adoption of operations 4.0 at zydus - ipa india · 2019-03-05 · privileged & confidential...

Privileged & Confidential

Adoption of

Operations 4.0 at

Zydus

This presentation and its materials contain proprietary/ confidential information and is the exclusive intellectual property of the author/speaker. Any

unauthorised copying, distribution, or other use of this is strictly prohibited. The author /speaker makes no representations and gives no warranties of

whatever nature and all liabilities arising therefrom are disclaimed.


Snapshot of operations at Zydus


Objective and Deliverable

Improve the COGS efficiency with focus on material and services

The modus operandi involves

• Spend base analysis and taking targets with the cross functional Category teams

• Systematic opportunities identification via idea generation workshops

• Drawing out exact action plans to achieve within a finite time

Improvement Levers

Alternative Vendor Development

Design to Value

Price development against benchmark

Switching products

Supplier substitutability

Operational Excellence has played a key role in driving

business growth



business growth


Achieving excellence in manufacturing operations with focus on improvement in efficiency, flow and capability.

Engaging people to identify opportunities and drive improvements via Kaizens and GB projects

Improvement Levers

Productivity Improvement

Process Capability Improvement (CpK)

Yield Improvement

Energy Conservation

Inventory Management

Material wastage reduction 1100 SLIM Members

Driven at 25 sites/functions

3400+ ideas implemented

Rs 355 Crores Saving


Daily

performance

huddles

Reinforcement:

awareness &

recognition

Review &

feedback for

continuous

improvement

01

02

03

*Key Sites

2017 2018

Go

od

Culture Score

30

80

2017 2018

Good

GDP Errors

2017 2018

Go

od

Lot Acceptance Rate

QUEST – Outcome*


business growth

QUEST Execution

Idea generation and implementation

Daily dialogues in circle

QUEST Campaign

Mass awareness drive

Posters and champions for daily dialogue

Quiz programs

Nukkad Natak

TTT Workshops

Town hall meetings

Recognition programs

QUEST circles are engaged in 3 key activities in order to build quality cultureat the site. All employees are regularly engaging in quality-related activities through circles.



business growth


ME (Manufacturing Excellence) function was launched in 2017 with an aim to drive operational

excellence at shop-floor with a Top-Down Approach in partnership with manufacturing & Quality function

to drive key strategic projects using Lean Sigma approach -

• Throughput Improvement

o Principles of ToC, Conducting VSM

o Release capacities from bottleneck at Mfg Sites.

o Batch Size increase

• Ops Agility

o Quick Changeovers.

o Lower Lead times.

• Cultural Transformation

o Performance Management System.

o White Belt & Green Belt training programs.

• Waste reduction

o Yield improvement

o PM wastages.

Since inception, ME program has delivered significant results

Throughput improvement by 20%+ across BU Mfg

Planned 18% reduction of batches through Batch Size increase initiative

OEE improvement of packing lines by 8%

Improvisation of Yield by 1%

Driven the Lean culture through Daily Management System and LeanSigma capability


Stage 3: Network wide Ops 4.0

deployment Stage 2: Select use case based

POCs in Ops 4.0 (Digital &

Advanced Analytics)Stage 1: Process automation to

drive efficiencies & compliance

• Focus on 2-3 high visibility use cases e.g. improving quality outcomes

• Useful to ‘test the waters’ and build organization conviction by demonstrating quick and high impact

• An integrated approach to drive multiple high impact use cases in a function/business units for tangible impact

• In parallel, transform the organization foundation, including building of new capabilities, data & tech backbone & effective change management

• Rolled-out across functions in a phased approach

• Focus on converting all possible manual documentation and workflow to digitization

• Automation of the mfg, and Quality operations to drive efficiency and mistake proofing

We are here

However, we are at an early stage in our Ops 4.0 journey


Stage 1

Process automation to drive efficiencies & compliance

Trackwise

• Part 11 compliant electronic QMS system to manage all quality processes in a centralized database.

• It obtains information instantly using powerful queries, personalized dashboards, trends and smart analytics.

Documentum

• Facilitates electronic document lifecycle management with version control, controlled printing and part 11 compliance.

