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Research Report: ZLC-2010-7
Classification of SKUs as a Way to Improve Company Performance:
A Case Study from the Telecommunication Industry
MIT Global Scale Network
For Full Thesis Version Please Contact:
Marta Romero
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KEY INSIGHTS
1. Inventory classification is able to provide benefits for the company via treating heterogeneous items in a different way.
2. Different classifications produce diverse results, which affect company performance in terms of costs associated with inventory
3. There is no the ¨best¨ classification method. As assumptions and conditions change, different classifications outperform each other.
Introduction
Inventory being an integral component of operations of virtually every company has been a matter of great concern and increasing attention in the academic and business world for the last few decades. Indeed, carrying high levels of inventory incurs financial and operating costs undesirable for any company. However, for the companies functioning in certain industries, the issue becomes a matter of vital importance.
The present Thesis Project is sponsored by company named TeleCo (a fictitious name was used in order to hide the company‟s true identity) that
operates mainly as an Internet, mobile network and cable television provider. The nature of these operations implies rendering repair services, and therefore, carrying a certain level of spare parts inventory.
Companies operating in the telecom industry are required to have as a high service level as possible. Indeed, the outcomes of shortage of important spare parts can be disastrous for telecom companies, especially if institutions such as banks or hospitals are affected. This service constraint leads to carrying extremely high levels of spare parts, which in general are expensive. The resulting high capital cost moves companies in the opposite direction – towards lowering inventory level.
The dilemma described above poses the following challenge to TeleCo:
How could the Company optimize the level of spare parts inventory given the service level in the telecommunication industry it operates?
TeleCo holds a substantial number of spare parts,
which are extremely heterogeneous in many ways. Naturally, this diversity requires applying different inventory policies, which is not realistic without appropriate spare parts classification. Hence, the
Classification of SKUs as a Way to Improve
Company Performance: A Case Study from
the Telecommunication Industry
By Yuliya Kasirava and HanCheol Lee Thesis Advisors: Dr. Mozart Menezes
Dr. Mustafa Çagri Gürbüz
Summary:
This thesis offers classification as one of the methods to lower the cost associated with spare parts inventory for a company operating in the telecommunication industry. Several classification methods were examined and compared in terms of total relevant costs. Further simulations were carried out in order to examine the impact on total costs.
University Degree in Economics with Major in World Economy, Grodno State University, Belarus Master of Economic Science in European Economic and Public Affairs, University College Dublin, Ireland
Bachelor of Arts in Geography Education, Seoul National University, South Korea Supply Chain Manager, LG Telecom, Seoul, South Korea
approach to inventory optimization without hurting customer service level would be multicriteria classification of spare parts.
First, we applied four classification methods to the data sample obtained from TeleCo and illustrated the disparities of classification results across different approaches. Our next step was the investigation of the impact that different classifications have on the total cost associated with inventory. Further, the sensitivity of total cost was examined while changing different assumptions. The final step was determining the optimum classification for the data sample under study.
Classification
We applied seven criteria in order to classify the sample of 109 spare parts obtained from TeleCo. The criteria included price of spare part, total demand, variation of demand across different regions, mean of lead repair time, coefficient of variation of lead repair time, criticality, and accuracy of replacement.
Four classification methods were employed, specifically, ABC analysis, multicriteria ABC-based classifications using Analytic Hierarchy Process (AHP) and weighted linear optimization (so called Ng model), and cluster analysis.
ABC analysis is a simple and widely applied technique in the managerial world, which is based on 80/20 Pareto law. The law states that 20% of SKUs (class A items) generate 80% of dollar usage, which is a product of price and annual demand. However, it is often criticized for employing only a single criterion, namely, annual dollar usage, and therefore ignoring other crucial aspects worth being considered.
Multicriteria ABC-based classifications fill the gap, introducing several criteria into the analysis. One of the main issues with the methodology is criteria prioritizing or assigning weights to each criterion. The problem was solved by using the AHP approach, which is based on pair wise comparisons of criteria by decision makers. The alternative solution was obtained by applying the linear weighted optimization model in order to get weights for each criterion. Clustering is a statistical method that is often regarded as a form of classification. The goal is that the objects within the groups be similar to each other, and different from the objects in other groups. We used the SPSS software package in order to obtain three clusters which were further approximated to A, B and C groups.
We found out that all classifications produce different results, which are illustrated on the Figure below.
