modern demand planning history, objectives, methods, and implications presented by: william h....

Modern Demand Planning

History, Objectives, Methods, and Implications

Presented By:William H. (Will) Benton

Vice President of Development & Professional ServicesGAINSystems, Inc.

Phone (630) 505-3030; E-Mail: [email protected]

Presented to APICS Chicago

September 17, 2002

WWW.GAINSystems.net© 2002 GAINSystems, Inc.; 10201-01c; APICS Chicago Sep 2002 Modern Dmd Plng Pres V03.ppt; 091702

2

Demand Planning: History & ‘Founding Fathers’

Approx

Date Contributing

Statistician(s) Background/Contribution Legacy in Modern

Operations

1200 Fibonacci

1) Introduces some of the first European computations based on our current Hindu/Arabic numbering system (base-10 including 0); moving from Roman numerals

2) Develops Fibonacci series [each successive number is sum of two preceding numbers (e.g., 1, 2, 3, 5, 8)]; divide any number by its precedent & quotient equals the Greek's 'Golden Mean' of ~1.6 (same ratio as human body from below-to-above the navel & length of each of 3 subsequent finger bones)

Interest payments including concept of inventory carrying costs

1545 Cardano

1) Establishes initial understanding of decision-making under uncertainty via probability assessment of equally-probable possible combinations (e.g., of two, 6-sided dice)

2) Laments the fact that his work contributes a great deal to understanding and very little to practical application; encourages others to pragmatic work

Combination theory including scenario planning

1715 Jacob & Nicolaus Bernoulli

1) Establish concept of assessing probability from a sample of data rather than from a fixed number of potential outcomes

2) First attempt to discern 'moral certainty' or degrees of belief from a sample set of data (e.g., number of standard deviations and confidence intervals); measuring uncertainty

Initial steps toward managing service levels

1735 Abraham de Moivre

Publishes "Doctrine of Chances" that defines processes for speculating on the population based on extrapolations from observations of sample sets

Statistical inference (e.g., mean-time-between failure & lead-time variability)

1765 Thomas Bayes "Essay Towards Solving A Problem In The Doctine of Chances" develops an approach for regenerative analysis in light of new information (i.e., no one approach is applicable for the long term)

Regenerative demand planning (i.e., changing former forecasts in light of new information)

1810 Carl Friedrich Gauss

Establishes the concept of the normal or 'bell' curve providing tools to indicate not accuracy but error

Completes process for managing service levels; basis for quality control (e.g., 6-Sigma)

Early 1900s

multiple statisticians

Develop process to divide, via multiple regression, demand into base, trend, and seasonality for forecast modeling

Basis of Many current (but data-intensive) models

1960 Robert Brown Invents exponential smoothing; first quantitative model for sales forecasting that is feasible to implement and attempts to combine all three factors above

Basis for many current practical models

1971 William C. Benton

Develops tool proving that applying multiple families of forecasting models rather than one, comprehensive model, provides more plausible & manageable forecast for demand and inventory planning over the complete life cycle of the item

Basis for GAINS and its Inventory Chain Optimization philosophy

Mysticism Begins Yielding to Science & Logic ~1300AD with Renaissance/Reformation*

* For an excellent survey of the development of statistics/forecasting (including above) see “Against the Gods” by Peter L. Bernstein


3

Key Term Definitions

• Demand Planning Optimization: Prioritizing demand planning improvements based on added value vs. organization cost/time & implementing those within budget (based on priority)

• ‘Forecast’ Types in a Corporate Context– Forecast: Projection of customer* demand by week/day in the near-term (e.g., 6 months)

and by month for the longer term (e.g., 12-24 months)– Schedule: Current projection of replenishment orders by day through manufacturing/order

time fence over which orders cannot be changed without significant cost; often adjusted for constraining factors (e.g., manufacturing capacity or budgetary constraints)

– Plan: Longer-term (e.g., 12-month) plan of replenishment orders by week/month often not constrained for near-term constraints

