Post on 19-Jul-2016
description
NCE/
P2P HR P2P HR
Basic Problem Solving ProjectBasic Problem Solving ProjectBASICBASIC
Streamline P2P S&IM process flow and ensure compliance to standards
NCE/
Objective:Objective:Improve Satisfaction level for « Procure To Pay » Chapter with 3% (2012 December results)Improve « PO before invoice » with 5% (2012 results)
Problem Statement – check slide 4 and 5.1 Domain of application:
All sites using S&IM Procurement flow.
Timing:
Measures:Internal Satisfaction Survey, PO after invoice, PR’s in error, Efficient & In time training sessions
Impact:
To all of the stakeholders involved (requestors, approvers, buyers, including super users and vendors) affecting compliance to standards.
2
4
6
14/11/11
10/02/11
Streamline P2P S&IM process flow & ensure compliance to standards
16/12/11 Expected Benefits:
- KPIs in target. - Increase the satisfaction of all stakeholders. - Endorse the compliance to standards.
3
5
7
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01/10/1109/03/12
Problem
Describe the problem with 5W1H and Is & Is not
P2P S&IM Flow UsageP2P KPI (S&IM Scorecard) with low results
A HR problem, organizational or people development issueA system (SAP) issueJob description issue or empowerment
What / which
S&IM flows, ALL sites (Bucharest, Timisoara, Clinceni, etc.)
Direct materials flows, Bucharest and Timisoara, including FERTs
Customers, Operational Buyers
Where
End to end P2P + AP + DSDMWhen
Who
IS IS NOT
Requesters, Approvers, Super Users, Vendors
How muchHow many
86% POs before invoice (vs 90% target) 78 PRs in error in 5 months (~ 16 per month) 10% of daily time spent to clarify different errors/situations
Problem
Problem Statement
After 4 years of working in GLOBE environment and regular knowledge refreshment sessions, the S&IM P2P flow is still considered to be slow and inefficient.
This is correlated with daily agents’ performance, despite ongoing training and support and it is affecting response time and service provided to the business, generating poor KPIs and time waste.
Misuse of GLOBE, bypassing SAP and procedures maintains poor satisfaction level for all involved agents (requestors, approvers, buyers, including super users and vendors).
NCE/
SOURCE and DATA of the Problem
Source of Problem: % of S&IM PO lines created before invoice date
Data Description: Data of PO < Invoice from January to September 2011 vs target
April 28, 2023NCE/ 7NCE/
SOURCE and DATA of the Problem
Source of Problem: Number of PRs in error
Data Description: Data of PRs in error from May to September 2011 vs target
Target 2011: 0 errors
Estimated solving time workflow:
• 30 min per agent for every error
• 5 days per month (avg. 16 errors)
8
2010 ZEUR Procurement Customer Satisfaction Questionnaire
Total Score - 0.35 difference Indirect NRO vs. BIC (5,8% of BIC)
Procure to Pay Chapter - 0.26 difference Indirect P2P efficiency (5% of BIC)
SOURCE and DATA of the Problem
Team Leader
Team Leader leads the team, manages team dynamics & team’s progress against schedule. Serves as a liaison with Sponsor, Coach and Process Owner. Maintains records & documentation.
Name & FunctionRinela Ivan
Junior L&T Specialist
Name & Function
Elena Manea
P2P Stream Leader
Coach gives expert guidance and coaching to the team on the DMAIC tools and cycle.
Name & Function
Dana Calea SC GA Lead
Process Owner is the customer of the project who the team needs to delight.
Name & Function
Name & Function
Team MemberName & Functions
Ana BotaSuper User P2P & Operational Buyer
Sponsor Approves project and scope. Accountable for the project result. Reviews progress (tollgates). Removes roadblocks for the team. Celebrates the project’s conclusion.
Name & Function
R. SecretianuProcurement Head
Name & Function
Irinuca DobreProcurement Excellence
Name & Function
Resource might be called upon by the team for additional information & expertise.
