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fahrbahn

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fortbewegung -

E-Grocery in Terms of Sustainability Simulating the Environmental Impact of Grocery Shopping for an Urban Area in Hanover

1 Source:https://pixabay.com/de/photos/verkehr scm-lab USEfUL Hanover The research project USEfUL

„Untersuchungs-, simulations- und Evaluations-Tool für Urbane Logistik“

Goal: “Contribution for a sustainable city and transport planning”

Hannover(2018b)

Landeshauptstadt Hochschule Hannover (2018c) Hochschule Hannover

2 FKZ 03SF0547 scm-lab USEfUL Hanover Our Hanover

• Population: 535.000 • Districts: 51 • Households: 296.000

• Surface: 204,14 km2

Prinz(2018)

Cf.

Landeshauptstadt Hannover(2017c): Statistische Berichte der Landeshauptstadt Hannover. Statistische Profile der der Profile Statistische Hannover. Landeshauptstadt der Berichte Statistische Hannover(2017c): Landeshauptstadt Stadtteile und Stadtbezirke 2017, Hannover: Landeshauptstadt Hannover. Hannover. Landeshauptstadt Hannover: 2017, StadtteileStadtbezirke und Wiki(2018); Cf.

3 scm-lab CITIES ARE RUNNING OUT OF SPACE

Our Hanover

-

Aufschlag

- Porto

Traffic (loads, jams): -

400811/

mit

-

-

ku enftig

-

Haustuer

b esch%C3%A4ftigt

-

-

zur

- menschen

Emissions (CO2, NOx etc.): -

welt/wirtschaft/Paketlief erung -

Quality of life:

https://www.shz.de/deutschland id22868857.html https://pixabay.com/de/photos/fu %C3%9Fg%C3%A4nger

4 scm-lab RESEARCH QUESTION

The research

-

urban

- city

E-Grocery in Germany -

mega

-

14

-

1

- illustration/techno

How doesthe area type - QBzcY9xrL6M10ucilQ “ E-Grocery Simulation - affect the utilityof e-

grocery in terms of traffic 746625394?src=0b4

volume and emission - technology

output? Comparison & Evaluation -

Source:https://www.shutterstock.com/de/image futuristic

5 04 scm-lab RESEARCH APPROACH The research

Framework:

Examination area: Hanover Research scope: „Mitte“, „List“, „Oststadt“ and Choice of software: Traffic/Emissions „Groß-Buchholz“ AnyLogic

Simulation model Emission model Impact Transfer Compare Kilometrage Emission factors Kilometrage, CO, CO2, N2O, NOx

Literature review

6 scm-lab RESEARCH FRAME Basis modell districts Groß-Buchholz • 4 district • 8700 households • 84 + 34 supermarkets List • 1 food fulfillment center

Mitte Oststadt

7 scm-lab RESEARCH FRAME

Area types & classification

1767540/

-

lights -

City Area Residential Area Industrial Area Mixed Area city -

• Trade and industry • Predominantly residential • Exclusively businesses on • Mix of all the skyscraper • Central facilities (economy, buildings large Properties above - administration and culture) • Often single and terraced • Differentiation between • Large parking deficits houses tertiary sector (not • Closed construction disturbing) and secondary sector (disturbing)

Mitte Reference: Reference: 21 Groß-Buchholz Source: https://pixabay.com/photos/dubai Oststadt List

8 scm-lab EGROCERY MODEL - Video -

9 scm-lab Simulation With AnyLogic…

• GIS map (Openstreetmap) • Incl. Point-Point navigation • Based on JAVA • Model building blocks + individual development

E-Grocery

• (around 25min / 100 runs)* (2016) Anylogic Traditional shopping • (around 120min / 100 runs)* 10 * CPU Simulation (Notebook DELL Precision 5530 // Intel I9 (Gen8) / 32GB RAM) scm-lab EGROCERY MODEL Simulation model scheme

Simulation LK01 – E-Grocery Driving Distances • Driving behaviour Stationary retail • Purchasing behaviour • Individual routing • Utilization rates

• Random interference factors Icons, [online] https://icons8.de/ [21.08.2018]. https://icons8.de/ [online] Icons, • Temperature control zones - • Delivery sequences • Driving speed Driving Distances • Loading and unloading times E-Grocery

• … Flat kostenlose 78,700 Icons8(2018):

