IFIP 8.3 task force

Learning from case studies in decision making & decision support

5th April 2007, UCC

“Putting the P back into ERP”• Does ERP help managers do their work• Who gets the benefit, HQ or site?• Writing the case studies (finding the thread)• Finding the dependent variable (IS, Org, DM)• Framework to model the gap between reality

and ERP

• Learning from case studies on decision support?

Research questions

• Is there a gap between ERP & reality?

• Significance of gap for mgt decisions?

• How should this gap be managed?

The cases

Firm A (pilot case) Firm B (in-depth case) Industry Pharmaceutical Data management Turnover 05 ($bn) 38.72 9.66 Employees 100,000 26,500 WW operations 119 52 Manufacturing sites 80 3 Head Office London, UK Boston, USA ERP System SAP R/3 Oracle 11.03 Architecture Single instance Single instance Server location Pennsylvania, USA Boston, USA Go-live Phased 2004 Big-bang 2001

2 in depth cases with embedded units of analysis (site and HQ)

Interview table Firm B Firm A Cork Boston Total Pilot Cork UK Total Finance 5 9 14 2 1 1 3 Manufacturing / Distribution 13 6 19 14 9 7 23 Sales 4 3 7 2 1 3 IS 4 2 6 3 3 3 6 Engineering 2 2 2 1 3 HR 2 2 3 3 Total 30 20 50 26 15 11 52

Field data can be sliced & diced

Function Firm A Firm BMaterials / Planning 8 8Manufacturing 6 6Distribution 2 9Total 16 23

# Interviews

For Mfg, # observations is still high

Seed category No. observationsGoal setting 92Strat goals 47Mgt goals 252Ops goals 273Core competence 24Mgt decisions 421Evidence of impact of ERP 559Changing business model 115

Case findings• Data

• Organisation and applications

• Key management decisions, role of ERP

Data• BI layer on top of ERP introduces data replication

reporting latency• Data handling (ETL) re-introduces data integrity and

timeliness issues• Metadata in BI tools redundant with respect to ERP • Decision support required by managers is aggregate

operational data• BI tools are used to “bridge the gap” between operational

systems and reality. • BI = glue connecting operational systems to the real

world• BI becomes critical to the organisation, and skills are

rare

Virtualisation

• Virtualisation: capturing & storing data relating to changes in the physical environment in an information system

• A measure of the degree to which information systems can reflect business reality

• Pre-supposes a structure (database), as data captured is related to a logical entity

Turning raw data into information = value-adding process

Level Value-add

Management Information aggregationDecisions Information visibilityRules Driving administrationProcess Data drives workflowTransaction Data integrity

iValue Chain example

* Capture at source eg. match physical goods received to a stock item in the system

Level Value-add PhysicalManagement Information aggregation Supplier relationship Decisions Information visibility PPV controlRules Driving administration Approve for paymentProcess Data drives workflow Receipt to PO matchTransaction Data integrity* Supplier delivery

Integration at a data level

• The level of “inter-connectedness” of IS

• # relationships in the database

• Reduces the risk of redundancy in system

• Ensures integrity

• Can boost system performance

Integration at a process level

• Logical steps can be linked and triggered

• Output from step 1 = input for step 2

• Automation of administrative processes

• Flow of work through departments not reliant on human intervention

Integration introduces latency

Level Value-add PhysicalManagement Information aggregation Supplier relationship Decisions Information visibility PPV control

Rules Driving administration Approve for paymentProcess Data drives workflow Receipt to PO matchTransaction Data integrity* Supplier delivery

Latency

Integration ≠ data integrity

• Data integrity means closing the gap between reality and it’s virtual mirror

• Golden data: right first time & all the time

• One version of the truth

• If business changes, rules need to change

But ERP cannot be changed

• Bureaucracy around change management

• In any case, the answer is no

• Business often asked to change process

• Investigative skills have been centralised

• Data integrity is now de-centralised

• So gap inevitably widens, requiring > BI

Writing the case studies

• The horizontal story–Making sense by theme

• The vertical story–Finding interdependence

Findings• Data integrity and investigative power

• Technical latency

• Planning versus actual data

• Coping with changing business models

• The widening functionality gap

• Use of BI tools to bridge the “gap”

How managers do their work

• What is happening? Actual

• What should be happening? Plan

• What therefore would happen if? What-if?

