Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction

Business Scenario at SAP System

Eren Esgin

AI Research, MBIS R&D Center, Istanbul, Turkey

Informatics Institute, Middle East Technical University, Ankara, Turkey

Keywords: Classification, CRISP-DM, Intelligent Maintenance, SAP, Spare Part Prediction, Weighted k-Nearest

Neighbor.

Abstract: In the context of intelligent maintenance, spare part prediction business scenario seeks promising return-on-

investment (ROI) by radically diminishing the hidden costs at after-sales customer services. However, the

classification of class-imbalanced data with mixed type features at this business scenario is not straightforward.

This paper proposes a hybrid classification model that combines C4.5, Apriori algorithms and weighted k-

Nearest Neighbor (kNN) adaptations to overcome potential shortcomings observed at the corresponding

business scenario. While proposed approach is implemented within CRISP-DM reference model, the

experimental results demonstrate that proposed approach doubles the human-level performance at spare part

prediction. This highlights a 50% decrease at the average number of customer visits per fault incident and a

significant cutting at the relevant sales and distribution costs. According to best runtime configuration analysis,

a real-time spare part prediction model has been deployed at the client’s SAP system.

1 INTRODUCTION

Average number of customer visits per fault incident

is a critical key performance indicator (KPI) at after-

sales customer services such that, undesirable

repetitive customer visits result in a significant

increase at hidden sales and distribution costs.

Additionally, it may affect the quality level of after-

sales services and deteriorates the organizational

goodwill at long run. Respectively, spare part

prediction business scenario aims to generalize the

spare part consumption patterns according to failure

characteristics, product’s own features and consumer

detailed information and then proactively proposes

the most probable spare part for new failure incident.

Although classification algorithms have been

widely used in retail, finance, banking, security,

astronomy and behavioral ecology domains

(Kantardzic, 2011) and the classifiers for class-

balanced data are relatively well developed, the

classification of class-imbalanced data with mixed

type features is not straightforward

(Liu et al., 2014).

This paper proposes a hybrid classification algorithm

such that, while Apriori is adapted to handle data

anomalies and redundancies observed at data

preparation, significance weights obtained at C4.5 are

used for doing normalization on categorical features

to adapt the inter-dimension similarity at computing

the similarity between fault instances. As the

following, two adaptations of weighted kNN are

applied: while instance based kNN with count

(

IkNNwC) gives more importance to major instances

that are more likely to represent a dominant class in

neighborhood region of feature space, instance based

kNN with average similarity score (

IkNNwAS) aims

to balance the discriminative power of minor (or

outlier) instances. Proposed approach is evaluated

according to the fault records of television (TV)

product group within 5 years’ time period and full-

cycle data mining framework, which covers all

phases from business understanding to deployment, is

implemented according to CRISP-DM (Cross

Industry Standard Procedure for Data Mining)

reference model.

The paper is organized as follows. Section 2

reviews the related work about kNN adaptations.

Section 3 explains the proposed classification

approach within the context of CRISP-DM reference

model such that, business understanding, data

understanding, data preprocessing and modeling

phases are briefly explained. Section 4 discusses the

experimental results according to the performance

218

Esgin, E.

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System.

DOI: 10.5220/0009103202180226

In Proceedings of the 9th International Conference on Operations Research and Enterprise Systems (ICORES 2020), pages 218-226

ISBN: 978-989-758-396-4; ISSN: 2184-4372

observed at evaluation and deployment phases. The

conclusion and future work are summarized in

Section 5.

2 LITERATURE REVIEW

kNN algorithm considers a firm representative of the

classification by analogy (Domingos, 2015). Naturally,

finding an optimal value of k, which represents how

many closest neighbors are to be considered, has been

one of the questions that some works have attempted

to solve

(Zhang et al., 2017; Zhu et al., 2016). Besides

finding the k-value, the underlying distance

calculation is another issue in this kind of

classification. Using a weighted scheme was first

introduced by

(Dudani, 1976), this variant of kNN is

called Distance-Weighted k-Nearest Neighbor

(

DWkNN). (Tan, 2015) proposed the algorithm

Neighbor-Weighted k-Nearest Neighbor (NWkNN),

which applies a weighing strategy based on the

distribution of classes.

(Mateos-Garcia et al., 2016)

developed a technique that optimizes the weights that

would indicate the importance of neighborhood in a

similar way of Artifical Neural Network.