• Gain centralized control and real time visibility into all quality and manufacturing documents along with faster retrieval.

LIMS

• Faster acquisition and management of electronic data in labs.

• Automation for sample management and analysis activities to minimize manual intervention and providing alert notifications.

ZyTims

• Offers central repository to hold enterprise level employee training data information.

• Inbuilt automation to trigger role based trainings need and Inbuilt analytics for training summary reports for decision making and planning.

http://www.google.co.in/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwjezPn6u5bcAhUPX30KHc8mD0UQjRx6BAgBEAU&url=http://www.tdvct.com/en/services/sparta-trackwise/&psig=AOvVaw3mHZuFOL-Uu8b2W9jBGrne&ust=1531378592202638

http://www.google.co.in/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwjezPn6u5bcAhUPX30KHc8mD0UQjRx6BAgBEAU&url=http://www.tdvct.com/en/services/sparta-trackwise/&psig=AOvVaw3mHZuFOL-Uu8b2W9jBGrne&ust=1531378592202638


Stage 1

Process automation to drive efficiencies & compliance

MINITAB

• Facilitates statistical analysis of data to find meaningful solutions to toughest business problems.

• Has helped us increase efficiency and improve quality through smart data analysis.

Warehouse Management System

• WMS is deployed for automatic storage and retrieval of all raw materials, packaging materials and Finished products.

• WMS identifies and tracks the materials through unique bar codes assigned to each storage location, Pellet and material containers. This makes it error free storage and retrieval system.

e-Logbooks/ e-SOPs

SAP

• Implemented PP, PM, MM and QM modules

• A well integrated model i.e with WMS, Trackwise, Zytims, LIMS, MES, Documentum…

• Virtual desktops are installed in manufacturing and packaging areas for electronic log books to replace paper based log books.

• Used for storage and retrieval of electronic version of SOPs through Documentum.

• Electronic log books shall also be used for various dashboard reports like breakdown trending, cycle time analysis etc


Stage 2

Digital & Advance Analytics in Manufacturing

Digital - Use Cases

• ZMES- eBMR

• PACE

• API yield improvement

• Factory Digital Dashboard

• Digital Energy Management

• New Product Management -IRIS

• Material flow control through Barcode/RFID

Source: Forbes; World Economic Forum


Digital – Use Case 1

ZMES

MES is piloted at one of our key Manufacturing Value Stream at the Moraiya campus. The MES implementation

is planned for our key mfg. sites

• Guarantee compliance & quality

Reduction of deviations

Enforcement of processes & specifications

Material Tracking

Equipment Tracking

Right first time documentation

Data integrity through integration

• Increase process efficiency

Streamlining of processes through electronic record introduction

Manual entries reduction / elimination

Reduction of manual verification steps

Review by exception (almost real-time)

• Elimination of paper within the manufacturing process

This scopes..

• Reduction of risk of non-compliance - DataIntegrity & Data Reliability

• Review of the batch records by exception only

• Enforcement of material flow and relatedquality checks throughout the manufacturingprocess

• Enforcement of equipment and related qualitychecks throughout the manufacturing process

• Integration with SAP

• Factory automation in the form of monitoringprocess parameters(CPP & CQA)

• Contemporaneous Entries

Benefits


Zydus has initiated digitization program for Supply Chain to improve agility, data driven decision making and visibility across stakeholders

Business Outcomes

Statistical Forecasting with product level auto best fit forecast

Defined variance levels for consensus planning

Digitization Initiative

Unconstrained Demand generation

Scientific Capacity Planning (RCCP)

New Product Launch Integration

Optimiser based batch level unconstrained demand generation

Real time performance monitoring to enable data driven

decision making

Prioritisation based scientifically derived production plan to

enable best capacity utilization, as per business constraints

Integration of New product launch with regular S&OP to

strengthen planning efficiency

Long Range Planning Process AutomationDigitisation of Long range planning with multiple scenario to

make business decision


PACE

Statistical forecasting and Consensus planning

Management and Operational Dashboards



PACE

1 2 – R preparation 3 4 and 5 – R Validation 6 & 7 – MRP Run

12 and 13 –Allocation and Validation 11 – Shortage Analysis 10 – Shortage calc 9 – BOM Correction 8 – Validation Check

