always being there for me. No matter what happens, family and friends never walk away. Their support got me through the toughest time. Now I am looking forward to entering the next stage of life, devoting my time, attention, and love towards them in return.

Xuehan Yu

Eindhoven, August 2016




Abstract ... i

Management Summary ... ii

Acknowledgements ... vi

List of Figures ... ix

List of Tables ... x

List of Abbreviations ... xi

1. Introduction ... 1

1.1 Background ... 1

1.2 Problem description ... 2

1.3 Research scope ... 3

1.4 Research question ... 4

1.5 Thesis outline ... 4

2. Project approach ... 5

2.1 The bottom-up approach ... 5

2.1.1 Concept ... 5

2.1.2 Framework ... 5

2.1.3 Summary ... 6

2.2 Portfolio heterogeneity ... 8

3. Complexity analysis ... 10

3.1 Literature review ... 10

3.2 Complexity management ... 12

3.2.1 Make trade-offs explicit ... 13

3.2.2 Install cross-functional project team ... 14

3.2.3 Link benefits to KPIs ... 14

3.2.4 Institutionalize the process ... 14

3.3 Cost of complexity ... 14

3.3.1 Complexity costs hidden in the standard costing system ... 15

3.3.2 Complexity costs distorted in the standard costing system ... 17

3.3.3 Labor cost in complexity measurement system ... 18

4. Process design ... 19

4.1 SKU family segmentation ... 19

4.2 SKU/SKU family characterization ... 20

4.2.1 Pareto Principal/ ABC analysis ... 20

4.2.2 Two dimension—Matrix ... 21

4.2.3 Three dimension—Bubble diagram ... 21



4.3 Candidates selection ... 22

4.4 Complexity costs drivers ... 22

4.4.1 Changeover process ... 22

4.4.2 Yield loss ... 24

4.4.3 Write off process ... 24

4.5 Key metrics ... 26

4.5.1 Hurdle rates ... 26

4.5.2 Utilization rate ... 27

4.5.3 Service level ... 28

5. Complexity quantification ... 30

5.1 Changeover costs ... 31

5.2 Write off costs forecast ... 31

5.3 Inventory costs ... 32

6. Case study—Worcester... 35

6.1 SKU family segmentation ... 35

6.2 SKU characterization ... 36

6.3 Hurdle rate check ... 36

6.4 Changeover costs ... 40

6.5 Inventory model ... 41

6.6 Write off costs ... 43

6.6.1 Forecast ... 43

6.6.2 Actual w/o ... 44

6.7 Overtime labor ... 44

6.8 Summary ... 44

7. Conclusion and recommendation ... 46

7.1 Answers to research questions ... 46

7.2 Future research and academic contribution ... 48

References ... 50

Appendix ... 52




Figure 1. Heinz's global product portfolio (adapted from Key, 2015) ... 1

Figure 2. Heinz’s business segments worldwide ... 1

Figure 3. European business units ... 2

Figure 4. Focus shift of product portfolio management (adapted from Brown, 2010) ... 2

Figure 5. Process diagram of SKU rationalization ... 5

Figure 6. Project framework (mainline of SKU rat. and branch line of cost of complexity) ... 6

Figure 7. Relations between sales value, margin, variable costs and complexity costs ... 8

Figure 8. Cost estimation by taking the average in the top-down approach ... 8

Figure 9. Typology of product complexity (Jacob, 2008) ... 10

Figure 10. Profit & Loss journey ... 15

Figure 11. Complexity costs hidden in the standard costing system ... 16

Figure 12. Cost classifications on financial statements (adapted from Seal et al., 2012) ... 17

Figure 13. Cost components of Heinz standard cost ... 17

Figure 14. Process designed for SKU rationalization ... 19

Figure 15. Pareto principal ... 21

Figure 16. Bubble diagram (adapted from Cooper (1997)) ... 21

Figure 17. Two-dimension analysis for SKU characterization ... 21

Figure 18. Product flow during the manufacturing process ... 23

Figure 19. Loss of line speed during and efficiency by changeovers ... 23

Figure 20. Write off process of S&OP and NOD ... 24

Figure 21. Mutual relationship of write off and SKU rationalization ... 25

Figure 22. Long term savings of SKU rationalization on write off costs ... 26

Figure 23. Utilization rate waterfall ... 27

Figure 24. Decision tree of SKU rat. benefit quantification ... 30

Figure 25. Variables of write off costs forecast ... 32

Figure 26 Inventory level (with 𝑄𝑖 ≥ 𝑀𝑂𝑄, 𝑖 ∈ {1,2,3, … }) ... 33

Figure 27. inventory impact under different batch sizes ... 34

Figure 28. SKU characterization matrix (volume-demand variation) ... 36

Figure 29. SKU characterization matrix (volume-demand variation zoom in)... 37

Figure 30. SKU characterization matrix (volume-CMA zoom in) ... 38

Figure 31. SKU characterization matrix (volume-standard cost zoom in) ... 38

Figure 32. SKU characterization matrix (volume-write off costs zoom in) ... 39

Figure 33. SKU characterization matrix (volume-average w/o zoom in) ... 39

Figure 34. Inventory level of SKU 71934000 (future) ... 41

Figure 35. Inventory level of SKU 71934000 (actual) ... 42

Figure 36. Inventory level of SKU 71934000 (actual) ... 42

Figure 37. Inventory level of SKU 71934000 (actual) ... 42

Figure 38. Summary of complexity quantification of the case study (Worcester line 1) ... 45




