Lean Six Sigma

1. Lean Six Sigma Reducing Street Light Inventory Rick Orr, Finance Manager Public Works

2. Project Objectives • Work Towards Achieving Mayor Richard’s City Goals -Safe City -Quality jobs -Improved customer service - B.E.S.T. • Demonstrate how Lean Six Sigma Improves Customer Service and Saves Resources • Improve Customer Service by Reducing Capital Investment in Street Light Inventory

3. What Is Lean Six Sigma? • Systematic approach to reducing process defects that produce undesired outcomes - in our case, improving the decision making regarding inventory purchases • DMAIC – Define, Measure, Analyze, Improve, Control • Team focus to problem solving - each of us are experts in certain areas of the inventory process and each have specialized knowledge of portions of the process

4. Project Description Street light inventory seems excessive relative to usage Problem Statement: Objective: Reduce inventory to optimum level

5. Cost of Poor Quality External Customers Citizens Carrying excessive inventory ties up capital that can be used elsewhere Lost capital opportunities cause unnecessary high tax rates Internal Customers City Staff Uncertain ordering schedules makes it difficult to anticipate ordering needs Inaccurate inventory records Inaccurate damage recoveries Inaccurate materials billing

6. Benefits Frees capital funds to be redirected towards other use and helps maintain low taxes

7. The “Y” The Y: the total value of street light inventory, measured monthly Y = f(x1,x2,x3,…,xk)

8. Why Minimize Inventory? Minimizing Inventory: Increases flexibility in asset management Makes it easier to control Reduces the need for space Makes it easier to count Reduces aged inventory Inventory is an asset, but it is a non-productive asset. It earns no interest but costs City in handling, shrinkage, and space.

9. Definition of the Y The Defect: excessive street light inventory The Y: the total value of street light inventory, measured monthly Y = f(x1,x2,x3,…,xk) The Project Plan: examine the factors that drive inventory levels on various items and appropriately reduce the level of individual street light items The Goal: Reach optimal levels of inventory to reduce the invested capital

10. Project Team • Champion: Greg Meszaros • Assisting: Michele Hill, Roger Hirt • Team Members: • Rick Orr, Project Leader/Black Belt • Dave Pepper, St Light Warehouse • Nate Parker, St Light Warehouse • Lori Dekoninck, St Light Warehouse • Phyllis Davis, St Light Engineering Admin • Steve Davis, Assistant Traffic Engineer • Tracy Neumeier, Internal Audit/Black Belt

11. Project Schedule Define March – April 2003 Measure May – Sept 2003 Analyze Oct – March 2004 Improve Apr – Jun 2004 Control Jun 2004 +

12. Street Lighting System Number of Street Lights (Approx) 27,500 Number of Alley Lights (Approx) 3,100 Energy Expense, 2003 $453,367 Department Expense, 2003 $2,743,285 Estimated Value of Network $8,500,000

13. Process Map Material Needs Determined Materials Ordered Materials Delivered Materials Stored Materials Depleted

14. Cause and Effect Matrix Cause and effect matrix: Important Factors: demand, lead time, order interval, level of safety stock

15. How Can Our Processes Fail? How can our process fail? • As ranked with FMEA, failures can result if: • historical usage data is not maintained and monitored • inventory usage is not recorded by maintenance crews • material usage is not recorded on work order tickets • expensive in-stock items are substituted for out of • stock items • vendor states inaccurate delivery time on bid • poor analysis done in budgeting cycle

16. Budget Vs. Actual Costs 2000-2003 Budget vs actual: 2000 - 2003 In May of 2003, the inventory budget was reduced by $100,000 in anticipation of project success. Approximately $80k less was spent on materials than modified budget would have allowed for ’03. Estimated savings to date (March ’04), $180,000.

