research portfolio harri lorentz, d.sc. marriott school of management, byu 18 november 2010 1
TRANSCRIPT
Research portfolio
Harri Lorentz, D.Sc.
Marriott School of Management, BYU
18 November 2010
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Presentation outline
• Research portfolio in brief– Past: selected publications– Present: submitted manuscripts, WIP– In planning, tbd
• In more detail: Geographic dispersion and supply chain performance – Empirical evidence from Finnish manufacturing
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Selected publications:
• Lorentz, H. – Ghauri, P.N. (2010) Demand supply network opportunity development processes in emerging markets: positioning for strategy realization in Russia, Industrial Marketing Management, Vol. 39, No. 2, 240-251.
• Lorentz, H. (2009) Contextual supply chain constraints in emerging markets – exploring the implications for foreign firms, Publications of Turku School of Economics, Series A-6, available at: http://info.tse.fi/julkaisut/vk/Ae6_2009.pdf .
• Lorentz, H. (2008) Collaboration in Finnish-Russian supply chains – effects on performance and the role of experience, Baltic Journal of Management, Vol. 3, No. 3, 246-265.
• Lorentz, H. (2008) Production Locations for the Internationalising Food Industry – Case Study from Russia, British Food Journal, Vol. 110, No. 3, 310-334.
• Lorentz, H. – Wong, C.Y. – Hilmola, O.-P. (2007) Emerging Distribution Systems in Central and Eastern Europe: Implications from Two Case Studies, International Journal of Physical Distribution & Logistics Management, Vol. 37, No. 8, 670-697.
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Submitted manuscripts (autumn 2010)
• Lorentz, H., Töyli, J., Hälinen, H.-M., Solakivi, T. & Ojala, L., Effects of geographic dispersion on supply chain performance, submitted to Journal of Operations Management.
• Lorentz, H., Solakivi, T., Töyli, J. & Ojala, L., Supply chain development priorities of manufacturing firms – empirical findings from a Finnish national survey, submitted to International Journal of Logistics Research and Applications.
• Lorentz, H. & Lounela, J., Retailer supply chain capability assessment in Russia, submitted to International Journal of Retail and Distribution Management.
• Hilmola, O.-P. & Lorentz, H., Warehousing in Northern Europe – Longitudinal Survey, submitted to Industrial Management & Data Systems.
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Work in Process
• Lorentz, H., Kittipanya-ngam, P. & Srai, J., Internationalising food supply chains: the impact of emerging market characteristics on supply networks
• Töyli, J., Solakivi, T., Lorentz, H., Ojala, L., Logistics and financial performance
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,10
Relative logistic performance
,60
VAR149
e1
,77
,64
VAR150
e2
,80
,43
VAR151
e3
,66
,44
VAR152
e4
,66
,42
VAR153
e5
,65
,54
VAR154
e6
,73
Strategic context
,54
VAR174
ce1
,73
,60
VAR175
ce2
,77
,71
VAR176
ce3
,84
,52
VAR177
ce4
,72,17
Intra-organisational coordination
,53
VAR178
ae1
,73
,59
VAR179
ae2
,77
,40
VAR180
ae3
,63
,62
VAR181
ae4
,79
,16
Inter-organisational coordination
,68
VAR182
ee1
,82
,50
VAR183
ee2
,71
,38
VAR184
ee3
,62
,57
VAR185
ee4
,75
,28
Performance evaluation
,67
VAR158
ep1
,82
,58
VAR159
ep2
,76
,40
VAR171
ep3
,63
,51
VAR172
ep4
,72
,38
VAR173
ep5
,62
,20
,12
,15efl
,04
financial performance
,77
EBITpercentage
ef1
,88
,39
ROA
ef2
,62
,92
Cash_Flow_per_Turnover
ef3
,96
,19
eef1
,53
,40
ep
ea
ee
,73
,41
Work in Process / in planning
• Further investigation of the SC geographic dispersion–performance –relationship: e.g. the increase in explanatory power by incorporating friction into the dispersion measure, and the role of moderating variables:
– Logistics significance– Performance monitoring– Internal integration– External integration– Information systems
• Determinants of SCM capability priorities: – Independents: SC geographic dispersion, firm size, sector,
manufacturing strategy, SC echelon, logistics significance, relative performance
– Dependents: 12 SCM competence areas
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A pet project…
• Lorentz, H. & Hilmola, O.-P., Supply chain confidence: conceptualisation and dynamics
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Personal development goals in research:
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• Aim for less articles with the word ”Russia” in the title
• Aim for emphasis change from descriptive to normative
• Aim for more research with modelling approach: – system dynamics, strategic business/SC models– discrete event simulation
Geographic dispersion and supply chain performance – Empirical evidence from Finnish manufacturing
Harri Lorentz
Juuso Töyli
Hanne-Mari Hälinen
Tomi Solakivi
Lauri Ojala
The authors wish to thank the Finnish Foundation for Economic Education (Liikesivistysrahasto) for significant financial support for this research (grant no. 29991).
