Improve Tech Pre Calibration Project Book


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Improve Tech Pre Calibration Project Book

  1. 1. DMAIC Project Report Please Select Belt Type: DMAIC BB DMAIC GB Project Title:      Improve Technetium Pre-calibration Project Number (1st, 2nd, 3rd…):      2nd Instantis Project ID#:       Project Leader Name:      Ramesh Rajan Project Leader Job Title:      Process Engineer Imaging Solutions/Nuclear Pharmacy Segment/GBU/Division/Plant/Region: Operations
  2. 2. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Executive Summary The Nuclear pharmacy network comprises 1/3 of the total Imaging division revenue. Technetium-99 is a primary radioactive isotope which is used for various diagnostic procedures at hospitals, imaging centers etc. This represents 80% of the pharmacy product portfolio. The product has an extremely short shelf life; a 6 hour half-life and hence is critical that it be used efficiently and in the most cost-effective manner. The pre- calibration time of the product is time elapsed between when the product was dispensed to the time it was used for the intended application. Currently significant amount of Tc-99 was decaying at the customer site (>6hrs). Historical data showed pre-calibration time for Tech products averaging at approx 8 hours. This represents significant product decay and lost revenue to the pharmacy network. There was also a large degree of inconsistency in pharmacies adhering to Pre-calibration policies with their customer base. The goal of the project was to improve the pre-calibration time on Technetium base products. Data collection was carried out by developing query language which helped in presenting accurate data that gave high visibility to this project. Through the use of the DMAIC methodology this project used tools such as VOC, FMEA, Hypothesis test that focused primarily in improving the pre-calibration times for Tech based products by implementation of the following: - Revenue stream programming change to charge activity beyond the 6 hour pre-cal limit -Customer letter from marketing addressing the goal to maximize the availability of technetium 99m (Tc 99m) for patients, using as much as possible in procedures rather than allowing it to decay on the shelf. These new policies would be designed to encourage unit dose customers use a more “just in time” approach. - Sales rep training on the Tc conservation program to help facilitate in changing customer behavior; i.e. Move orders to later scheduled runs. To promote lower pre- calibration times, Covidien will charge for pre-calibration activity beyond six hours, new charges will be based on bulk Tc-99 pricing. Bulk Tc-99 will be calibrated for actual delivery time, unless customer chooses to pay for additional activity Annual impact estimated from this project is $3million/year. Results taken after program launch in Oct 09 shows savings of $300K validate the projected revenue. The final metrics for the project are as follows: Name of Metric Baseline Goal Actual(A)-Oct09 Pre-calibration time 8.15 6.0 7.15 . 2
  3. 3. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Define Project Charter Operational Excellence – Team Charter Program Name: Improve Tech Pre-calibration Total Savings Identified ($ $3 Million/yr value) Team Leader: Ramesh Rajan Team Champion: Dave Becker Product or Service Impacted Nuclear Pharmacies Business Unit: Imaging Solutions MBB: Start Date: April 2009 Target Completion Date: October 2009 Element Description Team Charter 1. Process: The process in which the The pharmacy network holds significant improvement opportunities opportunity exists. in reducing length of pre-calibration time on technetium based unit dose products and calibrating bulk doses at their delivery time. 2. Problem Describe the problem that needs Currently significant amount of Technetium 99 is decaying at the Description: to be solved, or the opportunity customer site which results in product waste and lost revenue. to be addressed. Global moly shortage severely impacts product availability. 3. Objective: What improvement is targeted? Reduction in Tech pre-calibration time would yield significant cost reduction to the pharmacy business. In addition it will improve product availability and increase patient access to Tc99 products. Saved Technetium could be sold at favorable price to a customer. 