15 First level scheduling 16 17 18 &19 - First level commits 20 & 21 – Campus S&OP

27 26 – Market S&OP 25 – E S&OP 23 and 24 – Pre S&OP 22

14

Limited use of forecasting

29 – Sales Closing 30 31

Lack of timely and scientific data

Selective and limited shortage discussions

Nos. denote indicative

dates

S&O

P M

onth

ly t

imel

ines

, w

ith

proc

ess

gaps

Centralized RSP generation

Frequent RSP changes

Personal heuristic based Safety Stock Norms

High level R Validation

Blanket Inventory Norms

Distorted demand due to Constrained R

Artwork changes , Blocked mat : combined analysis with team

No impact analysis of commit carry overs

Daily manual C vs A tracking

Decisions under duress

28 - Final S&OP

As Is Process Gaps

Manual allocation :Priority, DOC, Value

Active BOM updation in Go-Green

SFG mat net – offRM / PM net off : retest & expiredPartial dispensing impact

Digital interventions are targeted to improve challenges of existing processes and improve effectiveness of planning



PACE

30th 1 2 – R preparation 3 4 - MRP run 5 - Material allocation & RCCP 6

7

13 – E S&OP 12 10 & 11 – Combined Pre and Campus S&OP 9 8 – First level Commits

18 19 20

27 26 25 24 23 22

21

14

28 29 – Sales Closing 30 31

Nos. denote indicative

dates

S&O

P M

on

thly

tim

elin

es,

wit

h p

rop

ose

d p

roce

ss

15 – Market S&OP 16 17 – Final S&OP

R from Tool output –Consolidation of all RSPs

Automatic material allocation

Commits based on better capacity definitions

Detailed Root Cause Analysis for R vs C gaps

Discussion on shortage reasons for exceptions

Decision on unresolved issues from Pre S&OP

Forecast generation

More time for analysis, decision making and improving ways of working

To Be Scenario

Digital S&OP will streamline planning processes with real time data visibility, improved collaboration and reduced planning cycle times


Implementation of Project Information Management tool for visibility in New Product Development and Launch

Business Outcomes

Real time performance monitoring to enable data driven decision making

Digitization Initiative


PACE

Portfolio visibility and prioritizationProvides structure across all activities and assures individual

ownership and accountability with defined SLAs in place.

Digitization of various actionable forms: DDF,

PMF, COD, SRF, QRF, VAR/PAR, etc.

Allocate operational resources and provide trail of all project-

related attributes

Visibility on entire product Life Cycle across all

cross-functional teams

Identify potential sources of bottlenecks and opportunities to

streamline activities

Management and Operational Dashboards

Visibility on Sales Forecasts and Budget

Allocated (restricted)

Ability to check budget adherence and identify causes for

deviation


…and applied multiple AA based models

Developed a data lake from 10+ data sources creating 300+

variables for each API stage…

Utility e.g.,

Steam,

chilled water

Temperature

from data

logger

QC LIMS

data for RM,

intermediate

& FG

Batch Man-

ufacturing

Records

Maintenance

records

Equipment

logs &

specifica-

tions

Operators &

Supervisors

Lab chemist

& supervisor


Advance Analytics for yield improvement

Approach




Current range maintained Proposed rangeParameter

Expected improvement

in yield (non-additive)

Final temperature of step X 30 to 35 degrees 30-34 degrees ~1.5

%

Duration of step A 125 Mins to 225 mins 200-225 mins ~2.8%

~2%

Final temperature of step Z 80 to 85 degrees ~1%

Duration of drying stage 500 to 915 mins ~1%

2

3

1

4

5

6

Parameters

with positive

impact on

grid

Parameter

values to be

avoided

Category

Final temperature of step Y 80 to 84 degrees 80-81.6 degrees ~1.5

%

Reflux for xx minutes and

collect water azeotropically 80 to 85 degrees 81-82.5 degrees

80-83.8 degrees

700-915 mins

Advanced Analytics gave us several insights for process parameter optimization


Baseline 95th percentile Achieved through AA

1.72

1.791.76

Baseline 95th percentile Achieved through AA

1.19

1.24 1.24



API 1

API 2

Implementing these ideas have given us immediate improvement in yield


The key objective

• To real time, track of the Key Performance Indicators for the Oral Solid Dosage manufacturing plant from Dispensing to release of batch.