BACKGROUND: Implications of internationalisation and global footprint on supply chains?
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Source: World Bank (2007 and 2010)
Source: Geringer, Beamish & daCosta (1989)
The research question:
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How does geographic dispersion of the firm’s supply chain impact supply chain performance at the firm level?
Construct no. 1 Construct no. 2
Research data
• Sub sample from the State of Logistics Finland 2009 – survey
• 109 large manufacturing companies operating in Finland (annual turnover over 50 million EUR)
• Sample covers over 80% of the total turnover of the Finnish manufacturing industry
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Construct No. 1: SC geographic dispersion
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Stock et al. (2000)
The geographic dispersion measures range between zero and unity, the former meaning the network is concentrated completely in one region, and the latter implying an evenly spread network in all six regions.
How did we measure SC geographic dispersion?
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Independent: Geographic dispersion
of sales - DISP calculated for % of sales from home, other EU (incl. Norway, Iceland, Switzerland), Russia, North and South America, Asia, and all other
SALESDISP
of production capacity
- DISP calculated for % of production capacity at home, other EU (incl. Norway, Iceland, Switzerland), Russia, North and South America, Asia, and all other
PRODDISP
of direct purchasing
- DISP calculated for % of purchases from home, other EU (incl. Norway, Iceland, Switzerland), Russia, North and South America, Asia, and all other
PURCHDISP
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10006
100Other%
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100Asia%
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100Ame%
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100Rus%
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100EU%
6
100Home%
1DISP
Based on Stock et al. (2000)
Construct No. 2: SC performance
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Dependent: Supply chain performance
Logistics costs
- Transportation costs (%-share of turnover) - Warehousing costs (%-share of turnover) - Inventory costs (%-share of turnover) - Logistics administration costs (%-share of turnover)
TRAN WARE INV ADMIN
Service performance
- Perfect order fulfilment (% of customer orders on time, at the right place, with correct documentation, in right quantity, and without damage)
POF
- Order fulfilment cycle time (average days from order to delivery)
OFCT
Asset utilisation - Inventory days of supply (average days material owned, from purchase to sale) - Cash-to-cash cycle time (in days; inventory days of supply + accounts receivable - accounts payable)
DOS CCC
Based on e.g. Töyli et al. (2008),
Gunasekaran et al. (2001), Stewart (1995)
Is there a relationship? Expected effects (hypotheses)
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Dependent -
Independent
Supply chain performance
Logistics costs Service performance Asset utilisation
Geo. dispersion TRAN WARE INV ADMIN POF OFCT DOS CCC SALESDISP + + + + - + + + PRODDISP - + + + + - + +
PURCHDISP + + + + - + + +
