Applications for stochastic models in lean management Opportunities and missing links

Transcription

1 Applications for stochastic models in lean management Opportunities and missing links GOR AG Supply Chain Management Synthese von Lean Management und Operations Research Leinfelden, Kai Furmans Institut für Fördertechnik und Logistiksysteme KIT Universität des Landes Baden-Württemberg und nationales Großforschungszentrum in der Helmholtz-Gemeinschaft

2 Development of Lean Systems MRP ERP SCM as APS today Lean SCM Toyota Lean - Shock Lean-Revival today 2

3 Why Lean Management? Empirical and Anecdotal Evidence, that Lean Management enhances Productivity more than the average productivity gains in the base population See i.e. Dehdari 2013 Design Elements which tackle variability and create a direct relation between Design, KPI and KPR Leadership and Target Setting Associate involvement and Empowerment, large base for problem solving 3 Institut für Fördertechnik und und Logistiksysteme,

4 Some foundations Supply Chain Physics Stochastic models can explain, why it works Design Elements which tackle variability and create a direct relation between Design, KPI and KPR Leadership and Target Setting Associate involvement and Empowerment, large base for problem solving

5 Supply Chain Physics Little s Law Initial situation stock = throughput * throughput time N s [units] = λ [units per time] * t s [time] Double throughput with same throughput time doubled stock Same throughput with longer throughput time increased stock 5

6 Supply Chain Physics Little s Law Application in Value Stream Mapping CT = 160 min = 1/3 d Response Time = Inventory Customer Takt Source: valuestreamguru

7 Supply Chain Physics Variability Generates Waiting Times arrivals & processing clocked no waiting time random interarrival time & clocked processing Waiting times arise visible by stocks (Little) 8

8 Excursus - Variability Ereignis 1 c²=1,00 0, Ereignis 1 c²=0,01 0, c 2 Var( X ) 2 E( X ) X 2 2 9

9 Supply Chain Physics Capacity Utilization (workload) and Variability c a c N b 1 2 stock ,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 workload With the workload, the influence of variability on stocks and leadtimes increases stock Medium vs. high variability at 94% workload Low vs. medium variability at 94% workload No vs. Low variability at 94% workload 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 No variability Low variability workload Medium variability High variability Comparison of the average stock of the system at varying variabilities 10

10 Typical sources for Emergence and Intensification of Volatility Caused by the design of the material flow Caused by downtimes In the material flow In the processing As a result of scrap Caused by forming batches in containers Caused by inventory control 11

11 Branches in the Production Flow Generate Variability Initial variability in front of the branch Create Linear flow Whitt,

12 standard deviation demand demand / order quantity Oversized Container Fill-Up Capacities Generate Variability standard deviation container calls (demand) , , ,00 100,00 Demand Order Standard Standardabweichung deviation Behälterabrufe container calls container quantity Average demand: 100 / period, = 20 50,00 0, period 13

13 Verw eilzeit der Aufträge Without downtimes dwell time Increase in capacity utilization (workload) by downtimes Effect of Downtimes The increase in leadtime caused by downtimes is essentially higher than it can be explained only by the loss of capacity! ,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45 0,5 0,55 0,6 capacity utilization Auslastung (workload) 0,65 0,7 0,75 0,8 0,85 0,9 0,95 14

14 Methods of Control Effects Why pull? Why leveling? 15

15 Open vs. Closed Systems N 1 Open system: control of system load (z.b. MRP) result is stocks Closed system: control of stocks result is throughput (performance) The required stock (leadtime at equivalent throughput) is in closed systems always less than/equal to the one in opened 16

16 Kanban-Systems are Closed loops with a Brake Pre- Products Station of Synchronization j satisfied demand Demand Pre-Products Demand Pre- and finished products with Kanban (WIP) 17

17 Impact of the Design Elements of Lean Systems stock Standards Small handling units Milkruns SMED Linear flow Poka Yoke Variability Levelling ,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 workload TPM Kanban Low Cost Automation Load Control 18

18 Leadership and Target Setting Design Elements which tackle variability and create a direct relation between Design, KPI and KPR Leadership and Target Setting Associate involvement and Empowerment, large base for problem solving

19 Leadership and Target Setting Evolution of Management Style z.b. size of company Maturity Pressure by results publication Develop target states Delegation and motivation Directions Creativity Age of organization Inspired by Greiner, HBR,