4. Metrics: What are the measurements Name of Baseline Goal Entitlement* Units of that quantify program progress Metric Measure and success? Precalibration 8.15 6.0 Hours *W hat is the bes t the process is time expec ted to pro duc e? 5. Business Results: What is the improvement in Cost Cost WIP/ Cash Labor Inc. business performance? Reduction Avoidance Inventory Flow Savings Sales Please list any other Reduction improvements on a separate X X sheet as needed. 6. Program Scope: Which parts of our business Included Excluded processes will be considered? The project will focus on all All other products Which customer segments, Technetium based products organizations, geographies, and which represent 80% of a timeframe? customer product portfolio. 7. Team Members: Names and roles of team Ramesh Rajan, Dave Becker ,Jeanne Landers, Terese Lafeber, members Carolyn Samra, Andy Farrow, Brian Courtney, Pharmacy Regional Managers. 8. Benefit to External Who are the final customers, Reduction in pre-calibration time will reduce cost, improve product Customers: what are their most critical availability to external customer and help mitigate the global moly requirements/measurements, shortage issue. In addition it will allow greater patient access to the and what benefits do we expect product. to deliver to them? 8. Schedule: Give the key milestones and Key Project Dates dates. Project Start April 2009 Define Complete May 2009 Measure Complete July 2009 Analyze Complete August 2009 Improve Complete September 2009 Control Complete October 2009 9. Budget: What financial resources are $5000 for TRON programming. required for the team? 10. Support Required: Do you anticipate the need for IS programming, TRON user training, customer communications. any special capabilities, hardware, trials, etc.? 3
  4. 4. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc SIPOC Map A SIPOC diagram was generated to provide a high level view of the Tech pre-calibration process. This tool helped the team to understand important inputs and outputs to the process. SUPPLIER INPUT PROCESS OUTPUT CUSTOMER Pharmacy receives customer Customer(Hospital, Standing, Demand order for Tech dose with Customer-Hospital, Presciption Independent) Orders,TRON requested delivery and Independent user calibration time Dispense dose with activity Pharmacist ,Technician Customer Order Dispensed dose Customer-Hospital related to calibration time Tech dose is ship confirmed Pharmacy Bill of Lading, TRON Shipconfirmed Dose Courier and taken for delivery Dose is delivered to customer Pharmacy Driver , Bill of Lading Delivered dose Customer-Hospital at requested delivery time Pharmacy bills customer for Pharmacy Dose charges Customer Invoice Customer dose includes freight charge. Customer(Hospital, Pharmacy collects sales Customer Invoice Sales Revenue Pharmacy Independent) revenue from dose sale Voice of the Customer/Business The team also undertook a brainstorming exercise to come up with key customer requirements. This “Voice of Customer” exercise showed the key outputs for the project .These outputs influence the end customer (i.e. hospitals etc) in maintaining reliable and quality supply. At the same time they also affect the pharmacies which are an internal customer as it will help in reducing product decay and capture lost revenue due to excessive pre-calibration. Reduce Tc Pre- Limit Precalibratiion time compared to current practice to save Tech waste Mimimize bulk dose waste by shrinking calibration limits calibration Maintain accurate delivery time in TRON Maintain ontime supply of Tech product to customer Voice of the On Time Delivery Need more Technetium due to global "Moly" shortage Customer Minimize product shortage Develop efficient production schedule at pharmacy Efficient Maintain efficient delivery routing for customer profile 4
  5. 5. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Project Y’s (KPOV’s) At the end of the Define Phase the team decided to focus on reducing the pre-calibration time on Technetium products as the key Output (Big Y). This would be achieved while still meeting product demand and delivery expectations. Key Process Output Variable • Reduce Precalibration time on Technetium based products Other Important Factors • Maintain product supply as per customer demand • Meet customer delivery schedules 5