ABC AHP A B C sum A B C sum A 5 8 24 37 A 12 6 7 25 Cluster B 13 8 13 34 B 7 4 19 30 C 7 14 17 38 C 6 20 28 54 sum 25 30 54 109 sum 25 30 54 109 AHP Ng A B C sum A B C sum A 17 17 3 37 A 16 7 2 25 Cluster B 8 9 17 34 B 6 5 19 30 C 0 4 34 38 C 3 18 33 54 sum 25 30 54 109 sum 25 30 54 109 Ng Ng A B C sum A B C sum A 12 18 7 37 A 14 11 0 25 Cluster B 6 12 16 34 B 7 15 8 30 C 7 0 31 38 C 4 4 46 54 sum 25 30 54 109 sum 25 30 54 109 ABC ABC AHP
Total relevant cost (TRC)
In this paper, we considered inventory holding cost, shortage cost, review management labor cost, and transportation cost as a total relevant cost (TRC) to determine the impact of each classification method on management performance. Also, several assumptions were made, including both endogenous and exogenous assumptions. Under current assumptions, the total relevant costs are seen below.
Current ABC AHP Cluster Ng Model Inventory Holding Cost 5,710 7,533 9,574 9,784 8,400 Review Management Cost 1,635 2,220 2,220 2,920 2,220 Transportation Cost 25,533 17,500 16,033 15,267 14,767 Shortage Cost 25,663 25,663 25,663 25,663 25,663 Total Relevant Cost 58,542 52,916 53,490 53,634 51,050
TRC simulations with endogenous assumptions
The simulations about endogenous assumptions, which managers can change, allow managers to easily find where the total TRC has the lowest value. The endogenous assumptions include review periods, service level, and transportation modes. First of all, the review periods for group A items should be 2 or 3 weeks depending on the classification methods, while those for group B should be 3 or 4 weeks, and those for group C 3 or 4 months. (Green shaded cells represent the lower cost, while red cells the higher cost.) 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 0.25 52,009 51,241 51,071 51,050 51,087 51,154 51,237 51,329 51,429 51,532 51,639 51,748 51,858 51,970 52,083 52,196 0.50 51,717 50,949 50,779 50,758 50,795 50,862 50,945 51,037 51,137 51,240 51,347 51,456 51,566 51,678 51,791 51,904 0.75 51,902 51,134 50,964 50,943 50,980 51,047 51,130 51,223 51,322 51,425 51,532 51,641 51,751 51,863 51,976 52,089 1.00 52,194 51,426 51,256 51,235 51,273 51,339 51,422 51,515 51,614 51,718 51,825 51,933 52,044 52,156 52,268 52,381 1.25 52,522 51,754 51,585 51,563 51,601 51,668 51,750 51,843 51,942 52,046 52,153 52,262 52,372 52,484 52,596 52,710 1.50 52,864 52,096 51,926 51,905 51,942 52,009 52,092 52,185 52,284 52,387 52,494 52,603 52,714 52,825 52,938 53,051 1.75 53,209 52,441 52,272 52,250 52,288 52,355 52,437 52,530 52,629 52,733 52,840 52,949 53,059 53,171 53,283 53,397 2.00 53,555 52,787 52,617 52,596 52,634 52,700 52,783 52,876 52,975 53,079 53,186 53,294 53,405 53,517 53,629 53,742 2.25 53,899 53,131 52,962 52,940 52,978 53,045 53,127 53,220 53,319 53,423 53,530 53,639 53,749 53,861 53,973 54,087 2.50 54,241 53,473 53,303 53,282 53,319 53,386 53,469 53,562 53,661 53,765 53,871 53,980 54,091 54,202 54,315 54,428 2.75 54,579 53,811 53,642 53,620 53,658 53,725 53,807 53,900 53,999 54,103 54,210 54,319 54,429 54,541 54,653 54,767 3.00 54,915 54,147 53,977 53,956 53,993 54,060 54,143 54,235 54,335 54,438 54,545 54,654 54,764 54,876 54,989 55,102 3.25 55,247 54,479 54,309 54,288 54,325 54,392 54,475 54,567 54,667 54,770 54,877 54,986 55,096 55,208 55,321 55,434 3.50 55,575 54,807 54,638 54,616 54,654 54,721 54,803 54,896 54,995 55,099 55,206 55,315 55,425 55,537 55,649 55,763 3.75 55,901 55,133 54,963 54,942 54,980 55,046 55,129 55,222 55,321 55,425 55,531 55,640 55,751 55,862 55,975 56,088 4.00 56,224 55,456 55,286 55,265 55,302 55,369 55,452 55,545 55,644 55,748 55,854 55,963 56,074 56,185 56,298 56,411
Group B review period (months)
Group A review period (months) Ng Group A Group B More frequent
(Short review period) Less frequent
More
As for the service level, under current assumptions, the spare parts with „H+‟ and „NBD/E‟ criticality should have 99% of service level while those with „NBD‟ 96% under current assumptions. This is mainly because the impact of penalty constant currently assumed is much higher than that of inventory holding cost, which is resulting from the attribute of telecom industry – “always on service”. When the assumptions about penalty constants are relaxed while increasing the inventory holding cost, the optimal set of service levels can be changed.