(Parvinnia et

al., 2014) also computed a weight for each training

object based on a matching strategy. Respectively,

(Aguilera et al., 2019) proposed a weighting based on

Newton’s gravitational force, so that a mass (or

relevance) is to be assigned to each instance. Two

methods of mass assignment is presented: circled by

its own class (CC) and circled by different class (CD).

The standard kNN algorithm is not suitable for the

presence of imbalanced class distribution. Hence,

kENN in (Yuxuan & Zhang, 2011) and CCW-kNN in (Liu

& Chawla, 2011)

have been proposed to improve the

performance of kNN for imbalance classification.

While

kENN proposed a training stage where positive

training instances are identified and generalized into

Gaussain balls,

CCW-kNN uses the probability of

feature values given class labels to weight prototypes

in kNN.

(Song et al., 2007) also proposed new kNN

algorithms based on informativeness which is

introduced as a query-based distance metric. This

informativeness is handled in two concerns: locally

informative (

LI-kNN) and globally informative (GI-

kNN

). Alternatively, (Wang et al., 2011) presented a

coupled nominal similarity to examine both intra- and

inter-coupling of categorical features. These

approaches majorly focused on the clustering on

class-balanced data.

3 PROPOSED APPROACH

CRISP-DM reference model is applied as the major

road map for spare part prediction scenario.

Respectively, the underlying sequence of the phases

is not rigid, moving back and forward between

difference phases is always required

(Chapman et al.,

1999)

. CRISP-DM reference model consists of six

phases: business understanding, data understanding,

data preparation, modelling, evaluation and

deployment. Except evaluation and deployment

phases, we briefly outline corresponding phases at the

following sections.

3.1 Business Understanding

In current (as-is) situation, each customer call to

customer call center triggers a new fault record at

SAP CRM system. During this call, fault occurrence

details (e.g. product group, complaint or symptom

information in a hierarchical manner) are gathered

from the customer. Then, customer details (e.g.

customer profile and location) are enhanced and

product details (e.g. product SKU (stock keeping

unit), material type, material group and product

hierarchy) are extracted from prior product assembly

history at SAP CRM system. Afterwards, the

corresponding fault record is assigned to a near-by

technical service according to customer’s location.

Finally, the technical service makes a feasibility visit

to check out the fault reason and defective

component. Each customer visit for the

corresponding fault incident is managed by a unique

maintenance line item and spare part consumption or

maintenance activity at this customer visit is charged

to this line item.

As the to-be situation, it is aimed to position a

spare part prediction model that suggests the most

probable spare part for the corresponding fault

incident and passes this suggestion to the technical

service in a real-time manner. Hence technical service

can proactively reorganize the in-car spare part stock

and daily customer routes. Moreover, it is aimed to

radically diminish average number of customer visits

per fault incident. Indeed, hidden sales and

distribution cost items and spare part consumptions

are strongly correlated to the number of customer

visits and reductions at the corresponding KPI will

minimize relevant expenses at income statement (e.g.

freight costs, maintenance and depreciation costs of

technical service vehicles, etc.). As an intangible

outcome, we also aim to improve the quality level of

after-sales services and increase the organizational

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System

219

goodwill in long run. Current as-is and proposed to-

be situations are represented in Figure 1.

Figure 1: Current (as-is) and to-be scenarios. By the effect

of spare part prediction model, undesirable repetitive

customer visits will be lessened.

As the human-level performance in the current as-is

situation, the average number of customer visits per

fault incident KPI is approximately 2.5, which means

a 40% accuracy at predicting the appropriate spare

part. The data mining objective in this business

scenario is 80% accuracy and this implies halving of

relevant sales and distribution costs.

3.2 Data Understanding

Data understanding phase starts with describing

major data sources, the relations among these data

sources and major attributes that build up the initial

raw data. At first, the data dictionary enlisting all

gross and surface properties of the initial raw data is

described. Then, the corresponding data description

is explored to assess potential anomalies and data

redundancies among the attributes and verify data

quality problems in order to refine the initial raw data.

3.2.1 Data Description

The corresponding business scenario is composed of

five data sources:

 Fault Incident. Fault incident holds the header

information of corresponding incident record,

e.g. fault incident ID, incident date and time,

symptom codes, document status, relevant

customer ID and product SKU.

 Maintenance Line Item. Maintenance line item

holds spare part consumption and maintenance

activity charged at each customer visit. There

exists a one-to-many (1:N) relation between fault

incident and maintenance line item.