• Planning Processes-related Terms– Deterministic: Based on known events (e.g., customer commitments*); common to

industries where maximum cumulative supplier lead time may be passed to customer (e.g., pure Make-to-Order)

– Stochastic: Events predicted, predictions are inherently errant; required where customers expect delivery within cumulative supplier lead time or process time (i.e., in cycled production when economics force infrequent production runs as seen in industries such as printing or glass)

• Collaboration-related Terms– Intra-Enterprise: Among departments within a single organization company (e.g.,

marketing, finance, operations, and/or sales)– Cross-Enterprise: Across organizations (e.g., supplier and customer)

* Notice the use of “commitments” rather than ‘orders’ as orders are often not know events (e.g., due to customer changes to quantity or date)


4

Forecast ‘Customers’ & Objectives

Forecast

‘Owners’ (i.e., Groups) Objectives/Focus Data Required by Planner for Review

Area(s) Where ‘Owner’ Contributes Most (i.e., Suggested

Responsibilities)

Operations Concerned primarily with service level & plan stability; secondary focus on inventory turns

Forecasts by week and month by Item by Location (in some cases also by day for first 2 months)

1) Response to system-derived forecast review suggestions by exception

2) Review of validity of adjustment rationale recommended by other owners

3) Monitoring new item demand on a frequent basis

4) Assessing performance

Sales Focus on achievement to budget (which is often based on commission goals)

Projections by month by salesperson and/or product line (depending on where commissions are available)

Indication of changes in objective changes in customer behavior (e.g., new sales locations, loss of customer’s customer(s), change in usage of product/BOM, etc.)

Marketing Determine trends by product line, monitor new item launch demand

Projections by month product line and new items by week by SKU

1) Provision of lists of present item equivalencies where history may be copied to new, similar item

2) Developing launch demand for new products based on: a) Launch patterns of similar

items in other companies b) Market size and market

share assumptions

Finance Primary focus on inventory turns and obsolescence

Dollarized inventory projections (requires processing of forecast through time-phased replenishment planning module)

Identifying nearly-obsolete items where reactive promotion in order

Designing a Demand Planning Process Based on Participants Interests & Likely Contributions


5

Optimizing the Demand Planning Process: Defining the Process

• Determining an Objective Baseline Forecast– Definition: An automated and regenerative approach that statistically incorporates all

structured, objective data including history and available leading indicators– Rationale for developing an objective baseline

• Provide a standard against which to review the value of different adjustments & methods

• Create a plausible default forecast in the event some forecasts are not reviewed

• Eliminate subjective arguments from the process

• Defining an Approval Process– Pros/Cons of implementing an approval process

• Pros– Participants are more aware of quality of contributions when explicitly authorizing them– Executors of the forecast (i.e., Operations) knows preceding reviews have been completed

• Cons– Potentially high cost of time involvement– Personnel bottlenecks may cause delays in execution– Cost of developing process and system

– Determining the ideal owners of the process & incentives for continuous improvement• Determine who has the most stake

• Link performance reviews, in part, to outcomes strongly correlated to the quality of the forecast (e.g., service levels and inventory turns)


6

SKUL by Key Account

SKUL

Division

Series

Product Line

SKU

Demand P

lan Genera

tion and A

ggregatio

n

‘Last-In Wins” Wildcard/Compound Adjustments(e.g., Class A SKULs within a specific Product Line @ a specific location)

Compound

or Hierarchical

Adjustment By %

Optimizing the Demand Planning Process: Defining the Review Process

• Prioritization of the Review Process (in approximate sequence)– Adjusted forecasts where the baseline has proven more accurate than the adjustment to

re-assess adjustment justification– Forecasts where participants have specific ‘Extrinsic’ knowledge*– High-impact baseline forecasts (based on service level and dollars) where no

adjustments exist & prospective baseline error is high; measured as: (forecast error)/(forecast)

– Assess aggregate trends to assure overall plan meets with business expectations

