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Moving Beyond Physical Inventory. Mike Hadley CPPS, CPIM The Boeing Company March 24, 2011 Originally presented at the NPMA National Conference June 6, 2007. Purpose of this Presentation.

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Moving beyond physical inventory

Moving Beyond Physical Inventory

Mike Hadley CPPS, CPIM

The Boeing Company

March 24, 2011

Originally presented at the NPMA National ConferenceJune 6, 2007


Purpose of this presentation
Purpose of this Presentation

  • Present a suite of tools/methods that are available for inclusion in an inventory record accuracy (IRA) program.

  • Present the concept and process behind an inventory quality (IQ) program.

  • Encourage open discussion among the participants regarding the methods presented, other methods used by the participants, successes, and challenges.

  • Initiate on-going discussions relative to documenting best practices into a standard.


Agenda
Agenda

  • Inventory Record Accuracy

  • Inventory Quality

  • Oversight Structure and Corrective Action

  • Open Discussion Forum



Ira inventory record accuracy tools methods
IRA: Inventory Record AccuracyTools - Methods

  • Random/Statistical Count – provides process health visibility

  • ABC Cycle Count – perpetual inventory with frequency based on risk

  • Inventory by Exception – leverages daily business “touches” as assumed counts

  • Opportunity Count – count when issued or touched for other reasons

  • Baseline or Wall-to-Wall Count – clean-up or correcting action

  • Control Group Count – focuses on known problem areas

  • Audit Count – audit the auditors

  • Floor-to-Record and Record-to-Floor Count – both are required

  • Hit or Miss Tolerances – industry standard practice for determining accuracy, based on material risk


Ira random statistical count
IRA: Random/Statistical Count

  • Indicates the overall process/system accuracy

  • Based on accepted statistical practices for:

    • determination of the sample size

    • generation of the random population

    • initiation of additional samples based on the error rate

    • determination of process capability and trends

  • Utilize non-declining population

  • Consider establishing multiple populations if the supporting processes are disparate.


Ira random statistical count population considerations
IRA: Random/Statistical CountPopulation Considerations

  • Government property vs. Company property

  • Raw material vs. parts

  • Conventional storage vs. ASRS storage vs. Outside (yard) storage

  • Warehouse storage vs. line-side storage

  • Shelf life material vs. Hazardous material vs. Standard material

    Where is the “system” different? Where would additional insight to its behavior would be useful?


Ira random statistical count sample error vs sample size
IRA: Random/Statistical Count Sample Error vs. Sample Size

  • Industry Standards*

    • .01 for quality assurance, health care products, flight safety parts, etc. (health/safety)

    • .05 level is selected for consumer research, product testing, brand preference, etc. (dollars)

    • .10 for political polling, opinion polls, etc. (opinions)

Sample size calculations** (Population ~= 50,000):

  • 99% confidence level @ +/-3: sample = 1787

  • 95% confidence level @ +/-6: sample = 265

  • 90% confidence level @ +/-9: sample = 84

*Cooper, D., Schindler, P. (2003). Business Research Methods 9e. McGraw-Hill, 2003. New York, NY.

**MaCorr Market Research Online Sample Size Calculator. Member of ESOMAR, World association of research professionals. 2005.


Ira abc cycle count
IRA: ABC Cycle Count

  • Utilizes a declining sample population over an extended period of time

  • Acts as a perpetual baseline inventory

  • Frequency is based on such considerations as cost, activity, or financial or operational risk :

    • Stratification A items = most frequent (i.e. quarterly, semi-annual, annual, etc.)

    • Stratification B items = moderate frequency (semi-annual, annual, bi-annual, etc.)

    • Stratification C items = low frequency, or via statistical sampling (semi-annual, triennial, or via sampling)


Ira abc cycle count frequency considerations
IRA: ABC Cycle CountFrequency Considerations

  • Cost

  • Availability or lead time to replace

  • Criticality to the production process (critical path)

  • Level of unplanned demand (scrap, spares, “as required” usage)

  • Short or long shelf life

  • Ease or difficulty to maintain accuracy (i.e. raw material)

  • Storage location (warehouse, yard, line-side)


Ira inventory by exception or cyclical inventories
IRA: Inventory by Exception or Cyclical Inventories

Based on physical or electronic “touches” of the subject population as part of the normal production/use process. Any item not touched in a given period is inventoried.

“Electronic touches” are where the property’s physical existence is verified via an electronic signal, such as:

  • Signal from an active RFID tag

  • Reading a passive RFID tag as material passes through a portal

    Physical touches include:

  • Moves

  • Production operations


Ira opportunity count
IRA: Opportunity Count

Occurs when an event triggers a convenient opportunity to verify inventory accuracy.

An example would be to verify the records:

  • As the last item is issued, and the bin balance is zero

  • During shelf life updates

  • Battery changes for active RFID


Ira baseline or wall to wall count
IRA: Baseline or Wall-to-Wall Count

Touches all inventories over a designated period of time.

A wall-to-wall typically is done in a relatively short timeframe and within a contained area in order to re-establish an inventory baseline.

It is necessary to apply a baseline or wall-to-wall inventory when:

1) correcting a previously identified process error, in order to “clean-up” the suspected population, or

2) when the random count method indicates that the process to maintain accurate inventories is “out of control”.


Ira control group count
IRA: Control Group Count

Utilized for root cause analysis and problem resolution. It is performed by:

  • Establishing a control group population consisting of a representative sample of the targeted problem area.

  • This control group of items is counted repeatedly, on a frequency based on the activity level of the items.

  • At each count the observations are recorded; however, adjustments are not performed on the records.