 Product. Product holds the major features about

the defective product, e.g. product SKU, material

type, material group, product hierarchy, brand

and product costing group.

 Product Details. Product details holds major

production details, e.g. production date and

warranty beginning date. There exists a one-to-

one (1:1) relation between product and product

details data sources.

 Customer. Customer holds the customer profile

and location in a city-to-district hierarchy.

Context diagram given in Figure 2 depicts the

relations among the data sources.

Figure 2: Context diagram for the corresponding data

sources.

3.2.2 Data Exploration

Data exploration assesses the correlation among the

attributes and checks whether any data anomalies and

redundancies occur. According to these assessment

actions, while each instance at initial raw dataset

represents a unique maintenance line item, a

significant data replication problem has emerged such

that; except the spare part target class, all attributes

are acquired from the same data sources, i.e. fault

incident, product, product detail and customer. As a

result, there occurs distinct instances featured with

replicated (the same) attribute values and distinct

spare part target value at initial raw data collection.

As a solution, each instance should be characterized

at a higher abstraction level by relating to a unique

fault incident. Hence, an alternative data exploration

procedure is applied to collect the spare part target

class as shown in Figure 3.

Accordingly, alternative data exploration

procedure is composed of three steps as follows:

 Raw Data Aggregation. This initial step

aggregates the spare part consumptions at each

maintenance line item that are relevant to the

same fault incident. Respectively, it resembles

transposing

the spare part values at relevant

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Figure 3: Alternative data exploration procedure. A fault incident with four maintenance lines is transformed into a single

instance at final dataset.

maintenance line items and concatenating these

values into a single derived target class, i.e.

SP_AGGR. While concatenating spare part values

at a fault incident, duplicated values are unified

and unique values are sorted in ascending order,

e.g.

<SP1, SP1, SP3, SP2> is aggregated as <SP1,

SP2, SP3>.

 Association Rule Generation. Respectively,

aggregated dataset is like frequent itemsets and

these itemsets can be represented by a Boolean

vector of spare part values to underlying

variables. Indeed, these Boolean vectors can be

analyzed for spare part consumption patterns that

highlight frequently associated spare part

combinations. These patterns can be represented

in the form of association rules.

Apriori is a seminal algorithm proposed for

frequent itemsets for Boolean association rules.

The name of the algorithm is based on the fact

that it uses prior knowledge of frequent itemset

properties

(Kantardzic, 2011). In this context,

Apriori is applied by using R (R packages:

arules and arulesViz). Then generated

association rules are filtered by min_lift, i.e.

min_lift > 1.0 threshold is used to extract only

positively correlated spare part combinations.



Rule Induction. Rule induction step converts

aggregated spare part target class values

(

SP_AGGR) into refined forms according to

previously generated association rules.

In this aspect, significant association rules are

determined by min_confidence threshold, the

default value of this parameter is 0.8. Filtered

association rules are sorted by lift and confidence

values in descending order. Afterwards, each

spare part target class value at aggregated data set

is searched at association rules whether

antecedent and consequent of the association rule

both exist at the corresponding aggregated spare

part target class value. In the case of presence,

consequent is removed from the aggregated spare

part target class value and this new value is

assigned to a new target class, i.e. rule induced

spare part

SP_RIND. Otherwise, original value of

aggregated spare part is copied to rule induced

spare part target class.

Figure 4 shows the effect of rule induction step at

the frequencies of target classes, i.e.

SP_AGGR and

SP_RIND. Due to <320001053>→<303113320>

association rule, there happens a significant increase

at the frequency of

320001053.

Figure 4: Frequency of spare parts according to aggregation

and rule induction operations.

3.3 Data Preparation

Data preparation covers data integration,

transformation and cleaning activities that are required

to construct the final dataset from the initial raw data.

In data integration step, an appropriate SQL script

according to the context diagram given in Figure 2 is

implemented to extract the fault incidents of

television (TV) product group, which occurred within

5 years’ time span (between year 2014 and 2018)

from SAP CRM and SAP BW source systems,. Then

alternative data exploration procedure stated in

Section 3.2.2 is applied to avoid data replications and

anomalies observed at maintenance line item level.

Due to raw data aggregation step at the underlying

procedure, a vertical data reduction occurs such that;

750K instances at maintenance line item dataset are

suppressed to 350K instances at aggregated final

dataset. Additionally, in order to avoid attribute

redundancy due to hierarchical (ordinal) attributes

(e.g. customer location, product hierarchy and

symptom codes), attributes with relatively higher

detail level and wider value range are selected. For

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System

221

instance, product hierarchy 8 attribute (PRDHYR8) is

selected as the most detailed product hierarchy

feature and lower level attributes (

PRDHYR2–7) are

omitted. At data transformation step, month and year

attributes are parsed from the underlying date typed

attributes, e.g. incident date and production date.

