Basic Dmaic Hr_p2p v1

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

__/__/__

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 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)


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



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


Report


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


Report


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


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


M2: SOURCE and DATA of the Problem (Big Y)


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



D2: SOURCE and DATA of the Problem (Big Y)


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 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




Source of Problem: Nr. of PRs in error by Department

Data Description: Data from July 2011 to January 2012



Source of Problem: % PO after invoice

Data Description: Data collected for 2011 in COM box



Source of Problem: % PO after invoice by Requester

Data Description: Data collected for 2011 in MAN box



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%


Monthly DPA is low averaging 77% vs target of 82%

Target: 82%


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/

Basic Dmaic Hr_p2p v1

Documents