When it comes to transportation modes, once classification methods are introduced, then it is easier to apply aggregate orders because different review periods and policies can be applied to the company‟s inventory. As an example of this, the Ng model can have the lowest cost if group A items are delivered by ground parcel and group B and C items by less-than-truckload.
TRC simulations with exogenous assumptions
The simulations about exogenous assumptions, which managers cannot choose, can make it easier to predict what classification method will be the best under given situations, which accordingly allows managers to properly cope with future changes in advance. These exogenous assumptions include the inventory holding unit cost, the labor salary, and the transportation cost.
There can be a trade-off between inventory holding cost and salary for those in charge of inventory review activity, because frequent review usually lowers the inventory level and increases the labor cost. If other assumptions stay the same, the Ng model has the best management performance and a quite stable position regarding changes in the salary level. Also, the Ng model yields the lowest cost regardless of the inventory holding cost.
Unlike that of the salary and the inventory holding cost, the simulation results of transportation costs show unstable patterns of the best classification like the Figures below. First of all, if the variable cost of the air parcel service is low, then the current method, in which TeleCo orders spare parts whenever one item is used, is the best. This is because there is no reason to aggregate procurement orders. However, as the variable cost of the air service increases, the probability that the current situation would be the best decreases.
Variable Cost for Air Parcel
Low High
Low
High
Current Current Current ABC Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current ABC Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current ABC Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster
Fi xed C o st fo r LT L Ng Model Current ABC C lu ster
Current Current Ng Ng Ng Ng Ng Ng Ng Ng Cluster Cluster Cluster Cluster Cluster Cluster Current Current Current Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Cluster Cluster Current Current Current ABC Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Cluster Current Current Current ABC ABC Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Current Current Current Current ABC ABC Ng Ng Ng Ng Ng Ng Ng Ng Ng Ng Current Current Current Current ABC ABC ABC Ng Ng Ng Ng Ng Ng Ng Ng Ng Current Current Current Current ABC ABC ABC ABC Ng Ng Ng Ng Ng Ng Ng Ng Current Current Current Current Current ABC ABC ABC ABC Ng Ng Ng Ng Ng Ng Ng Current Current Current Current Current ABC ABC ABC ABC ABC Ng Ng Ng Ng Ng Ng Current Current Current Current Current Current ABC ABC ABC ABC ABC Ng Ng Ng Ng Ng Current Current Current Current Current Current ABC ABC ABC ABC ABC ABC Ng Ng Ng Ng Current Current Current Current Current Current ABC ABC ABC ABC ABC ABC ABC Ng Ng Ng Current Current Current Current Current Current Current ABC ABC ABC ABC ABC ABC ABC Ng Ng Current Current Current Current Current Current Current ABC ABC ABC ABC ABC ABC ABC ABC Ng Current Current Current Current Current Current Current ABC ABC ABC ABC ABC ABC ABC ABC Ng Current Current Current Current Current Current Current Current ABC ABC ABC ABC ABC ABC ABC ABC
Variable Cost for Air Parcel
Low High Low High V a ri a b le c o st fo r G ro u n d P a rc e l Ng Model Current ABC Cluster Conclusions
Our research started with the belief that classifications of spare parts can provide TeleCo with the opportunity to reduce the total relevant cost, like the transportation cost, without hurting the service level because different decisions on the inventory can be made by the classification segment.
This paper introduced several classification methods to TeleCo, including not only the ABC method but also the multicriteria classification methods such as the AHP, the Cluster, and the linear optimization model. The reason why multicriteria classification methods were introduced is that the ABC method cannot perfectly reflect the attribute of the telecom industry – „always on‟.
After that, the impact of each classification method on the management performance was examined to see which one can have the lowest total relevant cost. On top of that, several simulations about how the total relevant cost and the best classification method vary were conducted by applying different levels of assumptions.
One of implications from the findings of this research is that there is not „one best‟ classification method that can be applied for all industries or for all situations. Therefore, managers have to revise the company‟s classification of spare parts and to examine this impact regularly. In addition, many papers regarding classification usually compare only the different grouping results of classifications, but do not consider the management impact. In this sense, this paper can make a contribution to the field of classification research.