Moreover, new attributes such as product age,

product stock age and warranty status are derived.

Indeed, the value range of rule induced spare part

target class (

SP_RIND) is composed of 1267 distinct

values. Hence, the instances with relatively less

frequent spare part values (i.e.

freq(RIND_SP)<1000

condition refers to a 0.22% frequency) are eliminated

at data cleaning step and a 95.27% total coverage at

final dataset is achieved after this operation as shown

at frequency histogram given in Figure 5.

Additionally, various spare part groups are defined

according to the frequency order such as

ALL, TOP3

and TOP6 such that, TOPX implies topmost X spare

parts according to the frequency at the final dataset.

The underlying histogram highlights class-

imbalanced dataset rationale.

Figure 5: Frequency of spare part target values after data

cleaning.

3.4 Modelling

Spare part prediction business scenario is a kind of

supervised learning due to the existence of a target

class,

SP_RIND, and the major objective of this

scenario is to seek significant drivers and patterns

highlighting the underlying phenomenon. According

to data dictionary, almost all attributes at final dataset

are categorical with a wide value range except the

derived attributes, e.g. product age and product stock

age. Therefore, we proposed a hybrid approach that

combines C4.5 and Apriori algorithms with weighted

kNN adaptations for the underlying class-imbalanced

mixed type final dataset.

3.4.1 C4.5

C4.5 adapts a greedy and nonbacktracking approach

in which decision trees are constructed as the

classifier in a top-down recursive divide-and-conquer

fashion

(Kantardzic, 2011). The corresponding

attribute selection method specifies a heuristic

procedure for selecting the attribute that best

discriminates the given tuples according to class.

In the context of spare part prediction scenario,

C4.5 is applied by using R (R package:

rpart) with

information gain attribute selection and min_split

parameter is set as 50. While 42.5% accuracy

performance of C4.5 suggests a ground truth for the

candidate algorithms, it majorly proposes the

significance weight of the attributes at determination

of spare parts as shown in Figure 6. Although several

similarity measures, such as the Jaccard coefficient

overlap

(Pang-Ning et al., 2006), cosine similarity (Liu

et al., 2014) and Goodall similarity (Boriah, 2008) can

be used with categorical data, they are usually general

as similarities at continuous data and ignores the

information hiding in the co-occurrence with the

target class. Hence, significance weight obtained by

C4.5 are used as inter-coupling similarity weights

(interDim_weight) at kNN adaptations as given in

Section 3.4.3.

Figure 6: Radar graph for significance weight of attributes

according to C4.5. Respectively, product hierarchy8 and

symptom code are the main determinants at spare part

prediction.

3.4.2 Apriori

As stated in Section 3.2, Apriori fundamentally

explores significant association and correlation rules

among spare part consumptions. The underlying

algorithm also generates

IF/THEN typed causality

rules for predicting target class without presence of a

classifier. Respectively, Apriori is applied with

min_support > 0.01 condition for the final dataset and

approximately 2710 causality rules are generated.

Table 1 exemplifies some generated Apriori causality

rules.

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Table 1: Sample IF/THEN typed causality rules generated

by Apriori.

Rules with min_lift > 1.0 property are validated

according to 66 test scenarios, which are configured

by different incident year, spare part groups (

ALL,

TOP3 and TOP6) and validation methods (i.e. hold-out

and k-fold cross-validation with k = 3, 5, 10). While

ALL year group has an average accuracy of 52%, the

accuracy for year 2017 peaks at 55.7%. The lowest

average accuracy of 45.3% is obtained at year 2015.

Moreover, a significant correlation between rule

precision and confidence is observed as shown in

Figures 7. However, rules with confidence values

between 0.4 and 0.6 seriously result in false

predictions (FP-false positive) with an average

precision of 25.27%.

Figure 7: Rule analysis (confidence vs average rule

precision).

According to Table 2, min_confidence = 0.7 is

designated as the confidence threshold for filtering

relatively weak causality rules. Figure 8 emphasizes

the average precision gap between the rule groups.

Table 2: Confidence threshold determination.

