Analysis of Operational Data for Expertise Aware Staffing View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Renuka Sindhgatta , Gaargi Banerjee Dasgupta , Aditya Ghose

ABSTRACT

Knowledge intensive business services such as IT Services, rely on the expertise of the knowledge workers for performing the activities involved in the delivery of services. The activities performed could range from performing simple, repetitive tasks to resolving more complex situations. The expertise of the task force can also vary from novices who cost less to advanced skill workers and experts who are more expensive. Staffing of service systems relies largely on the assumptions underlying the operational productivity of the workers. Research independently points to the impact of factors such as complexity of work and expertise of the worker on worker productivity. In this paper, we examine the impact of complexity of work, priority or importance of work and expertise of the worker together, on the operational productivity of the worker. For our empirical analysis, we use the data from real-life engagement in the IT service management domain. Our finding, on the basis of the data indicates, not surprisingly, that experts are more suitable for complex or high priority work with strict service levels. In the same setting, when experts are given simpler tasks of lower priority, they tend to not perform better than their less experienced counterparts. The operational productivity measure of experts and novices is further used as an input to a discrete event simulation based optimization framework that model real-life service system to arrive at an optimal staffing. Our work demonstrates that data driven techniques, similar to the one presented here is useful for making more accurate staffing decisions by understanding worker efficiency derived from the analysis of operational data. More... »

PAGES

317-332

Book

TITLE

Business Process Management

ISBN

978-3-319-10171-2
978-3-319-10172-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-10172-9_20

DOI

http://dx.doi.org/10.1007/978-3-319-10172-9_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1014626901


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Wollongong, New South Wales, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1007.6", 
          "name": [
            "IBM India-Research, Bangalore, India", 
            "University of Wollongong, New South Wales, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sindhgatta", 
        "givenName": "Renuka", 
        "id": "sg:person.015651720511.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015651720511.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "IBM India-Research, Bangalore, India", 
          "id": "http://www.grid.ac/institutes/grid.435338.a", 
          "name": [
            "IBM India-Research, Bangalore, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dasgupta", 
        "givenName": "Gaargi Banerjee", 
        "id": "sg:person.011623557301.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011623557301.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Wollongong, New South Wales, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1007.6", 
          "name": [
            "University of Wollongong, New South Wales, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ghose", 
        "givenName": "Aditya", 
        "id": "sg:person.015573517335.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015573517335.70"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2014", 
    "datePublishedReg": "2014-01-01", 
    "description": "Knowledge intensive business services such as IT Services, rely on the expertise of the knowledge workers for performing the activities involved in the delivery of services. The activities performed could range from performing simple, repetitive tasks to resolving more complex situations. The expertise of the task force can also vary from novices who cost less to advanced skill workers and experts who are more expensive. Staffing of service systems relies largely on the assumptions underlying the operational productivity of the workers. Research independently points to the impact of factors such as complexity of work and expertise of the worker on worker productivity. In this paper, we examine the impact of complexity of work, priority or importance of work and expertise of the worker together, on the operational productivity of the worker. For our empirical analysis, we use the data from real-life engagement in the IT service management domain. Our finding, on the basis of the data indicates, not surprisingly, that experts are more suitable for complex or high priority work with strict service levels. In the same setting, when experts are given simpler tasks of lower priority, they tend to not perform better than their less experienced counterparts. The operational productivity measure of experts and novices is further used as an input to a discrete event simulation based optimization framework that model real-life service system to arrive at an optimal staffing. Our work demonstrates that data driven techniques, similar to the one presented here is useful for making more accurate staffing decisions by understanding worker efficiency derived from the analysis of operational data.", 
    "editor": [
      {
        "familyName": "Sadiq", 
        "givenName": "Shazia", 
        "type": "Person"
      }, 
      {
        "familyName": "Soffer", 
        "givenName": "Pnina", 
        "type": "Person"
      }, 
      {
        "familyName": "V\u00f6lzer", 
        "givenName": "Hagen", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-10172-9_20", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-10171-2", 
        "978-3-319-10172-9"
      ], 
      "name": "Business Process Management", 
      "type": "Book"
    }, 
    "keywords": [
      "service management domain", 
      "service system", 
      "high priority work", 
      "discrete event simulation", 
      "operational data", 
      "real-life engagements", 
      "operational productivity", 
      "real-life service systems", 
      "IT services", 
      "management domain", 
      "event simulation", 
      "impact of complexity", 
      "repetitive tasks", 
      "business services", 
      "optimization framework", 
      "knowledge workers", 
      "service level", 
      "simple task", 
      "priority work", 
      "complex situations", 
      "services", 
      "complexity of work", 
      "task", 
      "delivery of services", 
      "experts", 
      "complexity", 
      "knowledge-intensive business services", 
      "worker efficiency", 
      "staffing decisions", 
      "expertise", 
      "novices", 
      "system", 
      "work", 
      "low priority", 
      "optimal staffing", 
      "framework", 
      "data", 
      "same setting", 
      "input", 
      "impact of factors", 
      "worker productivity", 
      "productivity measures", 
      "decisions", 
      "domain", 
      "empirical analysis", 
      "priority", 
      "technique", 
      "simulations", 
      "Task Force", 
      "efficiency", 
      "productivity", 
      "situation", 
      "research", 
      "one", 
      "analysis", 
      "assumption", 
      "setting", 
      "staffing", 
      "basis", 
      "impact", 
      "data indicates", 
      "delivery", 
      "measures", 
      "engagement", 
      "workers", 
      "importance", 
      "skill workers", 
      "counterparts", 
      "experienced counterparts", 
      "levels", 
      "activity", 
      "factors", 
      "indicates", 
      "force", 
      "findings", 
      "importance of work", 
      "paper"
    ], 
    "name": "Analysis of Operational Data for Expertise Aware Staffing", 
    "pagination": "317-332", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1014626901"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-10172-9_20"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-10172-9_20", 
      "https://app.dimensions.ai/details/publication/pub.1014626901"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-12-01T06:46", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/chapter/chapter_128.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-10172-9_20"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10172-9_20'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10172-9_20'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10172-9_20'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10172-9_20'


