Demand Planning & Forecasting: Utilizing IRT Data for ...1).pdf · Demand Planning & Forecasting: Utilizing IRT Data for Efficient Supply Chain Management ... ICE BREAKER . Objectives

Demand Planning & Forecasting:

Utilizing IRT Data for Efficient Supply Chain Management

4th Annual CBI IRT 2014 | 20 October 2014

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Who We Are

Almac is a global drug

development services provider.

Our mission is to be your partner

of choice, providing an expanding

array of innovative clinical

solutions worldwide.

Almac & Presenters

3420+ employees globally

Clinical

Technologies

Biomarker

Discovery &

Development

API Services

& Chemical

Development

Pharmaceutical

Development

Clinical

Trial Supply

Analytical

Services

Commercial

Services

DELIVERY DEVELOPMENT DISCOVERY

ICE BREAKER

Objectives for the Day

Supply Forecast & IRT Touch Points

Pre-study Set Up

Ongoing Study Forecast & IRT Adjustment

Learn, Share & Have Fun

AGENDA AT-A-GLANCE

8:30 – 10:00 Session 1

10:00 – 10:30 Break

10:30 – 11:45 Session 2

11:45 – 12:00 Closing Remarks

12:00 Lunch

Agenda

SESSION 1 Building a Forecast

Pre-Study and IRT Touch

Points

SESSION 2 Using IRT to Assist in

Managing Your Supply

Forecast

The Capital Markets: Cost per NME

(Total Annual Spend/Annual Approvals)

Cost per NME ($US Billions)

3.8 3.7

5.1

2011 2012 2013

Source: EvaluatePharma; PwC

Trial Expense

Confidential © Almac 2014

Trial Complexity

Typical Phase III Protocol 2002 2012

Total endpoints 7 13

Total procedures 106 167

Total eligibility criteria 31 50

Total countries 11 34

Total sites 124 196

Total patients randomized 729 597

Source: Tufts CSDD

Globalization – Spread of Clinical Studies

Graph illustrates the

number of clinical

trials registered by

country from data

supplied by

clinicaltrials.gov

Increase in countries involved, 11 to 34

Decrease in patients, 729 to 597

• Individual trials are now more complex and global…and more expensive

• More sites, less patients

– 48% of sites under-enroll

– 11% of sites never enroll a subject

• Average cost / patient $25,000 (2008) - $47,000 (2011)

Globalization, Sites and Patients

Changing Pipeline Mix

Complex Drug Supply

2013 2007

29% 36%

71% 64%

Biologics Chemical Entities

Growing Presence of Biologics

Up By

7%

Challenges

Manufacturing

Supply Chain

Blinding

Expiry

Storage

Distribution

Technology enables real time data to drive timely decisions

Built in flexibility to allow quick response to actual study

conditions

Focused planning for global and complex supply chain

Need to address physical and data needs for global clinical

supply

Informed Decisions

Eff

icie

nt

Tri

al,

Sit

e

& P

ati

en

t M

an

ag

em

en

t

Patients and Sites

Data entered across multiple platforms

Phone Web Web Device

Technology as an Agent of Change

Sponsors


Different

Perspectives on IRT

-Study Design

-Visit Windows

-Inclusion/Exclusion

-Randomization

-Enrollment

-Visit Registration

-Medication

Assignment

-Titration

-Electronic Data

Capture

-Un-blinding

-Study Results

-BioStatistics

Clinical Team

Clinical Supplies Group

-Inventory management

-Expiry management

-Enrollment tracking

-Inventory release and

control

-Shipment requests

-Shipment acknowledgement

-Quarantine and release

-Site levels

-Returns accountability

-Temperature control and

Excursion resolution

Track Progress and

Adjust Forecasts

Forecasting

What is a forecast?

• A prediction of clinical events and patient activity creating demand in

the clinical trial supply chain over time

Based on available information at a given point in time:

• If information is not available, make an assumption and document it!

Why generate a forecast?