Table 1. Factory and SKU overview Europe (2016) ... 2

Table 2. Research scope ... 4

Table 3. SKU rationalization comparison 2015-2016 ... 5

Table 4. Complexity components under operational functions ... 11

Table 5. Complexity drivers summary under functions ... 11

Table 6. Complexity drivers under the conversion function ... 12

Table 7. Activities (3-ups) involved in changeovers ... 22

Table 8. Yield loss calculation ... 24

Table 9. Utilization related time buckets ... 27

Table 10. Three utilization measurements ... 28

Table 11. Service level measurement ... 29

Table 12. Two dimensions of complexity quantification ... 30

Table 13. Variables of changeover cost and time ... 31

Table 14. Basic information of Worcester Line 1 ... 35

Table 15. Family division of Worcester line 1 ... 36

Table 16. Hurdle rate under three types ... 40

Table 17. Annual volume of the three candidate SKU families ... 40

Table 18. Changeover times overview ... 41

Table 19. Parameters of changeover time ... 41

Table 20. Write off costs forecast (FG) ... 43

Table 21. Write off costs forecast (RM) ... 43

Table 22. Write off historical data ... 44

Table 23. Capacity utilization forecast SF07-12 CY16 Worcester Line 1 ... 44

Table 24. Summary of complexity quantification and P&L domain (Worcester line 1) ... 45




AOP Annual operation plan

BU Business Unit

BOM Bill of material

c/o Changeover

CAPEX Capital expenditures

CIP Clean in place

CMA Contribution margin

COC Cost of complexity (i.e. complexity costs) COGS Cost of goods sold

EHQ European headquarter

EPN European product number

FC Fixed cost

FG Finished goods

HNZ Heinz

M&A Merge & Acquisition

MBO Management by objectives

MOQ Minimum order quantity

MTO Make to stock

MTS Make to order

NOD Notification of discontinuation

NPD New product development

NSV Net sales value

OEE Overall equipment effectiveness P&L Profit & Loss

PSI Production Sales & Inventory PU Practical Utilization

QA Quality assurance

R&P Raw and packaging

RIRO Run in run out

S&OP Sale & Operations plan

SC Supply chain

SS Safety stock

VIC Variable industrial cost

w/o Write off

YTG Year to go




In this chapter, the background of the company and the project will be provided. Secondly, the research scope and research questions will be discussed. In the end, the layout of the thesis is describe in accordance with the sequence of research questions.

1.1 B


This thesis presents the final results of the Master Thesis Project on SKU rationalization, conducted as collaboration between Technology University of Eindhoven and The Kraft Heinz Company (hereafter referred as Heinz). Heinz is the world’s largest supplier of ketchup sauce and one of the leading manufacturers and marketers of healthy foods. There are three core product categories served by Heinz: Ketchup & Sauce, Meats & Snacks and Infant/Nutrition. Figure 1 displays the overview of the global product portfolio under more than 200 powerful and iconic brands and 8 sub-categories. In the meantime, supply chain service covers 5 business regions of North America (US and Canada), Europe, APAC, LATAM, and RIMEA ( Figure 2). The European supply chain hub locates in the Netherlands, with the aim to “consolidate and centrally lead procurement, manufacturing, logistics and inventory control” (Holter, 2011).

Figure 1. Heinz's global product portfolio (adapted from Key, 2015) Figure 2. Heinz’s business segments worldwide

The project is under the scope of the European region, which consists of 5 business units (BUs) and 44 countries (Figure 3). 30 brands (e.g. HP Sauce, Wijko, Orlando, HONIG, Amoy, etc.) are kept under 25 categories, generating billions of dollars annual NSV. In total, over 4,500 SKUs are active in the European market, among which 55% are produced inhouse, 29% are supplied by external copackers, and the rest 16% are produced by affiliate sites outside Europe. Table 1 shows that more than 2,000 SKUs are produced at 9 internal factories in Europe.