3.4.3 kNN and Adaptations

kNN classification is based on online learning scheme

by analogy; that is by comparing a given test instance

with training tuples at knowledge repository that are

similar to it

(Kantardzic, 2011). The training tuples are

Figure 8: Average precision values per rule grouping.

represented in a n-dimensional pattern space. When

given an unknown tuple, a kNN classifier searches the

pattern space for the k training tuples that are closest

to the unknown instance. Closeness is defined in

terms of a distance function such as Euclidean

distance. Typically, the values of each attribute

should be normalized before distance calculation. But

the distance calculation for categorical attributes and

relative distance within the value range of these

attributes are two major issues emerged at spare part

prediction scenario. As the first adaptation to kNN,

the underlying closeness measurement is converted

into a similarity measurement,

simScr(ins

,ins

), as

shown in Equation 1.

simScr

൫

ins

,ins

൯

෍

interDim_weight

dim

intraDim_weight

൫

ins

.dim,ins

.dim

൯

dim

(1)

In Equation 1, interDim_weight is the

normalization weight assigned for each significant

attribute (dim) and the significance weight obtained

at C4.5 is used for normalizing categorical attributes

to adapt the inter-dimension similarity.

intraDim_weight factor holds the similarity degree of

different level of the corresponding hierarchical

(ordinal) attributes. While these similarity degrees are

determined by domain experts, nominal difference at

the numeric attribute values of instances, ins

and ins

is used as intraDim_weight. In the context of

similarity measurement, we propose two kNN

adaptations: instance based kNN (

IkNN) and average

kNN (AkNN).

At IkNN adaptation, the similarity between new

fault incident and neighboring objects in the final

dataset is measured by Equation 1. Then the nearest

neighboring data points according to the similarity

values are determined by neigh_limit. This argument

is a percentile limit that preserves the closest training

objects in a spherical-like region and its value is

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System

223

parametrized at [0.2%, 1.0%] interval. Then as shown

in Figure 9, count, total and average similarity values

per spare part target class value are summarized

within the closest neighboring objects. At this point,

IkNN has two variations:

 Instance based kNN with Count (

IkNNwC).

Common spare parts at prediction result list are

determined by count value. By doing this, it is

aimed to give less importance to objects that are

more likely to represent a different class. In other

words, the idea is to penalize rare instances and

make the classifier more robust to the outliers.

 Instance based kNN with Average Similarity

Value (

IkNNwAS). Common spare parts at

prediction result list are determined by average

similarity value. This variation aims to balance

the discriminative power of an outlier object,

since it could be relevant to classify other outlier

object. It also allows to better modeling class-

imbalanced dataset by giving more chance to

objects less represented.

Figure 9: A sample Instance based kNN (IkNN) use-case

for a new fault incident.

Finally, spare part prediction result list is finalized

according to k-limit argument as follows:

 In the case of k-limit = n, i.e. n = 1, 2…, the

topmost n spare part values according to count or

average similarity value rank are returned as

prediction result list. The maximal value for n is

limited as 2 at this business scenario.

 In the case of dynamic k, i.e. k-limit = DK and

DK in [0, 1] interval, the standard deviation

(stdDev) of the corresponding value (i.e. count or

average similarity) is calculated. If the difference

between two consecutive spare part target values

is greater than DK×stdDev, then prediction result

list is returned as the combination of all checked

spare part values. Otherwise, it is continued to

check the following lines at summarization list.

Table 3 exemplifies the k-limit application at

IkNN

variation.

Table 3: k-limit application for IkNN variations. Especially

larger DK values with lower standard deviation may

weaken the capability at selective prediction. Hence, longer

prediction result list results in an accuracy decrease.

Respectively,

AkNN is similar to IkNN adaptation

except neighbor preservation such that, neigh_limit

argument is not applied at

AkNN. Otherwise,

summarization list is formed by traversing all training

tuples at the final dataset. Therefore,

AkNN is

relatively more time-consuming and rather less

capable at pinpointing minor (or outlier) objects at n-

dimensional space.

4 EXPERIMENTAL RESULTS

Major outcomes of evaluation and deployment phases

at CRISP-DM life cycle are presented in this section.

4.1 Evaluation

As stated in Section 3.4.2, Apriori causality rules with

confidence between 0.4 and 0.6 tend to make

erroneous predictions such that, they have an average

precision of 25.27%. Therefore, min_confidence =

0.7 is designated as confidence threshold to eliminate

these weak rules. Respectively, we propose the

following hybrid classification algorithm:

 Initially, it is attempted to predict new fault

incident by relatively confident Apriori causality

rules.