• To maximize supply chain by minimizing waste without compromising

patient therapy

• To develop a supply strategy and packaging plan

• To reduce the risk of stock-outs

• To reduce cost

CRITICAL FORECAST DRIVERS

Study Design and Enrollment

Product Design / Characteristics

Randomization Parameters –

Patient Population

Visit / Dosing Schedule

Screening Windows

Participating Countries

Country/Site Activation Schedule

Local Depots

Screening/Enrolment Rates

Fixed or Variable Dosing

(Titration; weight based)

Drop-out/Discontinuation Rate

Study Completion Criteria

Therapeutic Setting

Bulk Drugs (Production/Procurement)

Pack Types

(Primary Packaging & Kit Design)

Label Strategy

Shelf Life and Stability Program

Temperature Monitoring/Excursions

Storage Conditions

Shipment Conditions

Shipment Frequency

Inventory Management Strategy

Seeding

Triggers

Prediction Ranges

FIXED VARIABLE

Information Sources

Previous studies and in-house experience

CRO

Clinicians and medical professionals

Forecasting Technology

• Allows for the creation of a clinical forecast that details

material needs in time phases based on Set Algorithm -

Fixed

– Enrollment rates, site and country ramp up, drop out rates

– Protocol design – visit schedule and titration choices

– Overage, site seeding and/or safety stock

– Label Groupings

• Allows for multiple scenarios to be programmed and

compared

Forecasting Technology

• Combines forecast technology with MRP to produce

planned production orders to satisfy demand taking into

account

– Inventory on hand at Sites, Depots, and vendor facilities

– Expiry Dates

– Production capacity and operation duration

– Bill of materials/component availability and lead time for

re-order

– Pending production orders

– Lead time for transfer from Vendor Sites to Depots

Some Thoughts on Overage

Overage = Insurance – Is the cost of insurance high or low?

– Be careful with percentages

• 15% of 10,000 units

• 15% of 100 units

– Overage is primarily affected by the following factors:

• # of Subjects

• # of Regions

• # of Depots

• # of Treatment Groups

• Screening Window

• Central vs. Stratified Randomization Scheme

• Certainty

Demand Planning and Forecasting –

Utilize IRT Data for Efficient Supply Chain Management

PROTOCOL Evaluation of Efficacy and Safety of IMP X vs Comparator Drug Y

• Phase 3, Randomized, Double Blind

• Indication: Breast Cancer

SCOPE 500 subjects screened

400 randomized

60 sites, 9 countries

COUNTRIES Countries (S Korea, Taiwan, Italy, Spain, France, Russia, Ukraine, USA, Canada)

STUDY DESIGN 2 Treatment Groups – 2 (IMP X 10mg/kg):1 (Comp Y10mg/kg) Ratio

15 cycles per subject

21 days between visits +/- 3 days

DURATION LPI = FPI + 24 Months

100% of subjects to continue to 6 cycles




SCREENING Randomization is 28 days after screening

DRUG PRODUCT Numbered supply, packaged as individual vials

Cold Chain : excursions permitted 8hrs 0-2oC 16hrs 8-24oC

SHELF LIFE EU and US comparator 18 months.

IMP initially 18 months, extension planned to 24 months

RANDOMIZATION Patients centrally randomized

Study / Forecast

Assumptions and

Constraints

0.7 subjects per site/month

Average Patient Weight = 70 kg

Min Vial usage: 5 (min weight = 50kg)

Max Vial usage: 14(max weight = 140kg)

Preliminary Forecast

• Overall Demand:

– Average patient weight

– Drug required for

average dose

– Estimated number of

cycles (visits)

– Calculate demand

– Add safe overage

Study Design

Forecast

IRT Development

Bulk or API Forecast

26

Study

Start

Supply Outlook

IRT

Study Design

Drug Supply

Demand Assumptions

DATA

IRT

Modules Inventory

Management Reporting

Data Integration

Managing Drug in IRT

• Inventory Calculation

– Set Algorithm - Fixed

– Step-by-step procedures

– Established at system build

• Adjustable values

– Suggested at beginning of study

– Easily modified as new data becomes available

– Dials to fine tune medication management

Common Site Inventory Management Methodologies

Initial shipment

Trigger and resupply

Randomization projections

Visit projections

Initial Supply to Sites

Screening Window = 3 to 10 days

Shipping Lead Time = 7 days

Screening Window = 10 to 21 days

Supplies shipped at site activation

Supplies shipped at patient screening

Trigger and Resupply

Trigger = 2

Resupply = 6

• Unpredictable demand

• Keeping stock at site for unknown patient variables

Randomization Projections

• 2 Treatment groups A and B

• 1:1 ratio

• Block size 4

• Enrollment prediction of 4 patients/site/month

Centralized Randomization Site Stratified Randomization

4 Type A kits 2 Type A kits

4 Type B kits 2 Type B kits

Visit Projections

• Using the visit schedule, IRT calculates when

subjects are expected to come in for visits and what

treatment they are on

• Orders will be placed for specific patient need X days

before the visit

• Often referred to as a “predictive look ahead”

Visit 1 [WEEK 1]

Visit 2 [WEEK 2]

Visit 3 [WEEK 3]

Projection Time Frame

Use Short Look Ahead Use Long Look Ahead

Short Transit Time Long Transit time

Short Expiration Long Expiration

High Discontinue Low Discontinue

Low Supply Availability High Supply Availability

Limited Site Storage No Site Storage Issues

Tying It All Together

Site Ordering Needs

Visit projection

Trigger & resupply Rand

projection

Let’s Get Into Your Teams!