• To transform existing manufacturing facilities into smart facility by introducing real time Intelligence and decision-making system for all

stakeholders.


Factory Digital Dashboard

• Real time performance indicator of M/C KPIs

• Customizable dashboards for Users. (BU President to Operator)

• User defined exception driven alerts and notification system (SMS, email and pop-ups)

• Reports generation basis different permutation and combination of M/C, Operator, Supervisor and all KPIs- Date range, equipment, suites, Operator and batch wise

• Operator Performance Analytics

• Interactive Data exploration

• Problem Resolution Support provides through phone / online support by trained professionals

Approach

• Business Critical

• Ease of

Implementation

• Infrastructure

• VOC

• Stakeholders

alignment

• Expert reviews

• Implementation

• User feedback and

amendment

• POC learning

• Project execution

Key features

Pilot Selection URS Drafting POC Deployment


Sr.No. Area MachinesNo. of

KPIs

To

tal

Pro

du

cti

o

n Avail

ab

ilit

y Perf

orm

an

ce

Qu

ali

ty

OE

E

Gra

nu

lati

o

n O

EE

Gra

nu

lati

o

n Y

ield

Yie

ld

Mach

ine

Ru

n T

ime

Mach

ine

Do

wn

Tim

eM

ach

ine

Uti

lizati

on

Tim

eB

atc

h

Cycle

Tim

eB

atc

h

Pro

du

cti

o

n k

g/t

ab

let

Reje

cti

on

Ch

an

ge

Over

Tim

e

Co

nv

ers

io

n C

os

t

MT

TR

MT

BF

1 Dispensing Dispensing booth

2

Granulation Area

RMG_1 11

3 RMG_2 11

4 FBD_1 11

5 FBD_2 11

6 BLENDER_1 15

7 BLENDER_3 15

8

Compression

COMPRESSION_1 16

9 COMPRESSION_4 16

10 COMPRESSION_5 16

11 COMPRESSION_2 16

12Coater

COATER_1 12

13 COATER_2 12

14

Packer

PACKER_3 16

15 PACKER_6 16

16 PACKER_8 16

17 PACKER_9 16

Hour

Shift

Day

Month

Year



Data Input – No or minimum manual intervention for data capturing and processing

• Machine time data acquisition from PLC

• Process and product data acquisition from SAP, LIMS and other software

• Manpower data acquisition through attendance and allocation system

• Time loss information acquisition through PLC or HMI

• “Standards” data acquisition from Digital solution library

Performance Management System

Dashboard KPIs




Plant/Cluster/ BU head

Section head

Supervisor

Operator

Real Time Performance Management System



IRIS

IRIS: Sample screen


Use Case @ the Chiller

Energy Consumption Optimization by 20%

Scoping

Entering chilled water temperature

Leaving chilled water temperature

Refrigerant evaporator Pressure

Entering condenser water temperature

Leaving chilled water temperature

Refrigerant condenser Pressure

Compressor Power

Evaporator / Condenser Approach

Chiller Efficiency (ikW/TR)

Cooling Load (TR)

Collected Parameters Calculated Parameters

Total Chiller Plant Efficiency

(ikW/TR)

Establishing Baseline

Zydus Chiller PlantTarget

Benchmarking

0.60

0.11 0.11 0.09

0.91

0.50

0.05 0.05 0.05

0.65

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Chiller (kW/TR) CHWP (kW/TR) CDWP (kW/TR) CT Fan (kW/TR) Total Chiller plant(kW/TR)

Sp

. E

ne

rgy

Co

nsu

mp

tio

n (

kW

/TR

)

Actual Recommended

Target Setting

Approach- POC at a Site -> Horizontal Deployment


Digital Energy Management



Flow control through RFID/Barcode

• Raw Material allocating & tracking through - SAP & Barcode

• Dispensed material tracking through unique barcode of dispensed material into Granulation.