  6. 6. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Measure Process Map A flowchart was developed to show the sequence and flow in the pre-calibration process. Using this, the teams were able to breakdown and understand the intermediate steps and determine inputs and outputs for each major step. Process Flow Map for Tc 99 Precalibration Pharmacy receives Customer Calibration time, Standing/Demand Order for Tech Delivery time dose Pharmacist enters order in TRON and generates prescription Dispense Dose with activity related Check to calibration time Order Is dose matching with BOL NO YES Tech dose is ship confirmed and taken for delivery Dose delivery to customer at requested delivery and calibration time Pharmacy bills customer and collects sales revenue for dose sale Pharmacy Invoicing $$ Pharmacy adds new customer , negotiates existing customer contracts, communicates any change in policy or contract 6
  7. 7. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Cause and Effects Matrix In order to prioritize the input variables affecting the output requiring improvement, the team used a Cause and Effect (C&E) matrix. A Pareto was also done on the top raking critical inputs from the C&E with a cut-off of 200. This tool helped to focus on the few critical inputs which affect the output. Cause and Effect Matrix Rating of Importance to 9 10 8 9 Customer 1 2 3 4 Requested Activity at calibration Availability of Tech products On Time Deliveries Drops per Delivery time Total Process Step Process Input Calibration,delivery time, Pharmacy receives 1 calibration policy,Customer 9 9 3 9 276 customer order communication Pharmacy negotiates Calibration policy, Customer 6 existing contracts, 3 9 9 9 270 contract, Communication communicates changes Dose Dispensed with Customer Order,TRON, 2 activity related to 3 9 3 9 222 Calibration,Dely time calibration time Tech dose ship confirmed 3 Dispensed dose, BOL 9 9 3 0 195 and ready for delivery Dose delivered to customer 4 at requested delivery and Dose, BOL, Driver 9 9 3 0 195 calibration time Pharmacy bills customer 5 and collects revenue from Sales Invoice, Freight Bill 1 9 9 1 180 dose sale 0 0 306 540 240 252 Total Lower Spec Target Upper Spec 7
  8. 8. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Pareto of C&E matrix C &E Pareto Analysis 1400 100 1200 1000 80 Percent Count 800 60 600 40 400 200 20 0 0 Process Inputs on n e L r il l i io tim O ve tB at at ,B ri h ic ic el y se D ig un un ,D do L, re m m m on d BO ,F co om r at i se e, ic e er ,C b en os vo m ct ali i sp D In to ra ,C D us nt N les ,C co R O Sa cy er r ,T oli m de n p u sto r O ti o ,C r ra cy me li b li to ca po us e, ion C tim at y br v er ali eli C d n, ti o ra lib Ca FMEA The FMEA focused on specific process failures that would affect the Tech pre-calibration process and which would cause product decay and lost revenue. A RPN cut-off of 300 was established by the team for taking the critical inputs coming out of the FMEA into the Analyze phase. 8
  9. 9. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Improve Tech Prepared by: Ramesh Process or Product Name: Precalibration Rajan Responsible: Ramesh Rajan FMEA Date (Orig) _September 29th, 2009_______ S O D R Potential Failure Potential Failure Process Step Input E Potential Causes C Current Controls E P Mode Effects V C T N How Severe is the How well can you effect to the detect cause or How often does cause or FM cusotmer? occur? FM? Step of the process Input under investigation? In what ways does What is the impact What causes the Key Input to go What are the existing under investigation the Key Input go on the Key Output wrong? controls and procedures wrong? Variables (Customer (inspection and test) that Requirements) or prevent either the cause or internal the Failure Mode? requirements? Should include an SOP number. Pharmacy receives Excessive Product decay , Lost TRON 8 Lack of limits on calibration time 7 None 8 448 customer order precalibration revenue Dose dispensed with Inaccurate delivery Customer Delivery activity related to Expected Delivery time Product Decay 7 Customer Behaviour 8 7 392 time schedule calibration time Pharmacy negotiates Miscommunicaiton Lost revenue due to existing contracts, Customer Contract, Lack of attention to true need, Customer Communication or lack of product decay, 8 8 6 384 communicates Calibration policy overemphasis on safety insurance by Sales reps standardized policy excess inventory changes Pharmacy receives Excessive Historical customer behaviour, Calibration time Product Decay 9 8 Customer Communication 5 360 customer order precalibration resistant to change Lost revenue due to Customer Contract, No adherence to Customer demand, variation in Customer Communication product decay, 8 7 6 336 Calibration policy policy Tech needs by Sales reps excess inventory Pharmacy receives Excessive Unfavourable product Lack of standardized Calibration time 9 7 Contract, Calibration policy 5 315 customer