• On the pages provided, you have a

scenario to consider

• You have 25 minutes to discuss

• Think about how you would:

– Consider the supply/forecast constraints

– Apply IRT settings using assumptions

• Feedback to the group on your thoughts







400 randomized

















Study / Forecast

Assumptions and

Constraints





Building

Flexibility

is more

important

than

Accuracy

“Forecasting is hard, especially first thing in the morning.”

.

SESSION 2

Managing Your Forecast

IRT

Study Design

Drug Supply

Demand Assumptions

DATA

What, when, and how

we check our work,

and make adjustments

Monitoring Forecast Drivers

– Site activation data

– Enrollment:

• Actual monthly enrollment is captured and forecast updated

• Estimated enrollment rate can be re-calculated for future projection

• Drop-out Rate – confirm agreement with projection or add to forecast

– Rate of drug supply utilization

• Is it in agreement with forecast?

• Should assumptions be modified?

– Frequency of forecast updates has to be decided upon

• When do we have enough data to merit a baseline change?

– Source of Data

• How will data be collected?

Forecast Maintenance Benefits of Monitoring

• Supply Utilization / Inventory Balance

• Comparator Purchasing

• Assessment of retest/expiry date impact

• Adjustment to IRT Drug Management Settings

Adjusting assumptions allows for optimization of clinical supply chain

• Adjust Production Schedule based on demand

• Adapt to changing lead times and demand

Supply Utilization and Inventory Balance

• Strategic Sourcing Comparator Purchasing

Forecast Adjusting and Weighted Data

Study Data Assumptions

0%

100%



25% 75%



25% 75%

Application of Weighted Average

Enrollment Actual Vials

Per Dose Weight of

Study Data

Initial

Forecasted

Vials per

Dose

Weight of

Initial

Forecast

Updated

Forecasted

Vials per

Dose

0% 0 0% 9 100% 9

25% 6.5 25% 9 75% 8.375

50% 6.4 50% 9 50% 7.7

75% 6.2 75% 9 25% 6.9

Average Patient Weight


• 600 subjects

• Dose of 5 mg/kg

• Two images

– 400 mg vial

– 100 mg vial

• Average patient weight = ~85 kg

• Average consumption per visit = 1 x 400 vial and 1 x 100 vial

• Average 9 cycles at Q21 = 9 x 400 vials and 9 x 100 mg vials

per subject

• 600 subjects x 9 vials = 5400 vials of each


Dose Ranges

(Kg) Number of

Subjects in Range 400 mg 100 mg

50-60 75 0 3

61-80 145 1 0

81-100 250 1 1

101-120 110 1 2

121-140 20 1 3


• With interim study data and adjusted outlook:

4,725 vials

of 400 mg

Over-forecasted

by 14.2%

6,795 vials

of 100 mg

Under-forecasted

by 25.8%

Leveraging Maintenance Data

Patient Weight Analysis

0

2000

4000

6000

8000

10000

12000

14000

16000 Supply Calculation by Average Weight

Cumulative Kits Released

Cumulative Projected Demand

Cumulative Projected Demand (Revised Assignment Qty)

Cumulative Actual Demand

Supply Utilization Monitoring

Comparison of Projected To Actual Treatment Preference

COUNTRY / REGION

TPC-1 TPC-2 TPC-3 TPC-4 TPC-5 TPC-6 Screened

Patient Total

WESTERN EUROPE Initial Assumption

26.0% 30.0% 20.0% 9.0% 5.0% 10.0%

Treatment of Physician's Choice (TPC): Preference

per Screening Data

Feb-13 46.7% 28.0% 11.7% 5.1% 1.4% 7.0% 214

Mar-13 50.8% 25.8% 0.1% 4.3% 1.2% 6.3% 256

Apr-13 51.9% 25.6% 11.3% 4.4% 1.4% 5.5% 293

May-13 53.5% 24.5% 10.9% 4.8% 1.2% 5.1% 331

Jun-13 54.7% 23.3% 10.6% 4.9% 1.6% 4.9% 369

Jul-13 54.6% 22.8% 11.4% 4.9% 1.7% 4.6% 412

Enrollment Monitoring

0

500

1000

1500

2000

2500

3000

3500

4000 Patient Visit Projections - Scenario Comparison

Projected Enrollment Visits 1) 6500 Pts in 18 Months 2) Interim Analysis 3) 4000 Pts in 9 Months Actual Enrollment Visits 10,500 Pts in 7 Months