• Levels of Aggregation for Approval/Review– Determine default level for non-operations staff and focus only there except when

participants have specific knowledge at a lower level– Select level to limit review to no more than 1 day

* ‘Extrinsic’ refers to any event not included in prior ‘Intrinsic’ history


7

Optimizing the Demand Planning Process: ‘Baseline’ Generation Process

• Raw Statistical Forecast Based on Following– Demand history across item generations– Automatic simulation across multiple forecast model families*

• Trend

• Seasonal

• Trend-seasonal

• Sporadic: Includes independent & dependent event

– Assessment of net impact of model change (e.g., retain forecast if new model provides minimal forecast improvement); intended to avoid non-value-added noise and assure a smoother schedule

– Elimination of models where error is unmanageable**

• Adjustments to Raw Forecast– Manual adjustments based on specific data &, possibly, human pattern recognition– Identification and impact measurement of leading indicators (i.e., ‘Extrinsic’ Factors)

• Potential indicators include indices such as building starts, oil futures prices, etc.

• Impact should be measured via multiple regression, or minimally, via simple correlation

– Addition of customer-generated forecasts: Add only for periods where correlation between customer forecast & actual demand is high (may be tested semi-annually on a sample of items)

* If a system that performs these functions is not available, utilize Excel’s regression model w/adjustments for seasonality (see Excel Analysis ToolPak)** The process of assuring safety stock assumptions are met is actually more important than maximizing accuracy, this is not discussed in this paper


8

Optimizing the Demand Planning Process: Measuring Performance*

• Determining Measures of Accuracy and Value Add**– Time horizon

• Monthly

• Quarterly

• Last period & total cumulative lead-time

• Last period & total production time fence

– Levels of aggregation: Measure levels strongly correlated with either cost or service level

• Determining Adjustment Types: Identifying adjustments by source and rationale; e.g.,

– Override of ‘implausible’ baseline forecast (intuitive pattern recognition)– Customer Data (in lieu of collaboration)

• Verbal customer order intent

• Specified/scheduled events– Promotions– Geographic expansion/contraction

– Budget goal-oriented adjustments– Product cannibalization

* See http://www.ollie.com/downloads/frc.pdf for an insightful and free paper on this subject from the Oliver Wight Companies** See Appendix A regarding specific measures

http://www.ollie.com/downloads/frc.pdf


9

Collaborative Demand Planning: Definitions & Application Criteria

• Collaborative Demand Planning (CDP) Defined*– Intra- Enterprise: Exception Review & Approval among different functional groups within a

company of a single demand plan – Cross-Enterprise: Exception Review & Approval between of a single demand plan at the

item/receive location level

• Organizations Where CDP Likely to be Applicable– Intra-Enterprise (Step 1)

• Marketing, Sales, &/or Operations demand plans often diverge from one another with the disparity directly leading to failures to achieve stated goals of one or more group(s)

• All owners of the results of the forecast (those whose performance review is based upon inventory turns and/or service level) are not included in the forecast review process on a routine basis

• Any group that has significant leading-indicator data that may not be effectively quantified or captured

– Cross-Enterprise (Step 2)• Supplier’s ability to discern customer ‘pipeline fill’ versus recurring end-user demand is

limited

• Promotional activity is significant and advance notice of customer promotional activity may be provided to the supplier lead time in advance

• Many items high volume and variably-demanded

• Customer has better access to and/or understanding of leading indicator data

* See HTTP://www.CPFR.org for more specific information on collaborative planning standards and case studies

http://www.cpfr.org/


10

Collaborative Demand Planning: Pitfalls & Alternatives

• Pitfalls (i.e., Organizations Where Benefits are Likely to be Less)– Multi-suppliers for the same SKU– Long cumulative manufacturing/procurement lead times– Customer’s base forecast is often inaccurate over the supplier’s cumulative lead time

(e.g., it may add value over short time periods but noise over the longer term)– Large customers do not account for a large proportion of the SKU demand

• Alternatives– Use the customers forecast as an input into/override of the internal forecast process for

those weeks where the correlation of their forecast with actual demand is higher than that of the internal forecast