17. Has All the Data Been Captured? Has all data been captured? Actual material expense 2001 $636,865 Actual material expense 2002 $584,287 Actual material expense 2003 thru 9-30 $320,199 Total $1,541,351 Historical usage captured Jan ‘01 – Sept ’03, valued at $966,547 Current inventory value as of Sept 30, 2003 $630,806 *Note that recorded usage does not total the amount expended

18. Has All the Data Been Captured? Has all data been captured? All recorded historical usage was collected • Work orders • Re-lamping lists • Proactive maintenance files • Capital project files Historical inventory values were not kept. It can not be determined if some usage was not recorded or if the differences shown on the previous slide are attributable to changes in the value of inventory on January 1, 2001 as compared to the value of inventory on September 30, 2003. What can be done to insure data integrity, going forward?

19. Low Hanging Fruit-Data Source Low hanging fruit – data source • Implementation of an inventory tracking database • Material usage recorded as it leaves warehouse • Information readily available to all staff • Facilitates data collection going forward • Improves accuracy of recorded usage • Accomplished without adding any additional tasks not already being performed by warehouse personnel • Data base implementation should help address 2 factors identified in the C&E matrix: availability of historical data and reliance on staff experience

20. Key Problem-Poor Record Keeping Key problem – poor record keeping Modified Microsoft Office Template: In-house expertise without added cost

21. Inventory Turn-Annual Inventory Use • Inventory Turn: A common method of measuring inventory • management • Calculated by dividing the average inventory level ($) into the annual inventory usage ($) • 2003 material usage $450,539 • 2003 average inventory value $682,441 • *For 2003, Street light inventory turned only .66 times • *For 2004, Street light inventory turned 1.124 times

22. Inventory Records-Inventory Accuracy At the start of this project, 165 items were identified with specific item numbers Shortly after implementation of database, an additional 88 inventory numbers were assigned to materials not previously carried on “the books” *Value of items not previously accounted for totaled $26,581 or 4% of inventory on hand as of Oct 21, 2003

23. Inventory Accuracy Inventory accuracy - Accuracy Benefits • Enhance Customer Service • Reduce Stock Outs • Production is not jeopardized

24. Inventory Accuracy Past: Historically, a physical inventory count was conducted once per year. Accuracy statistics were not maintained, and the existing stock record was over-written with updated counts. Effective 2004, implemented Cycle Counting Current: Inventory items are now differentiated and counted multiple times per year, depending on usage-value (inventory classification) Class A items, count 6 times/year – 80% of $ spent over 33 months Class B items, count 2 times/year – 15% of $ spent over 33 months Class C items, count 1 time /year – 5% of $ spent over 33 months

25. Inventory Accuracy Rates Inventory accuracy rates After annual 2003 inventory count, error rates were established. An error occurs whenever an item count differs from the inventory record, while considering +/- 5% as an acceptable tolerance. Class A items – 27.3% error rate Class B items – 35.7% error rate Class C items – 26.1% error rate All items – 27.3% error rate, 12-31-03 Error rates will be tracked with control charts, going forward. If the use of the inventory data base and the implementation of cycle counting fail to improve this error rate, this problem could be investigated further as a Green Belt project.

29. Show Me the Money! 3 yrs of expense, 165 item numbers Most of the project effort and analysis will be directed at the 22 items comprising 80% of the expenditures. These top 22 items are designated as class A items.

30. Ranked listing of high expense items (class A) Jan 01-Sept 03

31. Poles Used: Jan 2001-Sept 2003 Poles used: Jan 2001 – Sept 2003

32. Fixtures Used: Jan 2001-Sept 2003 Fixtures used: Jan 2001 – Sept 2003

33. Bulbs Used: Jan 2001-Sept 2003 In early October 2003, 48 250w bulbs and 48 400w bulbs were ordered! Why? “Because we need them!” Bulbs used: Jan 2001 – Sept 2003

34. Purchase Decisions Made On Usage Differences in usage values and dollars spent each month could mean that not all material usage was recorded or more inventory is being purchased than is being used. Total $ value of materials used = $1,034,998 Total $ expended = $1,577,055 *34 months examined

35. Correlation of Funds and Usage If R-Sq > 80%, then correlation is significant R-Sq = 1.2% * With monthly measurements, there does not appear to be a significant linear correlation between material usage and the amount of funds spent for inventory acquisition.