order precalibration margin policy/contract terms Inaccurate delivery Lack of foresight, Product Delivery time loaded in Expected Delivery time Product Decay 7 8 4 224 time Insurance TRON Pharmacy receives Incorrect activity Product decay, loss No activity limits in TRON, wrong TRON 6 6 TRON checks 2 72 customer order dispensed of revenue order entry 0 0 Pareto of FMEA Pareto of FMEA RPN 2500 100 2000 80 Percent Count 1500 60 1000 40 500 20 0 0 Potential Failure Mode n e ic y icy er t io t im ol l th ra y p po O a lib er d to ec liv ize de rd ce pr da en iv e a te n er ss ur ta h ce c fs ad Ex ac o No In k lac or n it o n ic a u m o m isc M Count 1123 616 384 336 72 Percent 44.4 24.3 15.2 13.3 2.8 Cum % 44.4 68.7 83.9 97.2 100.0 The key inputs from the FMEA were Calibration time, Delivery time, Customer contracts and TRON. 9
  10. 10. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Gage R&R Data collection for the Measure phase was obtained from queries generated from the TRON system A Gage R&R study was then conducted to test the precision of the measurement system and this was done with the help of 2 operators on the critical metric; Average pre-calibration time. A Gage R&R review showed the variation due to repeatability and reproducibility to be at 3% and part-to part variation at 99% which showed the measurement system was very accurate. Gage R&R for Over 6 hr precalibration time Gage R&R Study - ANOVA Method Two-Way ANOVA Table with Interaction Source DF SS MS F P Sample 34 95.0457 2.79546 3007.40 0.000 Operator 1 0.0037 0.00370 3.98 0.054 Sample * Operator 34 0.0316 0.00093 * * Repeatability 70 0.0000 0.00000 Total 139 95.0810 Alpha to remove interaction term = 0.25 Gage R&R %Contribution Source VarComp (of VarComp) Total Gage R&R 0.000504 0.07 Repeatability 0.000000 0.00 Reproducibility 0.000504 0.07 Operator 0.000040 0.01 Operator*Sample 0.000465 0.07 Part-To-Part 0.698633 99.93 Total Variation 0.699137 100.00 Study Var %Study Var Source StdDev (SD) (6 * SD) (%SV) Total Gage R&R 0.022457 0.13474 2.69 Repeatability 0.000000 0.00000 0.00 Reproducibility 0.022457 0.13474 2.69 Operator 0.006288 0.03773 0.75 Operator*Sample 0.021558 0.12935 2.58 Part-To-Part 0.835843 5.01506 99.96 Total Variation 0.836144 5.01687 100.00 Number of Distinct Categories = 52 10
  11. 11. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Gage R&R Study Reported by : G age name: Tolerance: D ate of study : M isc: Components of Variation Over 6 hr precalibration time by Sample 100 % Contribution 4.5 % Study Var 3.0 Percent 1.5 50 a ta ll e m n o d us d a s n er it le rg l le n a s a es is mi rd o i a ok h d w co is ul t e o re on n svi he st o ag la n b oo all yto nv tr o da u sv i st o n s in d e l ph i a f o nd ph r o rg la n na c s ou Pa e led ar i t o tla l t le o i c e m w D Da De De er ri is b ick ou Ka a L ng em M M il r la de eb s buort agi a n t L St t P To sb A l A Be t h B Ch lev o lo es t u d a H H om s A M O i la in it t P S F r S S e Be C C Cr i lk 0 La H L Lo Ph P P n W Ft Sa Gage R&R Repeat Reprod Part-to-Part Sample R Chart by Operator Over 6 hr precalibration time by Operator Bernard Ramesh 0.5 4.5 Sample Range _ 3.0 0.0 LCL=0 UCL=0 R=0 1.5 -0.5 Bernard Ramesh Operator Xbar Chart by Operator Bernard Ramesh Operator * Sample Interaction 4.5 4.5 Operator Average Sample Mean 3.0 Bernard 3.0 Ramesh _ _ 1.5 LCL=2.146 UCL=2.146 X=2.146 a ta l le m n o d us d a s n er it l e rg l l e n a s a es is m ir d o ia ok h d w co is u l te o re 1.5 o n n svi he sto ag la n b ooally ton v t roda u svi to ns indel p hi a fo ndphr o rg la nna cis ouP a e leda r to t l a t le o ic e mw D a e e r isb ck s a L g m M il la e b bur t gi n L t P o b l Al A Be th B C hevo loes t D D D dea r i HiHo u K a AnM e MO rlad inei tts PoS a r a St S St Tk es Be Cl C Cr u ms i F il La H Lo L o Ph P P n W Ft Sa Sample Data Normality Data was collected on average pre- time calibration by pharmacy for June09 and it showed it was normal. Normality Test for Normal 99 Mean 8.151 StDev 0.8384 95 N 35 AD 0.180 90 P-Value 0.910 80 70 Percent 60 50 40 30 20 10 5 1 6 7 8 9 10 Average precal time June 11
