Improvement of your performance in the Hinterland

Transcription

1 Improvement of your performance in the Hinterland Use Big data to increase competitiveness Today s host: Kim Meijer Marketing Manager EMEA AIMMS

2 Partners for 12 years > Incorporated in 1997 > Provides innovative business solutions using AIMMS technology > Consultancy, project management & implementation > Key player in the field of transport & logistics > Incorporated in 1989 > Provides a flexible platform for Ab Ovo to develop and deploy custom apps that improve business performance > Delivers fast and secure web-based solutions

3 Topics We Will Cover Challenges in the Hinterland Synchro-modal planning: Customer Case Smart Barging: Live Demo

4 Meet the Presenters Walter Kusters BU Director Consultancy Ab Ovo Ard-Jan Cieremans Senior Consultant Ab Ovo Strong hinterland background and successful in enriching business models with big data In depth knowledge in smart capacity-planning using real time information Working for Ab Ovo for +5 years Working for Ab Ovo for +10 years

5 Challenges in the Hinterland

6 Current challenges in the Hinterland External factors Pre-planning Internal factors Sales rate Modality choice Carrier choice External factors execution

7 Observations Enormous amount of data available All execution- partners registrate relevant data Port Community- Systems in place Relative easy entrance to external data Exchange between bilateral partners goes well PCS is limited used Companies stick to use internal data Decision makers (planners) facing lack of time Decision makers don t oversee the integral complexity Need for business modelling & decision support Enabling to use external data for decisions Enabling fast decision making in complex environments

8 Business modelling and integral decision support Opening times, import- export matching Depot for carrier or not. New trades, changed loops, merged loops (inland- depots, demurrage- rules, customs) Costs, lead time and traffic jam. Limited time for unloading / loading container Changed or new terminals or even change of port Forecasted availability to pick up Needed date for loading / unloading Resulting in more or less lead time Business logic Time- tables, schedules, available capacity Delays, additional services, alternatives (inland terminals nearby, alternative deep- sea carriers) Optimise for: > Cost price > Service Scenario's Scenario's Planning Price Margin > Co2 Can you handle all these information to take right decisions?

9 Challenges in the Hinterland Rates Quotations Surcharges Conditions Schedules Operators Connections 2 cases in practice Network

10 Synchro-modal planning: Customer Case

11 DSV A strong global network > Approximately employees worldwide > Own operations in more than 80 countries > More than 400 branch offices, terminals and warehouse facilities A strong logistics partner > Top 5 global freight forwarder > Annual revenue 6.5 billion EUR > Headquartered in Denmark, listed on Nasdaq Copenhagen

12 DSV One company three divisions

13 13 Challenge

14 Process Sales Purchase Operation Sales rate Modality choice Carrier choice

15 Objectives Reduce transport cost Better service to the customer Co2 reduction Higher competitiveness

16 Rates and costs are demodata Network knowledge included in the model

17 Rates and costs are demodata Combining data to analyze optimal scenarios

18 Rates and costs are demodata Integrating back-office data, linked to ERP

19 Rates and costs are demodata Different perspectives to work with the model

20 Rates and costs are demodata Integrating the hinterland and the deep sea

21 Plan using big data on the go using a mobile application Plan input Transport options Plan details Rates and costs are demodata

22 Rates and costs are demodata All required data are included in the model in a simplified way

23 Result Optimize transport cost Prevent additional cost for demurrage - detention Find the optimal combination of modalities Better service to the customer Better alignment with the logistical concepts of the customer Customers request clean transportation Co2 reduction 20% Modal shift to barge and truck Reduced Co2 emission of km. Higher responsiveness Higher competitiveness

24 Smart Barging

25 Smart Barging Classical Fixed barges in fixed loops. Smart barging Barges dynamically planned in dynamic routes based on contracted services and fluctuations in demand.

26 Goals Reduce transportation cost Optimize balance tonnage - Cargo Meet service levels Better service to the customer

27 The seaport puzzle

28 LIVE DEMO

29 Results Integral business model allows network- operator to adapt available data and analyse different scenarios Reduce transportation cost Optimize balance tonnage - Cargo Meet service levels Better service to the customer Higher competitiveness

30 Conclusion

31 Recent developments Carrier deepsea schedule changed Delays in the port of Antwerp Export volumes grow New tunnel in Switzerland New lock in Antwerp New take over carrier New inland barge terminal Network disturbance

32 Need to model your business Business logic Planning Scenario's Scenario's Price Margin

33 Questions & Answers Please feel free to submit your questions for our representatives using the Q&A tab in the Go-to-meeting control panel now. Walter Kusters Ab Ovo Ard-Jan Cieremans Ab Ovo Jan Willem van Crevel AIMMS

34 Thank you! >Link to slides and recording of this webinar will be ed to you. >If you have any questions, please contact us or visit our websites to download resources