 In the case of unpredicting by Apriori, kNN

adaptations are applied to classify the

corresponding incident by an online learning

schema.

Hence, 56 test scenarios are configured by varying

incident year, spare part groups, kNN variations and

arguments (neigh_limit and k-limit) and validation

methods. As shown in Table 4, while pure Apriori

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casuality rules have an average accuracy of 54.02%,

the combination of Apriori with IkNN adaptation

improves average accuracy towards 77.95% level.

This metric is approximately 85.2% for

TOP3 and

79.24% for TOP6 spare part group.

Table 4: Average accuracy values per hybrid classification

algorithms.

According to the runtime analysis given in Figure 10,

while Apriori causality rules within [0.78, 0.82]

precision interval are intensively used,

IkNNwC

variation has a better prediction performance.

Although increments at dynamic k (DK) argument has

a positive effect at the recall values of

IkNNwC

variation, the inverse effect is valid for AkNN and

IkNNwAS variations. This is due to the fact that, while

count-based adaptation at kNN is seemingly more

robust to the changes made at the extents of

neighborhood region, the selective prediction

capabilities of average score-based adaptations are

more vulnerable to these changes. Hence, the

discriminative power of minor class is lost.

Figure 10: Runtime analysis for kNN adaptations.

Table 5: Average accuracy per spare part group. Average

accuracy of proposed approach is improved by the

increments at the confidence threshold.

As the next iteration, the confidence threshold

determined in Section 3.4.2 is incremented linearly

within [0.7, 1.0] interval. As a result, the combination

of Apriori rules (with min_confidence = 1.0) with

IkNNwC variation (with k-limit = 2 and neigh_limit =

0.2% arguments) reaches to an average accuracy of

80.68% as shown in Table 5.

When recall values per spare part are separately

analyzed, spare part

303113250 (with a frequency of

13.01% as shown in Figure 5) has a significant

increase of 7.8% at its recall values as shown in

Figure 11. Potentially, erroneous causality rules with

consequent equal to

303113250 are intensively handed

over by the predictions made by

IkNNwC variation

and this online learning schema is relatively more

accurate. Similar mechanism is valid for spare part

303113320 with the highest frequency given in Figure

5. A 2.8% increase at the recall values of the

corresponding spare part causes a significant

increasing-return effect on accuracy as shown in

Table 5.

Figure 11: Recall values for TOP6 spare part group.

4.2 Deployment

Due to the results obtained at evaluation phase, the

best runtime configuration is designated as Apriori

causality rules (with min_confidence = 1.0) with

IkNNwC variation (with k-limit = 2 and neigh_limit =

0.2% arguments). This hybrid classification model is

implemented as a custom function at SAP BW system

as shown in Figure 12. In addition to spare part

prediction, the underlying function recommends

potential concomitant spare part consumptions. These

associated consumptions are based on the association

rules generated by alternative data exploration

procedure stated in Section 3.2.2.

According to performance measurement, average

prediction duration of a single fault incident is

approximately 7.79 second (i.e. remote function

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System

225

connection (RFC) time between SAP CRM and BW

systems is excluded). Since mean arrival time

between two consecutive fault incidents is

approximately 36.5 second, it is technically feasible

to perform a real-time spare part prediction.

Figure 12: View of spare part prediction result list. While

odd numbered lines at PRED_RESULT prediction result

list inform spare part predictions, even lines indicate

concomitant spare part consumptions. HZMBSLK is the

unique identifier for the corresponding fault incident.

5 CONCLUSIONS

This paper proposes a hybrid classification algorithm

for the underlying spare part prediction scenario such

that, while Apriori is adapted to handle data anomalies

and redundancies emerged at data exploration,

significance weights obtained at C4.5 incorporates the

inter-dimension similarity at interpreting the

neighborhood among fault instances. Finally, two

adaptations of weighted kNN are applied:

IkNNwC

gives more importance to major instances that are more

likely to represent a dominant class in neighborhood

region of feature space,

IkNNwAS aims to balance the

discriminative power of minor class.

According to experimental results, proposed

hybrid classification algorithm doubles the human-

level performance at spare part prediction, which is

approximately 40% accuracy. This performance

implies a 50% decrease at the average number of

customer visits per fault incident. Hence a significant

cutting at especially sales and distribution costs is

expected by the effect of spare part prediction model.

As future work, we plan to extend the corresponding

modeling to other product groups.

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