20% 19%


4% Infant Nutrition

Snacks & Desserts Beverages Meat Meals

Cheese & Dairy Ketchup & Sauces Other



Table 1. Factory and SKU overview Europe (2016)

Figure 3. European business units

1.2 P


Product proliferation has become a widespread issue among manufacturing companies. Many companies realized the importance and urgency to take actions and start managing the product portfolio. For this purpose, Heinz has been initiating SKU rationalization on a yearly basis to clean up the existing portfolio and improve profitability and efficiency.

The path of the portfolio management development is illustrated in Figure 4, which shows that the focus of product portfolio management has been shifting between Innovation/ New Product Development and Rationalization/Existing Portfolio. In robust economic environment, companies are fighting for rapid growth. Then along with global downturn, focus is shifted back to rationalization since the complexity issues offset by prosperous economy before become evident.

Figure 4. Focus shift of product portfolio management (adapted from Brown, 2010)

Since Heinz is producing thousands of SKUs on a daily basis, it is crucial to periodically monitor the performance of SKUs being active in the market in terms of volume, margin, and operation costs.

Based on performance assessment, the product portfolio will be “pruned” by delisting complexity- driven SKUs. Even though it is agreed that the product portfolio is oversized, there are still obstacles confronted by the company to proceed. Firstly, the coordination and alignment between functions

Factory Country Number of SKUs

Alfaro Spain 185

Elst Netherlands 529

Kitt Green UK 304

Latina Italy 493

Pudliszki Poland 247

Seclin France 115

Telford UK 159

Utrecht Netherlands 132

Worcester UK 103

Total 2267



need to be ensured. The final decision can only be made when relevant functions are all on board.

Secondly, the lack of measurable benefits from optimizing the portfolio. Without quantifying the benefits into clear and sound numerical results, it is hard for functions who are in favour of SKU rationalization to convince and reassure their counterparts. Thirdly, the difficulty to locate SKUs that to a large extent contribute to complexities. The project aims at developing a method to quantify the cost of complexity and further integrate the results into the rationalization process in order to make the decision less subjective and more data driven.

The main challenges for SKU rationalization include:


At the first stage of SKU rationalization, SKUs are proposed as candidates together with rationale and symptoms description. However, most of these comments are either too general (e.g. “difficult to produce”) or confusing (e.g. “two packaging lines are used simultaneously”). While the reasons do not clearly indicate what the exact complexities are, or what the potential saving areas could be, the corresponding savings are difficult to measure. Moreover, the selection criteria of SKU candidates are usually expressed in subjective judgement and managerial experiences. Sometimes different statements refer to the same complexity and sometimes the definition of the same word varies in different contexts. This problem not only leads to some needless confusions or mistakes, but also possibly results in an incomplete estimation.


Several production inefficiencies are for sure caused by intrinsic complexities of SKUs themselves, whereas some are due to random human errors or mistakes. It is challenging to distinguish the SKU- related complexities from totality. Furthermore, translating descriptive complexity rationales into numerical costs is another difficulty to overcome. SKUs are not comparable with only qualitative arguments. Even though quantification enables measurement in terms of savings, it is highly uncertain whether the cost can really be saved or where the costs will land. This would be contingent on situations.

1.3 R


The Netherlands is the supply chain hub of the Europe business region, thus the research only focuses on SKUs sold in European countries. It is more effective to execute SKU rationalization by business segments, since the local center has better knowledge and overview of the market environment and SKU performances. As aforementioned in chapter 1, there are three types of SKUs: a) produced internally by Heinz’s factories b) supplied by external copackers c) produced by affiliate factories. Each group of SKUs correlates to different sets of complexity areas and complexity costs drivers, which end up with distinctive complexity measurement system. Considering the relevancy of the Master program Operations Management & Logistics and data availability, the inhouse manufactured SKUs are chosen to be addressed in this study.

On the other hand, being a cross-functional project, SKU rationalization takes into account both the commercial and operations perspective (Figure 5). The thesis aims to contribute from the operations side, quantifying supply chain complexities as inputs to the overall rationalization process. Fixed assets



modifications (e.g. shut factories, close production lines, etc.) is affected by much more factors other than SKU rationalization, therefore complexity costs consist mostly by variable costs (Figure 10).

Table 2. Research scope

Domain Scope

Business segment Europe

SKU Inhouse manufactured SKUs

Perspective Supply chain/Operations (commercial strategies out of scope) Operational complexity Conversion and Supply chain complexities (see Table 5)

Costs components Focus on variable costs, both direct and indirect costs (Figure 13)

1.4 R


Following the research objective and by exploring the current problems in section 1.2, the research question is formulated as:

How can the supply chain complexity considerations be integrated into the SKU rationalization process such that the decision making is supported by both financial and operational performances in a quantitative way?

Sub-question 1: What are the complexities existing in operations and which complexity cost drivers should be considered for SKU rationalization?

Sub-question 2: How to select SKU candidates to be rationalized?

Sub-question 3: How do complexities drive extra costs?

Sub-question 4: How to measure and quantify the benefits of delisting SKU candidates?