 

This table displays all metadata directly associated to this object as RDF triples.

164 TRIPLES      22 PREDICATES      102 URIs      95 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-10172-9_20 schema:about anzsrc-for:08
2 anzsrc-for:0806
3 schema:author Nfb0ccc31c9fd4ee3a2116669665d9137
4 schema:datePublished 2014
5 schema:datePublishedReg 2014-01-01
6 schema:description Knowledge intensive business services such as IT Services, rely on the expertise of the knowledge workers for performing the activities involved in the delivery of services. The activities performed could range from performing simple, repetitive tasks to resolving more complex situations. The expertise of the task force can also vary from novices who cost less to advanced skill workers and experts who are more expensive. Staffing of service systems relies largely on the assumptions underlying the operational productivity of the workers. Research independently points to the impact of factors such as complexity of work and expertise of the worker on worker productivity. In this paper, we examine the impact of complexity of work, priority or importance of work and expertise of the worker together, on the operational productivity of the worker. For our empirical analysis, we use the data from real-life engagement in the IT service management domain. Our finding, on the basis of the data indicates, not surprisingly, that experts are more suitable for complex or high priority work with strict service levels. In the same setting, when experts are given simpler tasks of lower priority, they tend to not perform better than their less experienced counterparts. The operational productivity measure of experts and novices is further used as an input to a discrete event simulation based optimization framework that model real-life service system to arrive at an optimal staffing. Our work demonstrates that data driven techniques, similar to the one presented here is useful for making more accurate staffing decisions by understanding worker efficiency derived from the analysis of operational data.
7 schema:editor N7ebece3152304442a15784e3904e0157
8 schema:genre chapter
9 schema:isAccessibleForFree false
10 schema:isPartOf Nb7c4816880dc4a61ad9a31f1bc20987a
11 schema:keywords IT services
12 Task Force
13 activity
14 analysis
15 assumption
16 basis
17 business services
18 complex situations
19 complexity
20 complexity of work
21 counterparts
22 data
23 data indicates
24 decisions
25 delivery
26 delivery of services
27 discrete event simulation
28 domain
29 efficiency
30 empirical analysis
31 engagement
32 event simulation
33 experienced counterparts
34 expertise
35 experts
36 factors
37 findings
38 force
39 framework
40 high priority work
41 impact
42 impact of complexity
43 impact of factors
44 importance
45 importance of work
46 indicates
47 input
48 knowledge workers
49 knowledge-intensive business services
50 levels
51 low priority
52 management domain
53 measures
54 novices
55 one
56 operational data
57 operational productivity
58 optimal staffing
59 optimization framework
60 paper
61 priority
62 priority work
63 productivity
64 productivity measures
65 real-life engagements
66 real-life service systems
67 repetitive tasks
68 research
69 same setting
70 service level
71 service management domain
72 service system
73 services
74 setting
75 simple task
76 simulations
77 situation
78 skill workers
79 staffing
80 staffing decisions
81 system
82 task
83 technique
84 work
85 worker efficiency
86 worker productivity
87 workers
88 schema:name Analysis of Operational Data for Expertise Aware Staffing
89 schema:pagination 317-332
90 schema:productId N259d0e5241f645ddae6fc2f12123c59d