11 scm-lab EGROCERY MODEL Input parameters

• 84 stores in pilot area • 1.604 out of 8700 households • 34 discounters in adjoiningarea • 3%/10%/20% E-Grocery utilization • Variable purchasing times • 42% share of bulk shopping • 6 days/per week • 51% shopping frequency • 56% car possession rate

• FFC in Langenhagen • Capacity: 1 – 3 loads Icons, [online] https://icons8.de/ [21.08.2018]. https://icons8.de/ [online] Icons, • 2h time-window delivery • Shopping duration: 25min - • 3 tours per day • Store selection based on distances

• 6 delivery days per week and shopping purpose

17

- 78,700 kostenlose Flat kostenlose 78,700 • 18% simulation cope • Capacity/vehicle: 18 orders

• 1.000 simulation runs per scenario • Loading time/order: 2 minutes

References:1 Icons8(2018): • Optimized routing • Parking duration: 5 minutes • Driving speed: 25/30 km/h • Unloadingtime/order: 10 minutes • Navigation: Shortest route

12 scm-lab EGROCERY MODEL Shopping behavior mechanism

Shopping decision Do I take the car? Yes CO2

No [21.08.2018]. https://icons8.de/ [online] Icons, -

Shopping decision

78,700 kostenlose Flat kostenlose 78,700

Reference:18 Icons8(2018):

In E-Grocery, shopping activities of population fractions usually not employing a vehicle for shopping result in additional distances and emissions. 13 scm-lab EGROCERY MODEL Process flow – Stationary retail

14 scm-lab 4.95% purchases 35.99% 38.54% 18.27% 2.24% EGROCERY MODEL bulk purchases 13.70% 27.09% 35.04% 15.35% 8.82% Supermarket range selection 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 0-0,5 km 0,5-1 km 1-3 km 3-5 km >5 km 1. Random demand 2. Selection of stores in distance area

3. House takes random Icons, [online] https://icons8.de/ [21.08.2018]. [21.08.2018]. https://icons8.de/ [online] Icons, shop in distance area - 4. Car determines

distances Icons8(2018): 78,700 kostenlose Flat kostenlose 78,700 Icons8(2018):

15 scm-lab EGROCERY MODEL Process flow – E-Grocery

16 scm-lab EGROCERY MODEL Truck navigation

1. Random orders 2. Sort orders by district 3. Create tours

4. Delivery route [21.08.2018]. https://icons8.de/ [online] Icons, - calculation (next neighbor t.w.) 5. Truck determines

distances Flat kostenlose 78,700 Icons8(2018):

17 scm-lab RESULTS Kilometres driven by transporter

Truck (km)

Orders: 180 400 Trucks: 10 350

300

250

200

150

100

50

0 Mitte List Oststadt Groß-Buchholz In the city (km) 230 216 196 289 Out of the the city (km) 105 75 90 100 18 scm-lab RESULTS Simulated scenarios

3% 10% 20%

Icons, [online] https://icons8.de/ [21.08.2018]. https://icons8.de/ [online] Icons, -

Scenario 1 Scenario 2 Scenario 3 78,700 kostenlose Flat kostenlose 78,700 3 % E-Grocery 10 % E-Grocery 20% E-Grocery

utilization rate utilization rate utilization rate Icons8(2018):

19 scm-lab RESULTS Total distances in kilometers

Hanover “Mitte” Hanover “List”

500 1200 400 1000 300 800 600 200 400 100 200 0 0 3% 10% 20% 3% 10% 20% Transporter (km) 107 184 312 Transporter (km) 123 292 403 Car (km) 69 230 473 Car (km) 143 497 1027

Hanover “Oststadt” Hanover “Groß-Buchholz”

400 1500 300 1000 200 100 500 0 0 3% 10% 20% 3% 10% 20% Transporter (km) 88 125 214 Transporter (km) 138 324 493 20 Car (km) 42 156 311 Car (km) 195 658 1256 scm-lab RESULTS Emission Model

퐸푖푗 = ෍ ቀ푁푗,푘 × 푀푗,푘 × 퐸퐹푖,푗,푘൰ Truck 푘 Car distances distances

Nj,k - Number of vehicles in a nation’s fleet of Structural category j and technology k vehicle data

Mj,k - Average annual distance driven per vehicle

of category j and technology k in km per vehicle Reference:20 1; EFi,j,k - Technology-specific emission factor of Emission output values: CO, pollutant i for vehicle category j CO2, N2O, NH3, NOx

21 scm-lab RESULTS Total Emissions: Carbon Dioxide (CO2)

Hanover “Mitte” Hanover “List”