36. Changes to Bidding Specifications • Additional bidding expectations were requested of vendors bidding on poles, mast arms, and fixtures • Informed all bidders of our goal to minimize inventory carrying costs • Required bidders to list best price at minimum quantity levels, price at lesser quantity order levels, and worst price if only 1 unit ordered • Required vendors to list the length of time between order placement and order delivery (lead time) *This information will be critical in determining optimal inventory levels and reorder points

37. Purchase Decision: What Bulb is the Most Cost Effective to Purchase? Beginning in 2000, Street Light Engineering began testing the longevity of various bulb manufacturers

38. Low Price ≠ Best Price • Sylvania bulbs are the most cost effective for the City • Without the cost/lifespan analysis, former procedures would have directed us to purchase Phillips bulbs • The addition of bulb replacement labor costs to the analysis, would further expand the cost differences

39. Changes to Ordering Procedures • Material ordering procedures were tightened • for all inventory purchases • order form initiated by warehouse personnel or engineers • order requires sign-off by department director • order requires sign-off by finance manager First time the procedure was used, an order of photo cells was reduced from 500 (4-5 month supply) originally requested to 200 ordered

40. Purchase/Replenish Pull System Purchase/Replenish Pull System Implemented a widely recognized inventory system, developed by Toyota Motor Corp, known as Kanban Kanban is an empirically driven method of both signaling the need for inventory and controlling inventory levels Kanban – Japanese word for “sign”

41. Purchase/Replenish Pull System 4 Variables for an Effective Purchase/Pull System Demand – the average monthly usage amount Lead Time – length of time expired between placing order and receiving goods, measured in monthly units Order Interval – how often orders are anticipated, in monthly units Safety Stock – amount of inventory to be held to compensate for demand variability and/or lead time variability

42. Historical Demand Historical Demand • Estimate Future Costs By Analyzing Past Material Usage • 4 Uses of Materials • Maintenance Repair to Damaged Facilities • Re-lamping Activities Based on Light-Out Lists • Proactive Replacement of Aged Facilities and/or Bulbs • Capital Construction Project Capital projects are known prior to construction. By meeting minimum requirements, capital materials can be ordered on a project by project basis. On appropriate projects, capital needs will now be segregated from other material needs. Recall that some of the historical data might be suspect…

43. Demand Analysis Demand analysis • Demand Analysis = Compare means, standard deviations, • and medians for each item • Pre data base implementation • Post data base implementation If similar, conclude historical usage was accurately collected – use data collected since January 2001 for a specific item If different, conclude historical usage was not accurately collected – use data collected since October 2003 for a specific item

44. 100 HPS Town & Country Fixture 21.41% of material expense

45. 100 HPS Town & Country Fixture (Continued) • Should all data be used to estimate monthly demand? • Difference in means • Difference in medians • Similarity in Standard Deviations • Inconclusive – to not under estimate, use data since Oct 1, 2003

46. 150w Cobra Head Fixture 8.82% of material expense

47. 150w Cobra Head Fixture (Continued) • Large difference in means • Large difference in medians • Similar standard deviations • Conclusion – Including data prior to Oct ’03 might result in under estimation of usage

48. 100w Alley Fixture Demand Analysis – Lots of Variability

49. 100w Alley Fixture 100w alley fixture (continued) (Continued) • Similar Means • Similar Medians • Similar Standard Deviations • Conclusion – Including data back to Jan ’01 should not result in under estimated demand This methodology was used to analyze demand for all class A and class B items

50. Lead Time lead time Lead Time - Time Expired From Order Initiation to Receipt of Goods • stated in bid specifications for poles, fixtures, bulbs • include City staff time for requisition preparation and sign-off