Project Team Profile
Name & Function
Oana NegreaStrategic Buyer & Former SU P2P
Excellence
Resource might be called upon by the team for additional information & expertise.
Resource might be called upon by the team for additional information & expertise.
R. SecretianuProcurement Head
Team MemberTeam Member
Florin AgacheMarket Role Coordinator
Excellence
DE cautat datele si trimis Dana
Project Plan
NCE/
13
The Procurement Process Process Purchase Orders
Contract on paper/Create new vendorRequest form
Identify Requirements Vendor
determinationPurchase
Order
GoodsReceipt
Vendor delivery
Invoice
Invoice verificationPayment
PR – purchase requisition
ReleasePurchase
Order Approval v
ia workflo
w
3 way m
atch
PO-GR-In
voice
COR = centralisedOffer report
NCE/
M1: Data collection breakdown process Basis of Data Collection Leading to Focused Problem
1. Creating &
approving PRs
2. Creating POs
Total PRs by type of error
Total PRs in error
per month Total PR’s by department
Average approval time per
approverPRs not approved
within SLA
PR – purchase requisition
Purchase Order
Average approval time per
department
PO before invoice
date
PO before invoice per
requester
PO before invoice per PG
PR w/o
documentationApproved PR without
support documentation
PO number on invoice
4. Knowledge
Transfer ProcessTrainingDelivery
Globe End User Training
Planning Effectiveness
NCE/
M2: Data Collection PlanCollect Data following the CTQ Flowdown Draft (from Big Y to small y)
What to measure (WHAT)
Measuring Unit
Where to measure (WHERE)
Sample (WHEN)
How to collect(HOW)
Why this data is needed(WHY)
Person in charge(WHO)
DPA M-1 Percentage % In SAP BW report “DPA
with Lags Shelf Stable”
Monthly, every 3rd of the
month to get the previous
month’s result
MFR agreed and Orders are
downloaded and DPA is
calculated as 1- (abs(MFR-Order)
MFR)
To determine the accuracy of the
forecast from last month vs actual
orders
B/W report based.
Report is extracted from SAP
by Michelle Santos
Main category of DPA misses
PUM DPA Consolidated
Report
Every month after the B/W
snapshot
Top 80% DPA misses per BU
is tagged by the BU Demand
Planner
To identify the cause of the DPA
miss
Demand Planner: Ann Dela
Pena
Subcategory of DPA misses
PUM DPA Consolidated
Report
Every month after the B/W
snapshot
Top 80% DPA misses per BU
is tagged by the BU Demand
Planner
To provide more detail on the main reason code for the DPA miss
Demand Planner: Ann Dela
Pena
DPA misses with BU and
Brand details
PUM DPA Consolidated
Report
Every month after the B/W
snapshot
The report is generated
indicating the BU and Brand group per sku
For the demand planners to easily filter their Brand
and BU
Dev’t team Michelle Santos
April 28, 2023NCE/ 16NCE/
M2: SOURCE and DATA of the Problem (Big Y)
Source of Problem: % of documents not approved in SLA
Data Description:
Values
Row LabelsAverage of Approval time in days Count of Document number
ROBETAAN 4 1ROBUSUCR 3 1ROBUSUMI 6 13RODIMITRLA 3 9RODOXANCR 4 6RODUMITRMA 6 4RONEAGOEST 8 6RONUBERPA 3 5ROSERBANRA 5 2ROSTANCAAN 7 1ROSTOIANCR 4 2ROWATSONDA 3 1ROWATSONST 4 22Grand Total 4.5 73
2008
Data from September to November 2008
April 28, 2023NCE/ 17NCE/
M2: SOURCE and DATA of the Problem (Big Y)
Source of Problem: % of documents not approved in SLA
Data Description:2009