Based on e.g. Maister (1976), Prater et al. (2001), Chopra (2003), Choi & Krause (2006)…
N Mean Med. Std dev Skew. Kurt. Distribution
Independent SALESDISP 105 0.36 0.35 0.21 -0.18 -0.97 .. Independent PRODDISP 109 0.19 0.15 0.19 0.51 -0.96 .. Independent PURCHDISP 95 0.29 0.31 0.16 -0.03 -0.59 .. Dependent TRANS 103 7.64 5.00 8.08 4.79 34.36 gamma Dependent WARE 98 3.38 3.00 3.47 2.82 10.10 gamma Dependent INV 99 4.80 3.00 5.33 2.63 9.18 gamma Dependent ADMIN 95 1.77 1.00 1.85 3.37 19.37 gamma Dependent 100-POF 101 7.55 5.00 8.72 2.37 6.73 gamma Dependent OFCT 97 46.41 16.5 99.92 4.61 26.85 gamma Dependent DOS 95 52.47 35.0 56.22 3.12 12.33 gamma Dependent CCC 86 50.04 38.5 57.91 1.72 7.05 normal
Method selection
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We use generalised linear models (gamma as random component, and log as the link function) to investigate the relationships between independent and dependent variables, supported by Dodd et al. 2006 (except with CCC: normal distribution with identity as the link function)
Previous research has also shown the tendency of cost variables to high skewness and to cause complications in standard statistical analysis, such as in the OLS regression analysis (Dodd et al., 2006).
Research results
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Model (N)
Dependent Independents Omnibus test (Likelih. ratio χ2)
β Tests of model effects (Wald χ2)
95% CI
1 (75) WARE Intercept SALESDISP
3.764 (p=0.052) 1.055 0.737
45.914 (p=0.000) 3.900 (p=0.048)
0.750-1.360 0.006-1.468
2 (77) WARE Intercept PURCHDISP
8.518 (p=0.004) 0.719 1.810
12.096 (p=0.001) 8.861 (p=0.003)
0.314-1.123 0.618-3.002
3 (76) INV Intercept SALESDISP
12.037 (p=0.001) 1.079 1.494
40.240 (p=0.000) 13.116 (p=0.000)
0.745-1.412 0.686-2.303
4 (76) INV Intercept PRODDISP
6.715 (p=0.010) 1.380 1.318
108.766 (p=0.000) 6.736 (p=0.009)
1.121-1.639 0.323-2.313
5 (78) INV Intercept PURCHDISP
12.078 (p=0.001) 0.792 2.534
11.193 (p=0.001) 13.060 (p=0.000)
0.328-1.256 1.160-3.908
6 (65) ADMIN Intercept SALESDISP
10.978 (p=0.001) 0.455 1.020
12.491 (p=0.000) 12.043 (p=0.001)
0.203-0.708 0.444-1.597
7 (66) ADMIN Intercept PURCHDISP
16.791 (p=0.000) 0.088 2.191
0.248 (p=0.618) 19.328 (p=0.000)
-0.258-0.434 1.214-3.168
8 (83) 100-POF Intercept SALESDISP
8.132 (p=0.004) 1.589 1.308
73.349 (p=0.000) 8.788 (p=0.003)
1.225-1.952 0.443-2.173
9 (81) 100-POF Intercept PURCHDISP
13.929 (p=0.000) 1.256 2.492
32.667 (p=0.000) 15.016 (p=0.000)
0.825-1.686 1.232-3.753
10 (78) OFCT Intercept SALESDISP PRODDISP PURCHDISP
27.130 (p=0.000) 1.620 3.293 -1.884 2.898
24.242 (p=0.000) 17.509 (p=0.000) 3.761 (p=0.052) 6.752 (p=0.009)
0.975-2.264 1.750-4.835 -3.788-0.020 0.712-5.083
11 (75) DOS Intercept SALESDISP PURCHDISP
13.094 (p=0.001) 3.005 1.154 1.305
163.15 (p=0.000) 5.497 (p=0.019) 3.256 (p=0.071)
2.544-3.466 0.189-2.119 -0.113-2.723
12 (75) DOS Intercept PRODISP
4.020 (p=0.045) 3.650 1.081
739.976 (p=0.000) 4.016 (p=0.045)
3.387-3.913 0.024-2.138
13 (73) CCC Intercept SALESDISP PURCHDISP
12.541 (p=0.002) 3.707 54.723 82.904
0.76 (p=0.783) 3.100 (p=0.078) 3.418 (p=0.064)
-22.62-30.04 -6.191-115.6 -4.982-170.8
14 (73) CCC Intercept PRODDISP
4.406 (p=0.036) 34.749 74.688
16.263 (p=0.000) 4.542 (p=0.033)
17.861-51.637 6.000-143.375
Expected and observed effects of SC geographic dispersion on SC performance