Sub-question 5: How to use the estimated complexities as input supporting decision making of SKU rationalization?

1.5 T


Chapter 1 introduces general information and background of the company, giving insights into the business environment and specifying the scope and research questions of the project. Chapter 2 describes the concept and framework of the methodology applied in the thesis, followed by a brief discussion of the portfolio homogeneity assumption made in the alternative top-down approach of SKU rationalization. Chapter 3 addresses sub-question 1—complexity drivers under supply chain functions are summarized and how complexity costs are hidden in the standard costing system is discussed. Chapter 4 addresses sub-question 2 and 3—complexity costs quantification process is designed and complexity cost drivers are explained in detail, showing how extra costs are driven.

Chapter 5 develops the model to calculate complexity costs based on the drivers identified and Chapter 6 implements the models in a case study. The last chapter provides answers to all research questions, followed by academic contributions and recommendations for future work.




This chapter introduces the bottom-up approach applied in this research. SKU candidates selected for rationalization can only be compared when complexities are quantified on SKU level. Although downsides exist in this method such as data challenges and daunting tasks while debate occurs on a SKU-by-SKU level, the bottom-up approach complies with the research objective. The portfolio heterogeneity issue brought by an alternative approach is also discussed in the end of the chapter.

2.1 T





The method is originated from the learnings and experience of the SKU rationalization 2015. Given the result was not as satisfying as anticipated and the exercise did not consider operational issues and complexities, several adjustments and improvements are to be made in this year’s project (Table 3).

Table 3. SKU rationalization comparison 2015-2016

SKU rat. 2015 SKU rat. 2016

Centrally driven by commercial & finance teams Driven by both commercial team and SC team With a base of 1,200 SKUs With a larger base of 4,900 SKUs

Hard to manage a huge amount of candidates without SC savings in place

SC team commits to SC savings to help manage and review candidates

Use financial performance as the only criterion Multi-criteria for screening SKU candidates Gate-keeping of delisting to secure NSV Gate-keeping of delisting to secure NSV

As can be seen, a substantial improvement is that supply chain team joins as the representative of the operations function to: a) provide and integrate operational inputs into SKU rationalization b) help manage and prioritize the large number of SKU candidates by quantifying complexities c) ensure the to-be-delisted SKUs do contribute to a healthier product portfolio. Relying solely on financial performances does not completely reflect the problems in practice. Therefore, the project will be led by both teams to expand the scope and make the solution more reliable and pragmatic.


Figure 6 describes the framework of the approach:


· EU CMA<0K$

· EU NSV<20K$


· BBE issues

· High R&P write offs

· Inefficient at the line

· Stacked stock




· CMA recovered by SC savings

· NSV recovered in market


DECIDE ON CONFLICTS AND ENTER FINAL DECISION TO AOP CY17 Figure 5. Process diagram of SKU rationalization



Zooming in to the supply chain point of view, the contribution will be realized in the following three steps:

1) Identify SKUs that would qualify for discontinuation from operations perspective 2) Quantify the potential net cost savings

3) Agree on the final list of supply chain candidates for SKU rationalization

Unlike the commercial candidates selection, which is only based on financial criteria, supply chain complexity filters include:

· High finished goods stock write-off costs

· High Raw material & Packaging(R&P) write-off costs

· SKUs that are below supply chain hurdle rates

· Minimum Order Quantity(MOQ) violation

· Infrequent production runs

· High changeover

· High yield loss and high standard cost

· Unique materials/ingredients/recipe

Figure 6 demonstrates the framework of the approach: the main line illustrates the overall process of SKU rationalization from candidates screening to the final consolidation; the branch line estimates complexity costs and feeds the results back the to the main process. Chapter 3, 4 and 5 of the thesis focus on tasks on the branch line.

SKU rationalization


rationalization SKU Candidates

screen SKU Candidates

screen PrioritizationPrioritization Define Filters for

selection Define Filters for

selection ConsolidationConsolidation Steering team

meeting Steering team

meeting Complexity

analysis Complexity


Complexity drivers identification

Complexity drivers

identification DownscopingDownscoping Complexity costs estimation Complexity costs


Variables and parameters

selection Variables and

parameters selection

Preliminary model Preliminary model Model test

Model test Mathematical

model of costs of complexity Mathematical model of costs of

complexity Empirical results

comparison (revalidation) Empirical results

comparison (revalidation)

Supporting tool development Supporting tool


Main line Branch line

Feed back to


1. Cutting 2. Replacing 3. Re-engineering

4. Stage gate process for new SKUs, Phase- out process for existing SKUs


1. write off costs 2. hurdle rates

3. changeover and yield losses 4. unique materials

Figure 6. Project framework (mainline of SKU rat. and branch line of cost of complexity)


With the bottom-up approach, there are some suspending problems holding back the project from moving on:


7 A unified complexity costs system is required

Among 200 inhouse manufactured complex SKU candidates proposed by factories, 57.5% are provided with saving estimations. One possible way to proceed is to replicate the logic of these items to the rest after fully understanding the calculation details. Unfortunately, each site uses different sets of cost drivers and parameters. This kind of subjective evaluation tends to misestimate savings due to missing or overlapping cost drivers. This problem can be solved by developing a standard platform where information is fully shared and complexity filters/divers are clearly specified. Complexity estimators will be aware and act in accordance with the same sets of cost components.