  12. 12. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Process Capability Process Capability Sixpack of Average precal time June I Chart Capability Histogram LSL USL UCL=10.669 S pecifications Individual Value 10.0 _ LS L 0 X=8.151 USL 6 7.5 LCL=5.633 5.0 1 4 7 10 13 16 19 22 25 28 31 34 0.0 1.5 3.0 4.5 6.0 7.5 9.0 Moving Range Chart Normal Prob Plot UCL=3.093 A D : 0.180, P : 0.910 3.0 Moving Range 1.5 __ MR=0.947 0.0 LCL=0 1 4 7 10 13 16 19 22 25 28 31 34 5.0 7.5 10.0 Last 25 Observations Capability Plot Within Within O v erall 9 S tD ev 0.839367 S tDev 0.838408 Values 8 Cp 1.19 Pp 1.19 O v erall C pk -0.85 P pk -0.86 7 C pm * S pecs 15 20 25 30 35 Observation For the process capability calculations, the lower Specification limit was chosen as 0 hours and the upper limit was taken at 6 hours. The Cpk value of - 0.85 shows that the process capability is less than favorable and there is vast scope for improvement. 12
  13. 13. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Data Collection Data collection for the Measure phase was collected from TRON queries that measured the average pre-calibration time across the pharmacy network for June09. This was taken as the baseline. For this project the Average pre-calibration time is taken as the (Y). The pre-calibration time is defined as the time elapsed between the expected delivery time of the product and its calibration time. 13
  14. 14. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc 14
  15. 15. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Baseline Metrics From the data collected in June09 the report showed the following baselines Baseline Measurements Jun-09 Pharmacy Average Time over Delivery time( Bulks) Average Precalibration time June Ft Lauderdale 5.12 7.87 Crestwood 5.00 9.60 Kansas 2.00 7.95 Chicago 3.00 7.87 St Louis 6.25 6.98 Orlando 3.00 8.98 St Pete 3.00 7.87 Miami 4.00 9.04 Memphis 0.00 10.00 Atlanta 5.00 8.81 Boston 3.00 6.62 Wilkesbarre 3.00 7.12 Milford 3.00 7.41 Pinebrook 3.00 7.21 Hicksville 3.00 8.11 Hariisburg 4.00 7.80 Bethlehem 4.00 7.43 Altoona 4.25 7.38 Philadephia 3.00 8.10 Dallas 4.00 9.20 Los Angeles 3.00 7.52 Loma Linda 2.00 8.24 San Francisco 3.00 7.68 St Paul 3.00 9.50 Portland 4.00 8.34 Denver 3.00 8.12 Houston 3.00 8.46 Saginaw 2.15 7.62 Toledo 3.00 8.85 Colombus 3.00 6.65 Dayton 2.00 8.56 Detroit 3.00 8.66 Beltsville 3.00 8.67 Cleveland 3.00 9.25 Pittsburgh 3.00 7.81 Average 3.25 8.15 15
  16. 16. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Savings Summary Tc-99 Conservation Plan Annual Summary Impact Bulk Precal Beltsville 201,617 24,697 Cleveland 182,709 21,821 Hicksville 136,829 47,663 Pinebrook 165,864 6,599 Dallas 141,738 39,835 Los Angeles 142,937 4,037 St Pete 148,092 9,581 Denver 124,645 4,459 Orlando 107,840 13,175 Pittsburgh 81,113 52,328 Crestwood 107,076 34,832 St Paul 95,212 12,132 Boston 113,921 4,489 Harriburg 80,833 5,568 Detroit 73,360 3,802 Chicago 66,786 5,666 Milford 63,819 776 Dayton 38,784 6,496 Altoona 71,915 15,905 Wilkes Barre 60,352 3,273 St Louis 53,632 8,019 Bethlehem 63,835 4,635 Houston 25,771 2,203 Ft Lauderdale 54,235 5,967 Colombus 33,115 14,977 Philadelphia 45,994 5,390 Atlanta 40,616 5,900 Portland 21,396 3,756 San Francisco 27,539 4,327 toledo 22,373 5,967 Kansas City 20,967 6,968 Saginaw 23,145 1,298 Miami 29,064 7,046 Loma Linda 10,599 11,210 Cinci 9,582 1,516 Memphis - 5,151 $2,687,299 $411,461 Bulk = All doses sold as Bulk calibrated at expected delivery time Precal = All unit doses which exceed a 6 hour window between expected delivery and calibration time 16
  17. 17. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Analyze Basic Statistics Basic statistics were gathered on the key output to determine stability and shape. Normality test was also done on the key output which showed data as a normal curve. Summary for Average precal time June A nderson-D arling N ormality Test A -S quared 0.18 P -V alue 0.910 M ean 8.1511 S tDev 0.8384 V ariance 0.7029 S kew ness 0.223511 Kurtosis -0.483646 N 35 M inimum 6.6209 1st Q uartile 7.5226 M edian 8.0986 3rd Q uartile 8.8137 7 8 9 10 M aximum 10.0000 95% C onfidence Interv al for M ean 7.8631 8.4391 95% C onfidence Interv al for M edian 7.8038 8.5280 95% C onfidence Interv al for S tDev 9 5 % C onfidence Inter vals 0.6782 1.0985 Mean Median 7.8 8.0 8.2 8.4 8.6 The Mean of the data was 8.15 hours while the median is 8.09 which indicate both are very close to each other. Descriptive Statistics: Average precal time June Variable N N* Mean SE Mean StDev Minimum Q1 Median Average precal time June 35 0 8.151 0.142 0.838 6.621 7.523 8.099 Variable Q3 Maximum Average precal time June 8.814 10.000 17
  18. 18. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Graphical Analysis Box plot of Pre-calibration time by pharmacy showed there exists a difference between means of data between pharmacies of different size. The pre- calibration time variation among pharmacies showed there was a lack of standardized policy related to pre-calibration time. The frequency graph shows historically there have been excessive pre-calibration times practiced at multiple sites with times extending as far as midnight which accounts for substantial product decay and lost revenue. Boxplot of Average Precalibration time 10.0 9.5 Average Precalibration time 9.0 8.5 8.0 7.5 7.0 6.5 Large Medium Small Pharmacy Size 18