100000 250000 80000 200000 60000 150000 40000 100000 20000 50000 0 0 3% 10% 20% 3% 10% 20% Transporter (g) 27127 46901 79098 Transporter (g) 31183 74027 102422 Car (g) 13353 44511 91538 Car (g) 34805 121207 249959

Hanover “Oststadt” Hanover “Groß-Buchholz”

80000 250000 60000 200000 40000 150000 100000 20000 50000 0 0 3% 10% 20% 3% 10% 20% Transporter (g) 22310 31690 54507 Transporter (g) 35239 82140 124985 22 Car (g) 8128 30190 60380 Car (g) 37577 126797 242209 scm-lab RESULTS Total Emissions: Nitrogen Oxide (NOx)

Hanover “Mitte” Hanover “List”

300 400 250 200 300 150 200 100 50 100 0 0 3% 10% 20% 3% 10% 20% Transporter (g) 89 154 259 Transporter (g) 102 243 336 Car (g) 15 49 101 Car (g) 31 109 225

Hanover “Oststadt” Hanover “Groß-Buchholz”

200 500 150 400 100 300 200 50 100 0 0 3% 10% 20% 3% 10% 20% Transporter (g) 73 104 179 Transporter (g) 116 269 410 23 Car (g) 9 34 67 Car (g) 42 142 272 scm-lab RESULTS Total distances scenario 20% Mitte City Area

Mixed Area List Oststadt

Residential Area

Groß-Buchholz

Car (km) Transporter (km)

24 Industrial Area scm-lab CONCLUSION The environmental impact of E-Grocery

40%

Future research Emission saving potential Uniform, transferable model • Extension of simulation area The potential to reduce emissions by Our simulation model offers a • Route combinations means of E-Grocery heavily depends sophisticated framework for analyzing • Sensitivity analysis regarding FFC location on the utilization rate and the area and assessing the impact of E-Grocery • Cost analysis for individual fulfillment type. Residential and industrial areas in different contexts and scenarios. The elements show the most improvement in model is easily transferable to other • Extension of the emission model kilometers traveled and emitted districts as well as cities and can be (additional emission factors) emissions, especially CO and CO2 used to identify an ideal set-up for • Crowd-purchasing concept (about 40% reduction). saving emissions by leveraging on the • Impact of vehicle electrification advantages of E-Grocery.

27 scm-lab Discussion

Maik Trott M.Sc. E-Mail: [email protected]