Projected Re-supply Visits

Actual Re-supply Visits

IRT Adjustments

Forecast Adjustments

Trigger levels Patient / site / month

Predictive ranges High discontinuation

Site enrollment strategy Slow enrollment,

frequent small shipments, etc.

Expiry rules Supply crunch

Evolution of Forecasting Throughout Trial

Get Into Your Teams!

• Let’s reassess our study based on data

• You have 25 minutes to discuss

• Think about how you would:

– Consider the supply/forecast

– IRT settings using actuals

• Feedback to the group on your thoughts







400 randomized

















Study / Forecast

Assumptions and

Constraints





6 Common Pitfalls

Stress Test New Med

Management Ideas

Unsortable Data

Weight Based Calculations

Unplanned Reports

Alert Numbness

See No Data

Forecast

Production

Distribution

Subject Data/IRT

Data Transfer

INTEGRATION

Integration of Technology

Provides real time

clinical trials data:

• Randomization

• Dispensing

• Resupply

• Expiry date management

Interactive Voice and Web

Response System IXRS®

Powerful computational

tool determines:

• What is needed

• When is it needed by

• How much is needed

Material Resource

Planner MRP

Details material need in

time phases used to:

• Develop manufacturing

and distribution strategy

• Scenario evaluation

• Budget management

• Risk management

Demand Forecast

INTEGRATION OF TECHNOLOGY ALLOWS GREATER VISIBILITY OF DEMAND IN

THE SUPPLY CHAIN ALLOWING RISKS TO BE IDENTIFIED AND MITIGATED

Confidential © Almac 2014

• Can receive patient and drug usage events directly from the IRT via automatic data transfer

• Automatically adjusts forecast based on drug usage data

• Calculates a “net forecast “ based on available inventory and expiry dates

• Comparison reports for forecasted vs. actual usage

• Enables modification of production activities

• 3 Integrated elements of SupplyWiseTM

• Clinical Forecast

• Material Resource Planning system (MRP)

• Patient Data integration with IRT

Integration of Technology

SupplyWise

INITIAL FORECAST

• Projected patient demand

• Visit schedule

• Protocol variables

• Scenario comparisons

NET FORECAST INPUTS

• Site inventory

• Patients dispensing events

• Expiry date

TECHNOLOGY SOLUTION

MANUFACTURING

PLAN

Detailed component

planning

MRP

Creates planned

production orders

based on:

• Forecast

• Safety Stock

• Pending production

orders

• Existing inventory

• Item Bill of Materials

DISPENSING

to Patients

Almac & Depot Inventory

considered by MRP

Inventory at or in transit to sites considered in Net Forecast

Actual patient event data

considered in Net forecast

Patient Event Data

DISTRIBUTION

Depots & Sites

Drug Orders

Data Integration with (IVRS / IWRS)

Inventory Release File

Incremental Application of Weighted Average

Enrollment Actual Vials

Per Dose Weight of

Study Data

Initial

Forecasted

Vials per

Dose

Weight of

Initial

Forecast

Updated

Forecasted

Vials per

Dose

0% 0 0% 9 100% 9

2% 6.5 2% 9 98% 8.95

4% 6.4 4% 9 96% 8.896

6% 6.6 6% 9 94% 8.832

Key Take-Aways


- Consider the needs of all stakeholders

- Technology facilitates success

- Integrate systems for fluid data flow

Pre-study Set Up

- Flexibility in IRT design is critical

- At the beginning, work with the end in mind

Ongoing Study Forecast & IRT Adjustment

- Reduction in overage as data accrues

- Site resupply configurations to match demand

Documents

Demand Planning & Forecasting: Utilizing IRT Data for ...1).pdf · Demand Planning & Forecasting: Utilizing IRT Data for Efficient Supply Chain Management ... ICE BREAKER . Objectives