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Dependent -
Independent
Supply chain performance
Logistics costs Service performance Asset utilisation
Geo. dispersion TRAN WARE INV ADMIN POF OFCT DOS CCC SALESDISP +/- +/+* +/+** +/+** -/-** +/+** +/+* +/+ PRODDISP -/- +/+ +/+** +/+ +/- -/- +/+* +/+*
PURCHDISP +/- +/+** +/+** +/+** -/-** +/+** +/+ +/+
Shaded cells indicate hypotheses that can neither be confirmed nor rejected due to the lack of statistically significant results, white cells indicate results with statistically significant results at minimum 0.1 level, while * implies significance at 0.05 level and ** significance at 0.01 level. Signs + or – indicate the direction of the expected and observed relationships (expected/observed).
Performance effects of unit increases in geographic dispersion variables
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Geo. dispersion from 0 to 1 in:
Performance effect (A) Median among companies with 0 dispersion* (B)
Performance effect in % (A/B*100%)
Purchasing Warehousing costs as % of turnover up by 1.81 1.0 181% Inventory costs as % of turnover up by 2.53 0.5 506% Administration costs as % of turnover up by 2.19 0.5 438% %-share of imperfect orders of total up by 2.49 1.0 249% Order fulfilment cycle time in days up by 2.90 15.5 19% Inventory days of supply in days up by 1.30
Cash-to-cash cycle time in days up by 82.90 15 13
9% 638%
Production Inventory costs as % of turnover up by 1.32 Order fulfilment cycle time in days down by -1.88 Inventory days of supply in days up by 1.08 Cash-to-cash cycle time in days up by 74.69
2 22.5 30 40
66% 8% 4%
187%
Sales Warehousing costs as % of turnover up by 0.74 Inventory costs as % of turnover up by 1.49
2 1.5
37% 99%
Administration costs as % of turnover up by 1.02 %-share of imperfect orders of total up by 1.31
1 4.5
102% 29%
Order fulfilment cycle time in days up by 3.29 2.0 165% Inventory days of supply in days up by 1.15 13.5 9% Cash-to-cash cycle time in days up by 54.72 13.5 405%
Concluding remarks
• Signs of the statistically significant relationships are as hypothesised
• This research provides explicit evidence on the dispersion-performance relationship
• Of the three geographic dispersion dimensions, purchasing and sales seem to have equally notable effect on firm level supply chain performance
• Production dispersion is the only independent that may balance detrimental performance effects with an improved level of service performance
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Implications
• In internationalising and global companies, the management of supply chain performance should be high on the agenda, especially in terms of logistics costs and cash-to-cash cycle time.
• Managers should be acutely aware of the possibly major performance implications of geographically dispersing supply chains, for example in the context of internationalisation of sales, and aim, when feasible, for consolidation in for example the supply base.
• Such development aims should naturally be balanced with the drive to geographically diversify sourcing as a risk management strategy.
• In this context, our research sheds light on the possible cost effects of the diversification strategy.
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