Values Row Labels Average of Approval time in days Count of Document numberROBETAAN 6 11ROBUSUMI 5 8RODIMITRLA 12 3RODOXANCR 5 6ROHEPESCO 33 2ROMAGALINI 4 18RONANCUDA 4 5RONEAGOEST 4 2ROPOULIQMU 3 1ROREBERJA 3 6ROSERBANRA 4 2ROSTANCAAN 11 3ROSTOIANCR 9 20ROWATSONST 4 15Grand Total 6 102
Data from September to November 2009
April 28, 2023NCE/ 18NCE/
D2: SOURCE and DATA of the Problem (Big Y)
Source of Problem: % of documents not approved in SLA
Data Description: Values
Row Labels
Average of Approval time in days Count of Document number
ROBETAAN 7 26ROBORTEARO 5 1ROBUBENDTH 4 3ROBUSUCR 3 3ROBUSUMI 5 15ROCHOINSKA 3 11RODIMITRLA 3 2RODOXANAN 3 4RODOXANCR 3 10RODRAGOMAL 4 2ROERCEANDA 3 2ROFARTUSDO 3 1ROGEORGECR 5 10ROGHINEAMA 6 1ROHEPESCO 5 2ROIACOBNI 9 1ROILIESCO 4 10ROLEDESEOD 4 21ROMAGALINI 4 14RONANCUDA 5 17RONEAGOEST 4 49RONUNEZVI 4 27ROREBERJA 3 2ROSERBANRA 4 17ROSTOIANCR 3 7ROVETISAAN 6 11ROWATSONDA 4 8ROWATSONST 7 8ROZOGOPODI 4 1Grand Total 4 286
2010
Data from September to November 2010
April 28, 2023NCE/ 19NCE/
D2: SOURCE and DATA of the Problem (Big Y)
Source of Problem: % of documents not approved in SLA
Data Description:2011 Values
Row Labels Average of No.of days Count of Doc numberROALEXANST 3 1ROBETAAN 6 10ROBUSUMI 5 18ROCARELLGA 3 2ROCHOINSKA 3 2ROCONSTAAL 5 8RODIMITRLA 5 2RODOXANAN 4 20RODOXANCR 6 1ROERCEANDA 4 9ROFOTABO 3 2ROGATALI 10 1ROGHINEAMA 4 1ROHEPESCO 3 1ROLEDESEOD 4 25ROLEPADAAL 4 1ROMAGALINI 3 4RONUNEZVI 5 8ROPOSTELIO 5 9ROREBERJA 3 2ROROCAVI 4 5RORUSUEL 3 3ROTUNARUOV 4 15ROWATSONDA 3 3Grand Total 4 153
Data from September to November 2011
April 28, 2023NCE/ 20NCE/
D2: SOURCE and DATA of the Problem (Big Y)
Source of Problem: Nr. of PRs in error by Department
Data Description: Data from July 2011 to January 2012
April 28, 2023NCE/ 21NCE/
D2: SOURCE and DATA of the Problem (Big Y)
Source of Problem: % PO after invoice
Data Description: Data collected for 2011 in COM box
April 28, 2023NCE/ 22NCE/
D2: SOURCE and DATA of the Problem (Big Y)
Source of Problem: % PO after invoice by Requester
Data Description: Data collected for 2011 in MAN box
April 28, 2023NCE/ 23NCE/
D2: SOURCE and DATA of the Problem (Big Y)
Source of Problem: Difference in days between the moment of hiring vs. role allocation vs. training finalization
Data Description: Data collected for August – November 2011
0
10
20
30
40
50
60
Aug-11 Sep-11 Oct-11 Nov-11
Globe End User Training Planning Effectiveness
Difference between hiring/ change of position date and date of role allocation
Difference between the date of role allocation and date of training finalization
NCE/
M3: CTQ Flowdown - 1st Drill DownFrom DPA M-1 to DPA M-1 Main Reason codes
52%
18%
12% 11%
5%1%
NCE/
M3: CTQ Flowdown - 2nd Drill DownFrom DPA Main Reason codes to Sub Reason Codes
63% 37%
NCE/
M3: CTQ Flowdown - 3rd Drill DownFrom Sub Reason Codes to BU contributors
DPA misses in cases 01.2011 02.2011 03.2011 04.2011 Grand Total Ice Cream 371,321 618,305 989,626 Chilled 171,969 202,900 52,178 175,697 602,744 Beverage 86,102 30,806 79,536 115,350 311,794 Liquid Beverage 14,440 93,820 58,312 82,681 249,253 Nestle Professional 10,671 36,195 18,892 22,248 88,006 Coffee 62,167 62,167 Nutrition 3,480 1,301 8,400 13,181 Breakfast Cereals 1,006 3,578 511 5,095 Petcare 408 3,506 3,913 Healthcare Nutrition 114 114 Grand Total 658,990 983,734 274,664 408,506 2,325,894
Top candidates for the DMAIC would be Chilled and Beverages.