Slow and insufficient complexity information from sites

As stated in section 1.2, many SKU complexity descriptions are general or ambiguous. Building a calculation models requires good understandings of every detail of the operation mechanism and possible causal consequences of discontinuation of SKUs. It is necessary to communicate with site managers to make sure what the exact complexity is and how it contributes to savings. However, responses usually take days, which slows down the project progress. The reason is that the personal KPIs are not linked to the project benefits, neither is every site mobilized and aligned with the importance of SKU rationalization.

Data correctness

During the project, there were confusions and even failures led by the wrong data and it took time to make clarifications. When the supply chain team cross referenced its candidates with the financial performances, the NSV/CMA dataset was completely misleading and ends up with wrong results.

Financial performance of an SKU is one of the most important indicators for SKU rationalization, guaranteeing data completeness and correctness is the essence of reliable solutions.

Pseudo CMA-saving compensation

The CMA-saving compensation logic was claimed as the basis of SKU rationalization decision making at Heinz: an SKU is profitable when its contribution margin (CMA) is higher than the potential savings from complexity costs; while the SKU is loss-making the other way around. Viewing it from another angle, an SKU should be delisted when:

0 CMA Savings

NSV VC Savings NSV VC Saving

 

  

Where Savings refers to savings from complexity costs, VC is variable costs.

However, this statement is invalid in this research. The inequalities above indicate that complexity cost savings should not have any overlaps with variable costs (Figure 7), otherwise the overlapping parts will be deducted twice. Nevertheless, complexity costs could partially land back in the variable cost category on the standard costing system (Figure 10), then the logic does not hold anymore (e.g.

yield loss as one of the complexity drivers is covered by prime costs (see Figure 13)).

The role cost of complexity is discussed deeply in section 3.3.



Figure 7. Relations between sales value, margin, variable costs and complexity costs

2.2 P


For decision makings, the alternative top-down approach could be a fast and effective method for SKU rationalization. The concept of the top-down approach is that instead of asking for estimated complexity savings, business unit will force saving targets from high level management to the downstream sites. Compared to the bottom-up approach, the advantage is that this approach would not be stuck by inadequate data and continuous benefit-loss debate.

Although the thesis adheres to the bottom-up concept, the flaw of the complexity costs estimation in the top-down approach is worth stressing and being emphasized. The method of calculating cost savings per SKU is shown in Figure 8. First of all, SKU rationalization aims at reducing operational complexities, it is important to distinguish complexity costs and total costs of an SKU: complexity costs are driven merely by complexity costs drivers while total costs covers all costs attribute to the SKU.

Complexity costs overlap with total costs but they are calculated from different systems for different purposes. Mixing up should be avoided such that every itemized complexity cost component is directly related to the corresponding complexity driver.

Figure 8. Cost estimation by taking the average in the top-down approach

Secondly, allocating costs to SKUs produced in one factory by simply taking the average can sometimes be dangerous, because it assumes by default that these SKUs are homogeneous. Whether the product portfolio is homogeneous has to be checked in advance. SKUs in the same family might share common characteristics, but SKUs belonging to different ranges could deviate a lot from each other. Assuming homogeneity of all SKUs possibly leads to inaccurate and undependable results.

Identify 5 cost saving areas:

SC loss/write off Change overs/yield Warehousing SKU Handling

Opportunity cost of capital

Calculate total cost per area

Saving per SKU=

(total cost per area) / (the total number of SKUs accountable)



The core of the top-down approach is to encourage and convince the business unit to discontinue SKUs initiatively. Nevertheless, considering the preceding issue of the approximate estimation, if the realized savings diverge from the target, the worst case will be: BU suffers from sales loss from SKUs delisted; the operations department does not manage to save as much as expected because the SKUs on the list do not fit the average line; the loop of re-investment interrupts as the company is not able to invest what they do not save; it will be even harder to get BU’s on board supporting SKU rationalization again in the future.




Complexity management is a preoccupied issue for companies to deal with. One of the biggest complexities Heinz faces is product proliferation. SKU rationalization, just as its name implies, is a lever to reduce complexity through downsizing the product portfolio. As a cross functional project which is to some extent related to every part of the supply chain, it requires a holistic understanding of the complexity drivers internally and externally as well as their implications.