  19. 19. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Graph of Calibration time V/s Frequency 90 80 70 60 Frequency 50 40 30 20 10 0 10:30:00 12:30:00 13:00:00 13:15:00 13:30:00 13:45:00 14:45:00 16:00:00 23:59:00 Calibration time Hypothesis Testing Regression A Regression analysis was performed with the Key input variables and the Key output variable. The Calibration time and Expected delivery time are the critical Project Input Variable (KPIV) with the highest correlation to Average Pre- calibration time and is the root cause for pharmacies having excessive pre- calibration time. In addition some of the other critical inputs such as TRON and customer contracts were also taken into consideration for the Improve phase. A another regression equation was also tested to see correlation between Value of Tech waste due to excessive pre-calibration time and the Pre-calibration time and there exists a high degree of correlation between them. Regression Analysis: Average over 6 h versus Activity, ASP, The regression equation is Average over 6 hour Precal = - 4.99 - 0.00775 Activity - 0.31 ASP + 20.6 Calibration time - 19.3 Delivery time Predictor Coef SE Coef T P Constant -4.9892 0.4866 -10.25 0.000 Activity -0.007749 0.006703 -1.16 0.257 ASP -0.314 1.081 -0.29 0.774 Calibration time 20.556 1.088 18.88 0.000 Delivery time -19.292 1.504 -12.83 0.000 S = 0.219942 R-Sq = 95.2% R-Sq(adj) = 94.5% 19
  20. 20. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Analysis of Variance Source DF SS MS F P Regression 4 28.5317 7.1329 147.45 0.000 Residual Error 30 1.4512 0.0484 Total 34 29.9829 Source DF Seq SS Activity 1 0.1078 ASP 1 0.0673 Calibration time 1 20.3951 Delivery time 1 7.9615 Regression Analysis: Tech value o versus Average over, Activity, ASP The regression equation is Tech value over 6 hour precal = - 864 + 296 Average over 6 hour Precal + 79.8 Activity - 2450 ASP Predictor Coef SE Coef T P Constant -864 1772 -0.49 0.629 Average over 6 hour Precal 296.3 143.4 2.07 0.048 Activity 79.83 25.16 3.17 0.004 ASP -2450 5589 -0.44 0.664 S = 780.531 R-Sq = 32.4% R-Sq(adj) = 25.4% Analysis of Variance Source DF SS MS F P Regression 3 8465699 2821900 4.63 0.009 Residual Error 29 17667618 609228 Total 32 26133318 T-Test- A 2 sample T test was also conducted to test the difference between Average Pre-calibration time in June before the project was implemented and the time in October after implementation. The analysis showed that the pre-calibration time dropped by an average 1 hour with the project implementation. Two-Sample T-Test and CI: Average precal time June, Average Precal time Oct Two-sample T for Average precal time June vs Average Precal time Oct N Mean StDev SE Mean 20
  21. 21. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Average precal time June 35 8.151 0.838 0.14 Average Precal time Oct 35 7.151 0.838 0.14 Difference = mu (Average precal time June) - mu (Average Precal time Oct) Estimate for difference: 1.000 95% CI for difference: (0.600, 1.400) T-Test of difference = 0 (vs not =): T-Value = 4.99 P-Value = 0.000 DF = 68 Both use Pooled StDev = 0.8384 Results Since the p-value is less than 0.05 we reject the null hypothesis which was taken as there was no difference between the 2 populations and conclude that there is a positive difference between the pre-calibration time measured before the project was implemented to the time measured after which means the time has decreased since project implementation and this is a favorable impact resulting from the project. Project X’s Key input variables coming out of the Analyze were Calibration time – This is the time at which the requested dose is used for its intended application. The pharmacist calculates activity that needs to be filled based on customer requested calibration time. Delivery time – This is the time when the customer requests delivery of the product at it’s location. Customer Contract – Contract is a binding document between Covidien and the customer which contains information related to pricing, dose limits, calibration times and freight policy. All negotiated contracts define the long-term commitment between Covidien and its customers. TRON – The system used by the pharmacy network to carry out all operations from order processing to delivery of finished product. Since it’s a central processing system for the network, it has a significant impact on operations related to Technetium products. 21