Ira control group cont d
IRA: Control Group (cont’d)

  • The purpose is to observe the behavior of the system, and not to interfere with that behavior.

  • The control group count frequency should be established to allow for timely investigation of suspected errors.

  • The duration of observation of the control group should be long enough to allow a number of complete cycles of the item life-cycle (receive, store, issue, return).

  • Once the duration of the observation period has been completed, any necessary records adjustments can be performed.


Audit count
Audit Count

  • Performed on previously completed inventory accuracy counts.

  • Verifies the accuracy of the sampling and counting method itself.

  • To be effective, the audit count must be:

    • Performed shortly after the completion of the targeted inventory count

    • Executed by a different individual

    • Based on random sampling for both timing and sample selection


Ia floor to record count vs record to floor counts
IA: Floor-to-Record Count vs. Record to Floor Counts

  • Record to Floor = Count generated from system records

  • Floor to Record = Count generated from physical location

Need to apply both a record to floor sampling, as well as a floor to record sampling.

  • Record to floor only sampling could miss items that are in storage, but not on record.

  • Floor to record only sampling could miss items that are on record, but not in storage.

    All of the above Inventory Record Accuracy (IRA) methods can be applied to either sampling method.


Ira applying tolerances
IRA: Applying Tolerances

  • When determining inventory accuracy percentage, it is common practice to apply tolerances to the counts in order to determine a “hit” or “miss” count.

  • Tolerances are determined by the risks to the process associated to the items being counted.

    • Financial risk based on item value,

    • Operational risk based on criticality of the item or lead time to replace the item.

  • A typical tolerance scheme would be:

    • Stratification A items = 0% or 0%

    • Stratification B items = 5% or 3%

    • Stratification C items = 10% or 5%



Iq focus on operational effectiveness
IQ: Focus on Operational Effectiveness

  • Inventory accuracy (see prior slides)

  • Shelf life

  • Serialization

  • Ownership Identification

  • Storage

  • Packaging

  • Labeling

  • Fire code compliance

  • Condition code tag matches record




Iq example of quality checks
IQ: Example of Quality Checks

ASRS bin quadrant Flue space clear

ASRS bin clearance Sprinkler clearance

ID Labels available to issue Serviceability tag

Package labels facing forward Multi-piece labeled

Packaging secure Shelf life date, labeled

Packaging – protective bag Box/package damage

Free of trash & debris Part numbers segregated

Stacking appropriate



Notes on oversight structure and corrective action

Notes on Oversight StructureandCorrective Action


Sample oversight structure

Control Group

Wall-to-Wall

StatisticalAudit

CycleOpportunity

Exception

Sample Oversight Structure

External Oversight

FAR Rqmts. (annual)DCMA

MMAS (bi-annual)DCAA

SOX (annual)Deloitte & Touche

AS 9100 (annual)NQAR

Internal Oversight

Quality Dept. Internal Audits FAR Rqmts. (annual)

Internal Audits - Processes (As required or requested)

Government Property PCAB*

Materials Management PCAB*

M.M. Core Inventory Audits

Procedure/Process Reviews & Audit(tri-annual)

Process Owner Business Process Audits (weekly/monthly)

Process Control Point Metrics (daily/weekly/monthly)

Matl. Mgmt. Core

Gvmt. Prop. Mgmt

Quality

Functional OrganizationMaterials Management

DCMA = Defense Contract Management Agency MMAS = Material Management and Accounting Standards DCAA = Defense Contract Administration Agency SOX = Sarbanes-OxleyNQAR = National Quality Assurance Representative

*PCAB = PCAB = Preventative and Corrective Action Board


A word on corrective action
A Word on Corrective Action

  • Three elements of Corrective Action – The 3C’s:

  • Containment – “Stop it”

    Isolate all of the suspect items in order to stop additional escapements or issues.

  • Correcting Action – “Clean it up”

    Clean up all past occurrences of the discovered issue, and bring all items to an acceptable state.

  • Corrective Action – “Fix the process”

    Eliminate future occurrences of the same or similar condition.


Applying the tools methods
Applying the Tools/Methods

  • Process Oversight:

    • Statistical Count

    • ABC Cycle Count

    • Inventory by Exception

    • Opportunity Count

    • Audit Count

  • Root Cause Analysis for Corrective Action:

    • Control Group Count

  • Correcting Action (Clean-up):

    • Baseline/Wall-to-Wall Inventory




Questions discussion1
Questions? Discussion?

  • Who is utilizing active RFID as an inventory accuracy tool? Have you designated “pinging” from an active RFID tag as inventory accountability?

  • Who has utilized passive RFID logging through a portal as inventory accountability?

  • What other opportunity count triggers do you utilize?

  • Any experiences with control group cycle counts as a root cause analysis tool?

  • Any other tools or methods that you have utilized for inventory accuracy, or inventory quality?

  • Are these tools and methods the right ones for consideration in developing an inventory accuracy standard?


References
References

  • FAR 45.508 -- Physical Inventories

  • DFARS 252.242-7004 Materials Management and Accounting System (MMAS)

  • DoD 4161.2-M C3. 1 1. Physical Inventories of Government Property

  • Inventory Record Accuracy, Unleashing the Power of Cycle Counting; by Rodger B. Brooks & Larry W. Wilson, 1993.

  • Fundamentals of Personal Property Management; by the National Property Management Association, 2006.

  • Business Research Methods 9e; Cooper, D., Schindler, P. McGraw-Hill, 2003. New York, NY.

  • MaCorr Market Research Online Sample Size Calculator. Member of ESOMAR, World association of research professionals. 2005.www.macorr.com


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