3.1 L


Reviewing the complexity typology, Bozarth et al. (2009) used boundaries to divide complexity categories of downstream, internal and upstream. Upstream complexity concerns supply issues;

internal manufacturing complexity stresses issues of number of products and production process; and downstream part serves customers and control respond to demand variability. Additionally, Vachon

& Klassen (2002) developed a two-dimensional framework with two complexity types: Structure (Process/Product) and Infrastructure (Management System). The degree of complexity is conceptualized in dimensions: Complicatedness and Uncertainty. Similarly, Wilson & Perumal (2009) categorized complexities into product complexity (i.e. the variety of and within the product offered), process complexity (i.e. the number of processes involved in delivering products), and organizational complexity (i.e. the number of facilities, assets, entities involved in executing the processes).

According to the aforementioned typologies and the goal of SKU rationalization, product is the subject of complexity in this research. Jacob (2008) studied product complexity under three levels(portfolio, product, and component), and two dimensions(multiplicity and relatedness). At portfolio level, complexity is measured by the number of different models represented in the product portfolio; at product level, the measurement becomes the number of features or functions embodied in a product and interconnectedness. As for dimensions, multiplicity concerns the number of functions and components within a product, whereas relatedness represents the degree to which those components, subassemblies or other architectural representations are interconnected (Figure 9).

Figure 9. Typology of product complexity (Jacob, 2008)

The necessity of studying the general complexity typology is attribute to supply chain’s special nature of interdependency. Although SKU rationalization is directed connected to the product complexity, it will definitely affect both the upstream sourcing and downstream customer services. Hence, focusing merely on one complexity area can result in limited eyesight and detrimental results, since the impact of the delisting decision on other functional departments can be substantial.



To have a clearer view of how SKU rationalization can reduce complexities along the supply chain, the breakdown of complexity component in functions are shown in Table 4 (Ekinci & Baykasoglu, 2016, Manuj & Sahin, 2011, Rao & Young, 1994, Donk et al., 1996, Choi & Krause, 2006):

Table 4. Complexity components under operational functions

Function Complexity components Product


Customized products, physical characteristics and nature of the items, components(BOM) and variants, life cycles.

Manufacturing process

Lot sizes, production rate and sequence could easily drive complexities, machine breakdowns, changeovers and maintenance, techniques, cycle time, throughout, quality control, policies(MTO vs. MTS), product line capacity, scrap rate.

Supplier-buyer management

Number of suppliers, differentiation of suppliers, reciprocal inter-relationships among suppliers, delivery reliability, multiple marketing channel.

Production planning

Demand forecast, order fluctuation, sourcing originations, service level.

Inventory control

Managing discontinued or leftover stock, limited storage capacity, number of order lines, safety stock levels, replenishment policy.

Sales &


Selling channels, pricing strategy, assortment and display, branding, managing slow selling stock through outlet, internet or catalogue, geographical locations of stores, business strategy alignment, budget.

Outsourcing Number of SKUs produced outside BU, make-or-buy decision making, third- party providers.


Inbound and outbound shipment, shipping lanes, transportation modes, location selection, scheduling, equipment sequencing and maintenance, terminal control, handling, line-haul service, tracking and tracing, transitioning, supplying and distribution trading partners.

Network Number of types of connection and linkages in supply chain map.

Apostolatos et al. (2004) summarized the impacts of complexity on supply chain which is close to Heinz’s situation, the division of complexity drivers under each area are displayed in Table 5 below:

Table 5. Complexity drivers summary under functions

R&D Procurement Conversion Supply chain Marketing & Sales

· More effort due to more products to be developed

· Deteriorati on of innovation power due to less focus

· Spend dispersion

· Higher purchasing prices due to lower unit volumes

· Higher administrat ion costs

· Higher inbound logistics costs

· Higher

changeover costs

· Higher inventory costs

· Lower learning curve

· Higher quality control costs

· Higher waste &

yield loss

· Less responsive planning &


· Lower line utilization rates

· Too many assets

· Higher handling costs due to smaller lot sizes

· More effort in planning and scheduling

· Higher inventory costs

· More warehousing space

· Lower pallet/truck fill rate

· Higher

obsolescence costs

· Lower marketing spend available per brand/product

· Lower efficiency and effectiveness due to lack of product focus

· More effort account management

· More effort in order


· More effort in customer service

· Reduced forecast accuracy



Table 5 covers most of the complexities Heinz has confronted. Since SKU rationalization narrows down the scope to inhouse-manufactured SKUs, the area of conversion and supply chain complexities will be paid close attention to (Table 6) and details regarding each complexity driver will be elaborated in Chapter 4.

Table 6. Complexity drivers under the conversion function

Category Complexity Elaboration


Changeover Low volume SKUs, short production run, poor production scheduling, batch sizes, nr. of SKUs on a line;

Yield loss Overfill rate, cleaning frequency, spill, defection rate.