• FBD bags, Hose pipes issuance & tracking through unique barcode.

• Raw Material receiving & dispensing- SAP & Barcode

• Scoop, sieve, screen issuance & tracking through unique barcode.

• Blend granules tracking through unique barcode of blend material containers into Compression.

• Dies and punches issuance & tracking through unique barcode

• Compressed tablets tracking through unique barcode of comp. tablets containers into coating.

• Silicone pipes issuance & tracking through unique barcode.

• Coated tablets tracking through unique barcode of coated tablets containers into inspection.

• Inspected tablets tracking through unique barcode of inspected tablets containers into Packaging.

• Bottle barcode & 2D code -> shipper label barcode-> pallet barcode, linked with each other through serialization

• Packed shipper pallets transferred into bond through unique barcode of packed shipper pallet.

WH Dispensing & Sifting Granulation Compression

Bond/Dispatch Packaging Inspection Coating

Mapping of all the process across the value streams to “Mistake-proof” the material flow and use of right tools & equipment.


Data limitations

▪ No single source of truth: Multiple sources of data exist

▪ Significant time consumption in converting manual hard copy data into digital

record e.g., batch records

▪ Inadequate IT & data systems to provide real-time data for analytics & decision making

Lack of next-gen

capabilities

▪ Tradition continuous improvement teams do not have data science/ engineering skill

sets

▪ Challenges in recruiting & retaining such talent in traditional operations setup

▪ Integration of multiple digital solutions

Mindsets

▪ Openness towards adopting the new ways of working

▪ …..

Challenges faced


Ops 4.0

transformation

Value capture

roadmap

Data systems &

IT infrastructure

Organization and

capabilities

Culture and

change

management

Operating model

(Agile and value

assurance)

A

B

C

D

E

Stage 3

Network wide Ops 4.0 deployment

5 key enablers need to be put in place


Stage 3


We have identified an initial list of use cases to be deployed

Supply chain

1. Cost optimization in indirect spend items

(MRO, consumables, other commodities)

2. Predictive analysis of procurement

trends/volatility

3. Reducing inventory expiries across the value

chain

4. Use predictive analytics to predict supply gaps/

stock outs & field inventory

5. Order to cash cycle optimization

Manufacturing and quality

1. Machine learning to improve asset utilization/

OEE

2. Predictive maintenance for better equipment

reliability

3. AR/VR Based Training Application

4. Optimizing utility cost

5. Sterility assurance through digital & gamified

tools

6. Improving product / process robustness

7. Reducing valid/ invalid OOS


▪ Drive initiatives to embed Ops 4.0 approach in the organisation, e.g.,

– Shifts in governance to embed analytics-led output as part of day-day working

– Handholding of frontline to enable/ coach on use of the tools

Culture and Change

management

Organisation and

capability building

▪ Building a core team / CoE of people with differentiated talent, including

– Data engineers and scientists to drive data clean-up, mapping and modeling

– Pool of business translators from different functions who can act as the bridge between the

business and the CoE in defining and building various use-cases

– Coach senior leaders to operate in an “analytically enabled” environment

▪ Design end-state data & IT architecture and define/ drive execution of the roadmap to move

towards the end-state

▪ Define data governance (e.g., ownership various data sets, who is authorized to edit data, etc.)

and data security strategy (e.g., to protect patient and employee data)

Data backbone & IT

infra.

▪ Define and embed two new “way of working” to enhance impact from the journey

– “Agile” methodology to rapidly develop, deploy and refine solutions across each lighthouse

to ensure rapid delivery of solutions, impact

Operating model

shifts

Stage 3


Initiatives being undertaken on the other elements


Thank You!This presentation and its materials contain proprietary/ confidential information and is the exclusive intellectual property of the author/speaker. Any

unauthorised copying, distribution, or other use of this is strictly prohibited. The author /speaker makes no representations and gives no warranties of

whatever nature and all liabilities arising therefrom are disclaimed.

adoption of operations 4.0 at zydus - ipa india · 2019-03-05 · privileged & confidential...

Documents