– Encourage sales to submit quarterly customer status reports highlighting changes in customer behavior/demand conditions (e.g., by doing 1/3 of all customers each month)

– Have customers provide an ‘event’ calendar that registers non-recurring, objective events (e.g., price changes, removal from catalog) and incorporate events statistically or manually in demand plan (rather than relying on customer to incorporate events which would include both objective and subjective factors*)

• Include all high-cost or frequently demanded items in program

• Have customers rank significance of event (e.g., 1-3) to prioritize supplier review

* Note that subjective factors may include customer biases (e.g., tending to overstate the forecast to assure that products are available at the supplier)


11

Next Steps: Lower-Cost/Impact Initiatives to Improve Demand Planning Quickly*

• Develop Process for Generating Baseline (See Former Slide With Requirements)**– Develop means for acquiring refreshed history data on a monthly basis (e.g., see Excel

Help regarding “Get External Data” tool in “Data” menu)– Build one model that adds seasonality to linear regression

• Apply at the SKU/SKUL level and then sum to 1 aggregate level (e.g., product line)

• Add aggregate adjustments as % change from baseline

– Track prior forecasts for baseline and adjusted errors

• Develop Process for Assessing Forecast Adjustment Based on Adjustment Types– Develop enough adjustment types to account for at least 2/3 of all adjustments (i.e., do

not assign a majority of adjustments to other)– Note participant suggesting adjustment

• Develop Reporting Tool– Once aggregate level is chosen, create Excel Macro or Access report for displaying

summary baseline & adjusted forecasts at aggregated level– Create sorting process based on priorities on prior page to create listing of forecasts to

review

• Process: Review at least top two priority forecast sets and then all others possible in priority order in pre-specified time frame

* If processes must be manual, focus on key items only, high levels of aggregation, and/or perform infrequently (e.g., once-per-quarter)** For small data sets (e.g., < 5,000 SKUs), basic models may be built in Excel for initiating this process


12

• Assess Various Requirements of Baseline Forecasting System– Determine optimal system for generating baseline forecast (see prior requirements listing)

• Assure that forecast error management is robust and is properly blended with supply variability

• Assure that demand quantity and robust demand frequency models exist (if some demands are sporadic)

– Determine process for collecting input data regardless of process (e.g., leading indicators)

• Assure demand planning system includes Inventory Chain Optimization (ICO): methods for developing profit-maximizing, inventory policy process (e.g., determines M-T-S/M-T-O decisions, service level, safety stocks, & order quantities)*

• Develop Matrix of Forecast ‘Owners’ with Specified Responsibilities for Intra-Enterprise Collaboration (Step 1)

– Level at which forecast reviewed– Frequency of review– Horizon(s) of forecast for which performance measured– Recommend incorporation of performance metrics in employee performance review

criteria for process ‘Owners’

Next Steps: Initial Tasks for Complete Optimization of Demand Planning

* While forecasting and inventory policy systems may be distinct from separate replenishment/scheduling systems, the forecasting & inventory policy functions (ICO) must be robust and integrated as they are in GAINSystems proprietary product GAINS


13

Appendix A: Specific Demand Planning Error Measurements

• Line-Item % Accuracy*– Absolute value of: (Forecast – Actual) / Actual *100– Report via Pareto distribution summing number of line items in ranges of 15% increments

(e.g., 0%-15%, 15%-30%, etc.)– Ignore where Actual equals 0

• Noise: Measure % line item forecasts (&/or forecast models) changing per month

• Aggregate Accuracy: above formula with each component summed across the grouping

• Aggregate Bias: Bias causes major long-term problems even if specific period measured is highly accurate

– Sum of (Forecast – Actual) * Standard Cost for all items within the grouping

* Perform this for at least the last lead-time planning period horizon where a period equals the time between forecast reviews; this assures data is provided with sufficient advance notice for manufacturing/purchasing to react economically.

modern demand planning history, objectives, methods, and implications presented by: william h....

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