  22. 22. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc . Improve Solutions Identified Critical inputs from the Analyze phase was taken into consideration for improvement in this phase and the “Tc Conservation program” which was launched across the network captures all the improvements which were implemented in the field to achieve the desired objective of reducing pre-calibration time and capturing lost revenue due to excessive pre-calibration time. Current issues identified with Tc-99m utilization Significant amount of Tc-99 decaying at the customer site (~6hrs) - 50% of all doses dispensed with greater than 6 hours time - much bulk-tech is ordered with pre-cal for 12:00 am - In past, customers believed “Tc-99 is cheap”, the agent is expensive - customers like security of receiving daily doses early - past delivery issues may drive behavior for this “insurance” time - lack of standardized calibration policy and no pre-calibration limits in TRON Tech Conservation - Opportunity Valuing the saved material at current ASP -> Huge $$$’s • Allows greater patient access to Tc-99m or charge for overage • With generator shortages now and in FY10, we need fast action 22
  23. 23. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc • Excellent time to begin changing market behavior • Next Steps - Meeting held with IS requesting TRON changes (80 hrs) - Roll out with Sales management on 8/10/09 - Rep and pharmacy web-training (8/28 & 8/31) - Customer letter from marketing distributed on 9/1. - Reps have customer discussions in September - Target Go Live targeted for 9/26/09 (first day of FY10) Customer Letter – Summary of Program: Customer letter will be sent on 9/01/09, which will explain…. • Ongoing concern over global Moly supply • Significant investment required to improve availability • Our goal to increase patient access to Tc-99m based products • Covidien committed to servicing maximum number of patients • New conservation programs beginning on Sept. 26th, 2009(est.) • To promote lower pre-calibration times, Covidien will charge for pre-calibration activity beyond six hours • New charges will be based on bulk Tc-99 pricing • Bulk Tc-99 will be calibrated for actual delivery time, unless customer chooses to pay for additional activity Example #1 - Limit pre-calibration to 6 hours (expected delivery to cal time) Example: Customer orders 30 mCi of Sestamibi calibrated for 1300, but wants it delivered with an expected delivery time of 0600. This would trigger a charge up, using the R005A0 code and decay factor to charge them the customer’s bulk Tc-99m charge (say $0.31/mci). We would charge the customer for the hours beyond the 6 hr limit. In this case, it is one hour, so we would 23
  24. 24. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc charge the customer $0.31 x (30mCi/.891)-30 mCi = $1.14 using a new item code R005PC – TC-99M PRE-CAL OVER TIME LIMIT which will be attached to the dose for reimbursement Bulk tech will no longer be calibrated past delivery times. Customer Impact Analysis - example This is a calculation of the new cost to each customer if they do not change. Example from the month of June: Customer Name Tech Value over recommended qty Tech value over 6 hrs pre-cal at ASP ALBERT EINSTEIN MED CTR Total $0.00 $1,826.52 ATLANTIC MEDICAL IMAGING,GALLOWAY,NJ Total $0.00 $31.39 BOOTH RADIOLOGY Total $0.00 $98.36 CAPE MAY COURT HOUSE AMI Total $0.00 $23.97 CARDIAC DIAGNOSTIC CENTER,LEWES,DE Total $0.00 $0.96 CARDIAC DIAGNOSTIC CENTER,MIDDLETOWN,DE Total $0.00 $32.96 CARDIAC DIAGNOSTIC CENTER,WILMINGTON,DE Total $0.00 $2.04 CARDIOLOGY MEDICAL ASSOC,PHILADELPHIA,PA Total $0.00 $45.27 CARDIOVASCULAR SOLUTIONS,PHILADELPHIA,PA Total $0.00 $116.50 CHERRY HILL CARDIAC DIAGNOSTIC Total $0.00 $111.73 COMM MED CTR Total $134.85 $473.56 DOVER CDC,DOVER,DE Total $1.55 $1.15 EINSTEIN CTR ONE RADIOLOGY Total $1.55 $84.30 KIMBALL MEDICAL CENTER,LAKEWOOD,NJ Total $0.00 $1,690.34 LINWOOD AMI Total $0.00 $46.51 METHODIST HOSPITAL,PHILADELPHIA,PA Total $0.00 $313.70 MOSS REHAB/EINSTEIN AT ELKINS PARK Total $0.00 $371.03 NORTH WILMINGTON CDC Total $0.00 $2.04 PAMI Total $0.00 $59.12 RADIOLOGY ASSOCIATES,WILMINGTON,DE Total $0.00 $11.92 SOUTH JERSEY HEART GROUP,SEWELL,NJ Total $0.00 $1.97 THOMAS JEFFERSON U HOSP Total $0.00 $185.50 UNION HOSP OF CECIL CNTY Total $0.00 $1,343.71 WACHSPRESS & SHATKN CARDIO, VINELAND, NJ Total $0.00 $27.97 24 Grand Total $139.50 $7,013.32