Marvin auf der Landwehr M.Sc. E-Mail: [email protected] FKZ 03SF0547

Prof. Dr. Christoph von Viebahn Hochschule Hannover Faculty IV E-Mail: [email protected] Business Informatics Ricklinger Stadtweg 120 D-30459 Hannover 28 scm-lab 11. Reutterer, T., & Teller, C. (2009). Store format choice and shopping trip types. International Journal of Retail & Distribution Management, 37(8), 695–710. References https://doi.org/10.1108/09590550910966196 12. Papastefanou, G., & Zajchowski, D. (2016). Zeit fürs Einkaufen: Sozialer Wandel der Zeitverwendung für Einkaufsaktivitäten 1990-2012. gesis. Retrieved from 1. Landeshauptstadt Hannover (Ed.): Statistische Berichte der Landeshauptstadt Hannover: Strukturdaten der Stadtteile und Stadtbezirke 2018. Hannover: 2018. https://www.gesis.org/fileadmin/upload/forschung/publikationen/gesis_reihen/gesis_papers/201 Available online https://www.hannover.de/Leben-in-der-Region- 6/GESIS-Papers_2016-14.pdf Hannover/Politik/Wahlen-Statistik/Statistikstellen-von-Stadt-und- Region/Statistikstelle-der-Landeshauptstadt- Hannover/Strukturdaten-der- 13. Adlwarth, W., & Kecskes, R. (2013). Consumer Index: Total Grocery 08 | 2013. Retrieved from Stadtteile-und-Stadtbezirke. https://www.gfk- verein.org/sites/default/files/medien/1/dokumente/gfk_consumer_index_082013.pdf 2. Delfmann, W., Albers, S., Müßig, R., Becker, F., Harung, F. K., Schöneseiffen, H., ... & Kukwa, C. (2011). Concepts, challenges and market potential for online food retailing 14. Lademann, R. P. (2007). Zum Einfluss von Verkaufsfläche und Standort auf die in Germany (No. 108). Working Paper, Department of Business Policy and Logistics, Einkaufswahrscheinlichkeit. In M. Schuckel & W. Toporowski (Eds.), Theoretische Fundierung und University of Cologne. praktische Relevanz der Handelsforschung (pp. 143–162). Wiesbaden: DUV. 15. van der Laan, J. (2017). Nine Battles of Online Grocery. Retail Economics. Retrieved from 3. Plachetta, S. & Röttig, B. (2012). Ein Geschäft auf allen Kanälen. Lebensmittel Praxis 2012, Issue 8, p. 32. https://retaileconomics.com/wp-content/uploads/2017/05/Ebook-Online-Food-Retail-PART-1.pdf 16. Hays, T., Keskinocak, P., & López, V. M. de. (2005). Strategies and Challenges of Internet Grocery 4. Warschun, M., Krüger, L., & Vogelpohl, N. (2013). Online-Food-Retailing: Ein Markt Retailing Logistics. In P. M. Pardalos, D. W. Hearn, J. Geunes, E. Akçali, H. E. Romeijn, & Z.-J. M. Shen im Aufschwung. A.T. Kearney. Retrieved from https://www.atkearney.de/documents/856314/3014702/BIP+Online-Food- (Eds.), Applied Optimization. Applications of Supply Chain Management and E-Commerce Research Retailing+Ein+Markt+im+Aufschwung.pdf/2cfae910-1c7e-4ccb-98fc-730e6ae10ff5 (Vol. 92, pp. 217–252). Boston, MA: Springer US. https://doi.org/10.1007/0-387-23392-X_8 17. Bouchain, J., Große, S., Großmann, A., Runge, F., & Heinrich, C. (2017). Städte Ranking zur 5. Recker, W. W., & Kostyniuk, L. P. (1978). Factors influencing destination choice for the urban grocery shopping trip. Transportation, 7(1), 19–33. nachhaltigen Mobilität. Greenpeace e.V. Retrieved from https://doi.org/10.1007/BF00148369 https://www.greenpeace.de/sites/www.greenpeace.de/files/publications/20170322_greenpeace_ mobilitaetsranking_staedte.pdf 6. Janssen, L. (2018). Filiallogistik im stationären LEH. In L. Janssen (Ed.), Abfallreduktion im Lebensmitteleinzelhandel (pp. 13–42). Wiesbaden: Springer Fachmedien 18. Nobis, C.; Kuhnimhof, T.: Mobilität in Deutschland - MID Ergebnisbericht: Eine Studie von infas, Wiesbaden. https://doi.org/10.1007/978-3-658-23012-8_2 DLR, IVT und infas 360 im Auftrag des BMVI, (FE-NR.70.904/15) Bonn. Online aivailable: http://www.mobilitaet-in-deutschland.de. 7. Google (Eds.) (2019): Google Maps. Map: Mitte, Hannover. Retrieved from http://maps.google.com/maps 19. European Environment Agency: EMEP/EEA air pollutant emission inventory guidebook 2016: Technical guidance to prepare national emission inventories. Luxembourg: Publications Office of 8. Bodkin, C. D., & Lord, J. D. (1997). Attraction of power shopping centres. The the European Union 2016. International Review of Retail, Distribution and Consumer Research, 7, 93–108. https://doi.org/10.1080/095939697343058 20. van Loon, P.; Deketele, L.; Dewaele, J.; McKinnon, A.; Rutherford, C.: A comparative analysis of carbon emissions from online retailing of fast moving consumer goods. Journal of Cleaner 9. nielsen (2011). Shopping & Saving Strategies Around the World: A Nielsen Report. Production 106 (2015), pp. 478–486. Retrieved from https://www.nielsen.com/content/dam/corporate/us/en/reports- downloads/2011-Reports/Nielsen-Global-Shopping-Saving-Report-October-2011.pdf 21. Schäfer, P.K.; Höhl, S.; Schocke, O. (2017). Analyse und Empfehlungen für Belieferungsstrategien der KEP-Branche im innerstädtischen Bereich. Frankfurt am Main: Frankfurt University of Applied 10. Koupon Media (2018). 2018 State of the Industry: Mobile Offers in Convenience Sciences. Stores. Retrieved from https://go.kouponmedia.com/2018-state-of-the-industry- mobile-offers-in-convenience-stores. 22. Hochschule Hannover (2017b): Quantitativer Bewertungsprozess der Stadtteile zur Findung der geeignetsten Untersuchungsräume in Hannover nach VDI 2225, internes Dokument.

29 scm-lab ANNEX Discussion

30 scm-lab