Write off (w/o)

Production volume, forecast accuracy, MOQ, shelf life, Trade BBE, quality assurance period, lead time, safety stock period, unique material, low rotation.

Inventory costs (FG)

MOQ violation, unique material, low rotation items, safety stock level, demand variability, demand forecast accuracy, inventory policy.

3.2 C


Jacob (2008) proposed three strategies to manage complexity: a)Standardization: minimize redundancy and supply disruptions risks by using common products; b) Product platforms: to migrate products from one quadrant of the BCG matrix to another (e.g. enabling entry into new markets gives chance to create new stars while facilitating cash cows through scale economies. Revenues will also grow for each variant because the variant can be targeted at an untapped niche) (see section; c) Modularization: minimize the interdependencies products, the benefits can be expressed in economies of scale, inventory reduction, engineering efficiencies, improved coordination between functions, etc.

In addition, Apostolatos et al. (2004) also provided several complexity management strategies which are closer related to SKU rationalization: a) Elimination: Eliminate complexity that customers will not pay for; b) Leveraging: Exploit to the fullest complexity (product or service value attributes) that customers will pay for; c) Adapting: Reduce to the minimum the costs of any complexity you offer.

Manuj & Sahin (2011) brought up the term “solid process” to demonstrate that reducing duplication and redundancy, eliminating non-value added steps and channels will weaken the negative effect of complexity. Except for standardization and modularization, another lever that many scholars emphasize is segmentation, which is applied later section 4.1—SKU family segmentation, since it will be easier to manage SKU families than thousands of scattered SKUs. Product segmentation is an effective method to understand performance of the existing portfolio. The commonly used methods are ABC analysis, life cycle phases, BCG matrix, etc. as SKU rationalization is defined as the process of evaluating each item based on its contribution to overall business objectives (Gilliland, 2011), it helps to find the optimal configuration of the product portfolio to maximize profitability, fit overall strategy and balance the portfolio (Tolonen, 2015). It is also proved moderating of partitioning activities with scope and boundaries and assign the subset of SKUs to a particular group of manufacturing or distribution facilities (Manuj & Sahin, 2011).



In accordance with the strategies of complexity management, some guidelines are followed and implemented: make trade-offs explicit; install cross-functional project team, link benefits to KPIs, and institutionalize the process.


Profitability vs. Complexity

Complexities come along with reasons (e.g. market demand, customer requirements, product range completeness, lower purchasing price, maintaining shares, updated techniques and system, etc.).

Every complexity enters the system as an expense of the corresponding benefits, and the decision is made only under the justification that benefit prevails over the negative effect it brings, i.e. the complexity. Despite that the marketing department may detect the prevailing trend of a new product variety and predicts it could hit millions of sales value, operational complexities should be examined when procurement team is negotiating material prices with additional suppliers, or extra minutes spent on changeovers when a new SKU joins the production line, etc. This concerns the first trade- off—whether the overall profitability generated offsets the cost of complexity (COC). What should be emphasized is that the trade-off is between the additional complexity and the according incremental profitability. The above discussion concerns the general profitability-complexity relation of introducing new products into the portfolio, the cost impacts of delisting SKUs by SKU rationalization is further analysed in section 3.3.

Batch size

The second trade-off is regard to production batch size. At Heinz, MOQ is an ongoing debating subject.

Low-volume/small-batch items lead to short production runs and frequent changeovers, and therefore impede efficiency and add difficulties to procurement teams to negotiate for lower prices.

To allow scale advantages, MOQ and hurdle rate are set to control the minimum quantity.

Furthermore, scale effect also helps develop human cognitive abilities and shape the experience curve for efficiency gains. However, on the other hand, a system also needs to be agile and flexible. Funk (1995) stressed the importance of manufacturing smoothness. He stated smaller lot sizes enables factories to react promptly to schedule change and to product a broader variety of SKUs. Moreover, when the production volume is restricted by MOQ and higher than actual demand, the exceeding part will lead to high risks of write off.

Brand integration (M&A)

This trade-off is not closely related to SKU rationalization, but as Kraft and Heinz just merged in 2015, it is something worthwhile to think through. Being one of the complexity drivers, M&A brings plenty of complicated issues for managers to deal with, including the significantly enlarged product portfolio, changing of market segment, volatility, shift of strategies and business process, adjustment of infrastructure asset, etc. This boils down to the question whether to use the same brand name in different countries and leveraging brand strength across boundaries, or to focus on local brands responding to local customer preference. Heinz chose to keep the mix of local and international brands to be responsiveness and adapt to the local competitive environment. This will, however, sacrifice the advantage of economies of scale and standardization of a harmonized brand architecture (Apostolatos et al., 2004)