  25. 25. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc 25
  26. 26. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Sales Rep Actions & Timeline: • Study the customer letter and understand the key points • Review the customer impact analysis with Regional manager • Work with pharmacy manager • Develop preliminary plan using customer knowledge to minimize customer impact • Shift doses to later deliveries, while minimizing impact on delivery costs • Visit with customers • Provide the customer a choice to remain with the status quo or shift schedule • Finalize a plan for each customer with your pharmacy • Understand changes to invoices • Corporate will add pricing for bulk tech at rep guideline for customers not having this item code currently on their contract 8/28 or 8/31 Wk of 8/31 Wk of 9/7 Wk of 9/14 Wk of 9/21 Sat. 9/26 - Meet with customers Attend - Meet with customers Finalize plan with -Target Go Live Web- Meet with Reg. Mgr pharmacy training Meet with pharmacy team Review customers Develop plan 26
  27. 27. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Sales Rep talk track: • Reference the customer letter • Explain that Tc-99m shortage will continue far into 2010 • Our focus is on increasing patient access to Tc-99m products • Asked your customers to use Tc-99m more efficiently • Explain how doses can be delivered on later runs If they purchase bulk tech, explain new pre-calibration policy Give them their options for: 1 Unit doses with excess (> 6 hours) of calibration a.) Move doses to later scheduled runs b.) Pay for extra activity 2 Bulk Tc-99m orders a.) Move orders to later scheduled runs b.) Reduce sizes of bulk Tc-99m orders c.) Pay for extra activity d.) Eliminate bulk Tc-99m orders and utilize STAT unit dose orders where needed. • It’s the customers choice • Use your Tools -> Moly calendar, delivery schedules, customer impact analysis, Moly shortage update letter Pharmacy Managers Actions: • Study the customer letter and understand the key points • Review the customer impact analysis with your sales team • Develop a preliminary plan to minimize customer impact • Ensure you are capturing accurate expected delivery times in TRON for all customer orders. • Consider shifts in delivery schedules without increasing costs • Utilize existing scheduled runs and routes • If freight costs exceed Tc-99m savings, exceptions approved by VP Ops, VP of Sales, and Marketing through email. • Make certain to capture freight revenue for these exception accounts • Understand changes to TRON and invoices • Finalize a plan for each customer • On Go Live date, begin calibrating all bulk tech at delivery time • Note that corporate will add pricing for bulk tech at rep guideline for customers not having this item code currently on their contract 27
  28. 28. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc TRON Invoice change? Invoices will show the added Tc-99 charges similar to I-131 caps. Invoices will reflect any necessary surcharges on the line following the applicable dose, assigned to the same prescription number for reporting ease. The following product codes and descriptions will appear to indicate the surcharge type(s): R005PC - TC-99M SODIUM PERTECHNETATE ACTIVITY OVER PRE-CALIBRATION TIME LIMIT (PER MCI) 28
  29. 29. improvetechprecalibrationprojectbook-12584117210388-phpapp01.doc Control The updated process flow shows the changes made to the TRON pharmacy system to generate invoices capturing the surcharge for products exceeding the 6 hour pre-calibration limit. Updated Process Map Updated Process Flow Map for Tc 99 Precalibration Pharmacy receives Customer Standing / Calibration time, Demand Order for Tech dose Delivery time Pharmacist enters order in TRON and generates prescription Dispense Dose with activity related to Check calibration time Order Is dose matching with BOL NO YES Tech dose is ship confirmed Is unit dose over 6 hour pre - YES calibration?Bulk dose over delivery time? Excess surcharge billed to NO customer invoice Dose delivery to customer at requested delivery and calibration time Pharmacy Invoicing $$ Pharmacy bills customer and collects sales revenue for dose sale Exception sent to Pricing for adjustment and surcharge Pharmacy adds new customer , negotiates adjusted to $0 existing customer contracts , communicates any change in policy or contract Exception raised if freight revenue exceeds Tech Customer changes delivery schedule , savings adjusts calibration times to adhere to Tech Conservation program 29