20 Approach: Design a System, which achieves your goals! translate from to physical units Business Targets in translate from physical units to Set New Targets Target Value Stream Design Calculate KPI and KPR Improvement KPI and KPR met? Implement Implemented Value Stream 8 Institut für Fördertechnik und und Logistiksysteme,

21 Support needed by OR some examples Develop exact or approximate methods, which allow a fast and simple sizing and evaluation of value streams Sizing supermarkets (here, only number of Kanbans) Shingo: K = Q+α n K = number of kanbans; Q = quantity of products in batch production; α = minimum security stock level; n = quantity of products transported on a pallet. Source: vision-lean.com DIMASCOLO, M. ; FREIN, Y. ; DALLERY, Y. : An Analytical Method for Performance Evaluation of Kanban Controlled Production Systems, 1993 K = Monden: d te + tf (1 + β) c Bosch: K = RE + LO + WI + LO + SA k = number of kanbans; d = demand on the planned period; te = waiting time, defined from the time since the necessity of production is defined until effective production starting time; tf = time it takes to produce a container (one kanban) of products; β = safety factor (around 15%); c = container capacity. Source: Salgado, Varela,

22 Dynamic Aspects When to change the supermarket size? Determine escalation levels -> when to change operations When to increase / decrease capacity? When to use a second source How to ramp up production over several levels? Fill the buffers first? Try to ramp up simultaneously? What is the impact of adjustable capacity, when capacity adjustment is subject to: Early announcement? Limitations in extend? How much does (possibly inaccurate) advance information help in a dynamic environment?

23 Approach: Design a System, which achieves your goals! Design Description and Calculation Layout Measure it! Try it! 8 Institut für Fördertechnik und und Logistiksysteme,

24 Continuous Improvement at the Base Design Elements which tackle variability and create a direct relation between Design, KPI and KPR Leadership and Target Setting Associate involvement and Empowerment, large base for problem solving

25 The Continous Improvement Cycle Location, Time, Sequence Improve Standards Describe Standards Observe Standards - Measure Deviations Right Location? Right Time? Right Sequence? Solve most Severe Problems first Pareto Analysis of Deviations 7 Institut für Fördertechnik und und Logistiksysteme,

26 Development of a Lean Operation Mind-set Awareness: we are not good enough Willingness: we have to change Support: we support those who try to improve Continuous Improvement: we are never satisfied Management level Face-the-Facts On the shop floor Face-the-Facts On the shop floor Face-the-Facts Attention to Change Project Request Adherence to Defined Processes Project level Value Stream Analysis Value Stream Design Vision, First Step, Set-up Project Implementation upstream, from customer to supplier one value stream, no shortcuts Hand Over responsibility moves from project to operations Shop-floor level Lean basics training 5S, pull, Kanban Basics training Kaizen, KVP, CIP Problem Solving Implementation of basics Attention to Work Systems i.e. MTM Continuous Improvement Advanced Lean i.e. heijunka Warming Storming Norming Performing t 2 Institut für Fördertechnik und und Logistiksysteme,

27 Problem Solving Approaches What is better: Put more emphasis on problem solving or just have more inventory? What is better: A simple system, which is well understood and under control or an optimal system, which is not quite understood and not under control? What is the impact of reward systems on Problem solving vs. having excess capacity / excess inventory?

28 Sylt Flensburg Rügen Ostfriesische ins. Wilhelmshaven Bremerhaven Kiel Lübeck Hamburg Fehmarn Schwerin Rostock Oldenburg Bremen Karlsruher Institut für Technologie (KIT) Institut für Fördertechnik und Logistiksysteme () Postanschrift Kaiserstr Karlsruhe Besucheranschrift Gotthard-Franz-Str. 8 Geb Karlsruhe Telefon +49 (721) Telefax +49 (721) kai.furmans@kit.edu www Hamm Duisburg Dortmund Krefeld Essen Hagen Mönchengladbach Düsseldorf Köln Siegen Bonn Trier Osnabrück Hannover Wolfsburg Hildesheim Braunschweig Münster Bielefeld Salzgitter Koblenz Wiesbaden Mainz Frankfurt am Main Offenbach Darmstadt Kaiserslautern Mannheim Ludwigshafen Heidelberg Saarbrücken Karlsruhe Freiburg Pforzheim Paderborn Heilbronn Stuttgart Konstanz Göttingen Würzburg Ulm Erfurt Jena Erlangen Fürth Nürnberg Augsburg München Potsdam Dessau Halle Leipzig Gera Chemnitz Zwickau Regensburg Berlin Cottbus Dresden