As mentioned earlier, SKU rationalization affecting the entire supply chain makes consistent communication and coordination very important. Manufacturing managers tend to be supportive of the rationalization since redundant product variances only make their work more difficult, but sales team is concerned about the revenue loss. Each function could propose well-reasoned SKU candidates to delist but the final decision can only be made when Operations, Finance, and Marketing& Sales are all on board. The initial project team for SKU rationalization at Heinz is configured as the following:

· Marketing operations

· Commercial Finance

· Procurement Copacking

· Procurement Raw materials & Packaging

· Sales & Operations Plan(S&OP)/ supply planners

· External operations Copacking

· Supply chain Finance/ site controllers 3.2.3 LINK BENEFITS TO KPIS

With a cross-functional structure, not only the project managers is motivated, the rest of the team members should also be mobilized. As we experienced, this project is not prioritized on team members’

task list and they are reluctant to spend time on it and response promptly. One possible way to encourage concerted effort is to link the project to employees’ KPIs and make the benefits visible. For supply chain department, most KPIs relate to Operational cost, Factory Championship and Service level. Although SKU rationalization poses potential risks of sales loss, it directly contributes to operational cost savings, smoothens the production, relieves pressure on over-utilized lines and further increase the order fill rate. More importantly, SKU rationalization offers a platform for all functions and sites to point out SKUs that hurt their interests and expose them under evaluations of delisting.


SKU rationalization is deployed as a long-term, ongoing exercise, since a one-time pruning does not prevent the portfolio from expanding in the future. For the purpose of maintaining the product portfolio at a relatively stable and heathy level, an institutionalized process helps avoid needless double work, as well as enables the consistency and continuity of the project.

When multiple optimization programs are running simultaneously at Heinz (e.g. NOD, Honig Optimization, SKU rationalization, etc.), it happens that candidates selected for SKU rationalization are already agreed to be delisted. For people who have no knowledge of other programs, this requires manual work to screen them out. Although environment is ever-changing, the structure of SKU rationalization from supply chain perspective should be institutionalized for the convenience of handover and replication. Besides, reviewing sessions should be hold to monitor the results of complexity reduction and to incorporate lessons learned in future plans.

3.3 C


In this section, the role of complexity costs (COC) in SKU rationalization is elaborated in detail. As the CMA-saving compensation logic is proved flawed in section 2.1.3, the application of quantified complexity costs is discussed at first. To comply with the objective of the thesis, the results are



supposed to be used as inputs of SKU rationalization. Then two issues of complexity costs hidden and complexity costs distortion are addressed afterwards.


Figure 10. Profit & Loss journey

Dealing with the hidden complexities requires the understanding of the cost terms of the current standard costing system. The Profit & Loss (P&L) report in Figure 10 shows the costs categories and calculations of profits: the gross margin is defined as sales value less the cost of goods sold (COGS);

the contribution margin (CMA) is defined as the sales value less only the variable costs (including VIC and VLC); on top of CMA, the operating profit also covers Fixed costs category and One off costs category and in the end, the final earnings capture the CAPEX costs as well.

We assume there is a “net profit” that takes into account the total costs loaded on each SKU. If the quantified complexity costs are hidden in the total costs and are able to be fully landed back on the P&L, then the profitability of the SKU is independent of the amount of complexity costs estimated.

The situation is simplified in the equation and Figure 11 below: when the complexity costs are covered by the total costs, whether the SKU is profitable or loss making is not affected by its complexity costs, the sales/profit loss would not be compensated by cost savings of itself either. Instead, it is



compensated by increased sales of substituting SKUs (or branded SKUs to which the spare resources after rationalization could be allocated more), streamlined manufacturing footprint, higher efficiency and utilization rate, simplified and consolidated unique features and supply bases, better customer service and higher fill rate, etc.

Sales − Total costs = Net profit

Figure 11. Complexity costs hidden in the standard costing system

To connect the complexity costs to the current costing system, the variable/fixed and direct/indirect costs construct is discomposed in Figure 12 and Figure 13. Seals et al. (2012) defined manufacturing costs under 3 components: direct material, direct labor, and overhead, representing the costs attributable to goods produced and sold by a business. Whereas Heinz’s standard cost (COGS) contains slightly different components, which include fixed costs as well (Figure 13). Clarifying costs components could help retrieving complexity costs to the corresponding category.



Figure 12. Cost classifications on financial statements (adapted from Seal et al., 2012)

Figure 13. Cost components of Heinz standard cost


Preferably, the costing system is appropriate for managing complexity such that SKUs’ level of complexity can be revealed by their costs. The traditional costing method is not able to recognize the SKU variances completely and allocate the cost properly to reflect the true complexity level of the SKU.

The direct costs (material and labor) can be physically and conveniently traced to a product (Seal el al., 2012), while indirect overhead costs are less straightforward to allocate to individual SKUs. When the cost driver system is disconnected to the complexity drivers (e.g. COC is born evenly by all SKUs




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