Beverages becomes a more ideal candidate due to the following reasons:
1.Impact on Finished Goods and Raw Materials is much more evident (Covers and Freshness)
2.Chilled is planned daily and weekly, thus, more frequent reviews
3.MBP is already in place in Beverages, thus, it should lead to less errors in forecasting
NCE/
M3: CTQ Flowdown - 4th Drill DownFrom BU down to Brand level
Best candidate is Nestea as it shows consistent over forecasting month on month and the value is increasing.
Drilling down further, this is mainly due to Nestea Lemon Litro Pack.
BRAND Material Desc 01.2011 02.2011 03.2011 04.2011Grand Total
Nestea NESTEA Lemon Litro Pack MP12(12x45g) PH 39,080 79,536 52,927
171,543
NESTEA Lemon Litro Pack 72x45g PH 30,806
30,806
Nestea Total
39,080
30,806
79,536
52,927
202,349
MILO MILO ACTIGEN-E High-Malt 42(12x20g) PH 47,022 62,422
109,445
MILO Total
47,022
62,422
109,445
Grand Total
86,102
30,806
79,536
115,350
311,794
M4: Profile of the Focus Area
From September 2010 to April 2011, Nestea has not hit the DPA target of 82%
6 of 8 times, the bias has been positive, over-forecasting
Target 82%
M4: Profile of the Focus Area
Monthly DPA is low averaging 77% vs target of 82%
Target: 82%
M4: Profile of the Focus Area
Weekly DPA performance is also low averaging 47%, ranging from -59% to 96%
Ave: 47%
M5: Understanding the Focus Project Y
a)What is the Operational Definition of “y” (including formula)?y is DPA of Nestea Litro – Demand Plan Accuracy. This compares the
forecast versus orders. The formula is defined as: 1- (MFR Agreed M-1 – Orders) MFR Agreed M-1
Bias is an indicator of over-forecast and under-forecast. Positive bias is over-forecasting, while negative is under-forecasting. This is defined as:
(MFR Agreed M-1 – Orders) MFR Agreed M-1
b) What is the Performance Standard of “y” (how does the customer want y to perform)? Includes Lower & Upper Limit and Target value as appropriate.
y should be between 82% to 100%Target y is 82%
PROBLEM Statement (purpose): 5W1H
GOAL Statement: SMART“S“Specific but Stretching pecific but Stretching MMeasurable easurable AAgreed, aligned, achievable & attractive greed, aligned, achievable & attractive RRealistic & Relevant ealistic & Relevant TTime boundime bound” ”
One of the main contributors to DP misses in 2011 is the consistent over-forecasting of Nestea Lemon Litro in the Beverages BU. From January 2011 to April 2011, Nestea Lemon Litro has been consistently over forecasted , averaging only 77% DPA M-1 with a Bias of +15%
To increase Nestea Lemon Litro DPA M-1 from the average of 77% to 82% from June 2011 onwards (5% incremental, 7% increase vs base of 77%).
A 5% increase in DPA of Nestea Litro would result to 4.5% increase in Beverages DPA and 0.4% increase in National DPA.
This would bring Bev DPA from 74% to 78.5% DPA and National DPA from 72% to 72.4% DPA.
M6: Project Charter – Focus Area
Note: Once the GOAL is set, ALIGN this to the Goal Statement in DEFINE.
NCE/