HubNorth Seminar Intern logistik og procesoptimering Mandag den 9. januar 2012, Scandic Aalborg

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1 HubNorth Seminar Intern logistik og procesoptimering Mandag den 9. januar 2012, Scandic Aalborg Projekter og samarbejdsmuligheder med CELOG Hans-Henrik Hvolby ) Bedre Performance med CELOG Diagnostics Kenn Steger-Jensen ) Diskussion herefter pause kaffe og networking Mere info på

2 Kontakt CELOG Kenn Steger-Jensen Telefon : (+45) kenn@celog.dk Hans-Henrik Hvolby Telefon : (+45) hhh@celog.dk Center for Logistik, Aalborg Universitet Fibigerstræde 16, DK 9220 Aalborg, Denmark Telefon: (+45) , info@celog.dk Web:

3 Organisation Production Technology Building Management Materials Technology Department of Mechanics & Production Centre for Logistics (CELOG) Mechanical Engineering Product Configuration Vore tidligere kolleger fra CIP er flyttet til Institut for Økonomi og ledelse (Business & Management)

4 Research focus Tools and techniques to analyse, plan, optimise, integrate, track and trace CELOG S primary Enabling Technologies are Planning, Scheduling & Logistics ERP, APS, VMI m.v. & OM Diagnostics & Predictive Performance Management ERP add-on (delivery precision & inventory) & RFID New areas: Reverse Logistics & Carbon Footprint (EU project) Intelligent Harbour & Transport Simulation & Inventory Management in Hospitals Link

5 Types of projects Industrial research and development projects Regional funded projects (close company collaboration - lab ) EU/DK funded projects (close university collaboration) EU/DK funded Ph.D. students Industrial co-funded Ph.D. students Student projects Level of knowledge and expectations grow during their study Final year master students are currently attached to Port of Aalborg, Royal Arctic Line, Siemens Lego, Danfoss, Celog First year master students and second/third year bachelor students attached to project companies

6 Current and pastprojects Goal, Business Intelligence and Diagnostics (Regional EU) ViaNord, Logistic Competence Development (Regional EU) Tapas, Robotics-enabled Logistics (EU) LogiNord, Logistics in the Food Supply Chain (NordForsk) People (Prossalic), Reverse Logistics (EU) SmartLog, Logistic Assessment tool (Regional EU) ValuePole, Value Chain Optimization (EU) InLog, Intelligent Logistics (Regional EU) EMPO, Performance Optimisation (EU) VUR, Logistics Competance Development (E&BS) PLUS, Supply Chain Collaboration (VTU) LEVUD, Supply Networks using SCOR (VTU/CIP) VIVIT, Systems Integration (Regional EU) OS2000, Order Management (Regional EU) ACM, Actitity Chain Model (EFS) Industrial Ph.D. (Aalborg Harbour, Alfa Laval, Oracle, Microsoft)

7 Projektportefølge LOGINORD. The objective is to enhance sustainability and efficiency of logistics in Nordic fresh food supply chains through development of next generation supply chain planning and control solutions. Partners: Universities in Helsinki, Gothenborg and Trondheim. Funded by FI via Nordforsk. Celog share: 2 mio NOK. ( ). [link] PEOPLE. Product-Service System across the LifeCycle. Focus on Sustainable Reverse Logistics (Re-cycle, Re-use, Re-pair). Partners are universities in Italy, UK, Israel, Australia, Japan, US and Brazil. Network project financed by EU ( ). SMARTLOG.Etablering af netværk for virksomheder inden for transport og logistik i samarbejde med Aalborg Kommune og Aalborg Havn. Planlagt opstartsseminar 8 marts Projektet er støttet af Aalborg Samarbejdet og søges støttet af vækstforum. VIANORD. The objective is to improve the logistic competence of 80 regional companies through assessment of current competences [link] combines with projects, lectures, seminars etc. The project is funded by Vækstforum (regional EU funds). Celog share 7 mio. DKK. ( )

8 Det går som vinden blæser!

9 The Value Chain Material Suppliers Supply Base Component Supplier Manufacturer Contract Manufacturer Distributors Customers Retailers Distribution Channels Business Consumer

10 Forrester effekten Order batching: Bedre kapacitetsudnyttelse af distributionsapparatet, konsolidering af ordrer for at opnå kvantumsrabatter eller forenkling i ordreafgivelsen. Price fluctuations: Kampagnetilbud, udsalg o. lign., som kan få kunderne til at købe større mængder, give et forkert billede af efterspørgselsudviklingen. Rationing and shortage gaming: Spekulation i mangelsituationer for, at får en større andel af produkter. Demand signal processing: Bearbejder de efterspørgselssignaler de modtager, og hvordan det påvirker de behov de sender upstream (fx ugentlige genbestilling). Non-zero lead-times: Tidsforsinkelser pga. proces forløb, lange transporttider etc.

11 Måling af forstærkning på en knude

12 Manipulering af data Variationen kan mere eller mindre bevidst manipuleres ved at aggregere eller disaggregere data alt efter hvordan den disaggregerede tidsserie ser ud. For de aggregerede data øges variansen i tidsserie 1, mens den falder i tidsserie 2. Hvis tidsserie 1 var behov ind mod en knude og tidsserie 2 var behov ud fra en knude ville det medføre en markant efterspørgselsforstærkning med de disaggregerede data, men ingen efterspørgselsforstærkning med de aggregerede data.

13 Løsningselementer Større gennemsigtighed i kæden gennemsigtighed og point-of-saleinformation kan give bedre prognose & styring Reduktion af leadtime tilnærm leadtime til kundens ønskede leveringstid! eller omvendt? Fjern usikkerheden aftal aftrækket hos slutkunden så tilfældig ordreafgivelse helt undgås Reducer antallet af beslutningstagere i kæden eks. VMI, leverandørstyring og central distribution

14 Diagnostics To help companies improve their performance by: providing a more precise and understandable diagnosis of performance issues better understand how multiple KPI s interact

15 Background and Motivation Performance Optimization What? Minimizes the gap between target and actual performance by implementing positive changes to organizational culture, systems and processes Is based on performance measures represented by a set of KPI s for a firm and derived to planning and control Performance Optimization Why? Assists in achieving management goals and fulfillment of enterprise or supply chain strategy Increases the profit margin Insight into the organisational behavior and daily decision making

16 Delivery performance (On-Time-In-Full) - Actual delivery versus promised delivery -Often these data are not available in the ERP-system. In this case, selected data (customer order number, items, quantities, delivery date(s) etc) are stored on a daily basis and used for diagnostic purposes

17 The DiagnosticApproach Utilizing a cause-network model for identifying root causes for poor performance Order Changes Customer OTIF Supplier OTIF Planning Performance Production Performance

18 Expected versus actual delivery precision Delivery Performance [%] Top10 Items R-OTIF OL-OTIF V-OTIF

19 Inventory Management Analyse past inventory fluctuation - Customer orders (size and time) - Purchase orders - Manufacturing orders - Recommend settings regarding - Aligning settings of related components (BOM) - Order size calculation methods - Minimum inventory holding - Calculate expected service level based on current settings - Compare with strategic/managerial expectations

20 Q U E S T I O N S A N S W E R S