91 N51b3ebe6f9264c4cbeba7f43c6830207
92 schema:publisher N70a8ebc6c0b843cd8f5736d0d590d427
93 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014626901
94 https://doi.org/10.1007/978-3-319-10172-9_20
95 schema:sdDatePublished 2022-12-01T06:46
96 schema:sdLicense https://scigraph.springernature.com/explorer/license/
97 schema:sdPublisher N3f127f6a43d348439b4250a5c04e381c
98 schema:url https://doi.org/10.1007/978-3-319-10172-9_20
99 sgo:license sg:explorer/license/
100 sgo:sdDataset chapters
101 rdf:type schema:Chapter
102 N259d0e5241f645ddae6fc2f12123c59d schema:name dimensions_id
103 schema:value pub.1014626901
104 rdf:type schema:PropertyValue
105 N29ba6bd1476d4957b330e5955ccc792a rdf:first N46e2c18cd07c4ff7968f90a1d680b54f
106 rdf:rest N56283fc8fa4943638dec9aa01e0ca384
107 N2c0e188f8c5147e1ad66c212fd614bdd rdf:first sg:person.011623557301.33
108 rdf:rest N41769f48a5324175bbc083435471d422
109 N3f127f6a43d348439b4250a5c04e381c schema:name Springer Nature - SN SciGraph project
110 rdf:type schema:Organization
111 N41769f48a5324175bbc083435471d422 rdf:first sg:person.015573517335.70
112 rdf:rest rdf:nil
113 N46e2c18cd07c4ff7968f90a1d680b54f schema:familyName Soffer
114 schema:givenName Pnina
115 rdf:type schema:Person
116 N51b3ebe6f9264c4cbeba7f43c6830207 schema:name doi
117 schema:value 10.1007/978-3-319-10172-9_20
118 rdf:type schema:PropertyValue
119 N56283fc8fa4943638dec9aa01e0ca384 rdf:first Nd58e06eed6c34b3aa6c3c23cb5030bcd
120 rdf:rest rdf:nil
121 N70a8ebc6c0b843cd8f5736d0d590d427 schema:name Springer Nature
122 rdf:type schema:Organisation
123 N77276221f74b40fd8c576aeea12cf257 schema:familyName Sadiq
124 schema:givenName Shazia
125 rdf:type schema:Person
126 N7ebece3152304442a15784e3904e0157 rdf:first N77276221f74b40fd8c576aeea12cf257
127 rdf:rest N29ba6bd1476d4957b330e5955ccc792a
128 Nb7c4816880dc4a61ad9a31f1bc20987a schema:isbn 978-3-319-10171-2
129 978-3-319-10172-9
130 schema:name Business Process Management
131 rdf:type schema:Book
132 Nd58e06eed6c34b3aa6c3c23cb5030bcd schema:familyName Völzer
133 schema:givenName Hagen
134 rdf:type schema:Person
135 Nfb0ccc31c9fd4ee3a2116669665d9137 rdf:first sg:person.015651720511.55
136 rdf:rest N2c0e188f8c5147e1ad66c212fd614bdd
137 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
138 schema:name Information and Computing Sciences
139 rdf:type schema:DefinedTerm
140 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
141 schema:name Information Systems
142 rdf:type schema:DefinedTerm
143 sg:person.011623557301.33 schema:affiliation grid-institutes:grid.435338.a
144 schema:familyName Dasgupta
145 schema:givenName Gaargi Banerjee
146 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011623557301.33
147 rdf:type schema:Person
148 sg:person.015573517335.70 schema:affiliation grid-institutes:grid.1007.6
149 schema:familyName Ghose
150 schema:givenName Aditya
151 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015573517335.70
152 rdf:type schema:Person
153 sg:person.015651720511.55 schema:affiliation grid-institutes:grid.1007.6
154 schema:familyName Sindhgatta
155 schema:givenName Renuka
156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015651720511.55
157 rdf:type schema:Person
158 grid-institutes:grid.1007.6 schema:alternateName University of Wollongong, New South Wales, Australia
159 schema:name IBM India-Research, Bangalore, India
160 University of Wollongong, New South Wales, Australia
161 rdf:type schema:Organization
162 grid-institutes:grid.435338.a schema:alternateName IBM India-Research, Bangalore, India
163 schema:name IBM India-Research, Bangalore, India
164 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...