• Adjust plan and/or change actual Manage

Where do managers get their info?

• What is happening? ERP + BI

• What should be happening? Manual

• What therefore would happen if? ?

• View of actual data is improved …

But business models evolve

• High margin to high volume

• Hardware to software & service

• Manufacturing becomes logistics

• Gap opens between ERP & reality

The zipper

IS for management

Virtual

Physical

Actual Plan

Framework to model the gapDecision layer------------------

Reporting layer (pivots, broadcast rules, …)-----------------------------------------------

Consolidation logic and reconciliation (meta data)-----------------------------------------------

Interface layer (from planning systems)----------------------------------------------------------

Extraction layer (which tables, when, where, …)----------------------------------Transaction layer (ERP)

Themes• ERP no different than any other TPS

• Change Management bureaucracy

• Goal focus driving behaviour

• Hard vs. Soft goals

• Latency

GSK local “wins” – Jun 2005• Data integrity• Management accounting• Trust in data?

• But: no ability to change anything!• Diffusion of knowledge: JF

• PS. Both JF and his boss now left!

Post Go Live

• The technical support offsite is atrocious. We are very very disappointed with that. …

• Since the core team moved away, and since the next wave started for SAP implementation, they lost interest

Research on decisions difficult• Managerial decision making is fast

• Characterised by deadlines

• Difficult to prioritise in terms of importance

• Use “the best available information”

• Defining role of IS in DS is tricky

Solution: focus on goals• Unit of analysis is goal, not decision

• Managers define the goals, not researcher

• If it’s a goal, it must be important

• Measure IS role in goal achievement

• But does it work?

Post-ERP process improvement

Post-ERP projects

Focus ERP team role

Skills Sponsor

Firm A Plant vision

Supply chain, flow, lean mfg, taking “noise” out of planning process

Low In-house, business owner led, little IS

Mfg

Planning for 3rd party products

Planning None In-house, business owner led, no IS

Global Planning

KanBan Visual p lanning tool for mfg None

In-house, no IS, manual system

Site mfg

Firm B Energy Demand, sales effectiveness Low

30% in-house (business) 30% Oracle 30% Accenture

Finance / Sales

6Sigma, Lean

Reducing the variance Low

In-house, business & some IS

Directors in Mfg

KanBan Reduce inventory None

In-house, no IS, manual system

Site mfg

Why no role for ERP team or IS?

• Don’t have the skills?

• Don’t have the enthusiasm?

• …

• Neither company seems to be capitalising on ERP learning experience

Out

Post go-live example

• “There was an awful lot of resources thrown at go-live, most of those resources were gone after go-live. Trying to get something fixed, it wouldn’t happen.”

• “In order to actually utilize it in a way that actually improves our lot, took, is still taking, quite a long time, and if you can’t do it yourself, it’s even worse, because you can’t get IS available, at times to do the work.“

GSK local “wins” – Apr 2004• More efficient Sales and Distribution

processes

• Management of bulk solvent

• MRP for material spares

• Better procurement process

• Simplified labelling

• Real-time view of stock levels

Ownership - Apr 2004• Information

– Answer many reporting requirements = Excel

• Data – Single instance, remote server– Requirement for local data warehouse– 8 new people in full-time data maintenance

• Process– No more fasttrack orders!– Changes to template for local needs difficult– Subject to changes made on behalf of other sites

Findings• Goal setting in MNC’s seems to be increasingly cross

functional • Goal setting in MNC’s seems to be increasingly

quantitative “Load and chase” mentality• Integration implicit in ERP systems is cross functional• Impact of this integration on transaction processing is

explicit• Impact of this integration on decision making is not

explicit• ERP processes notoriously inflexible• Respondents vary in understanding of role of ERP

administrative processes

Impact on management decision making? • Jury’s out for operational performance• Many functions maintain local DW’s for decision

support• Data volumes become a performance issue• Questions could be refined:

– How well does ERP support the measurement of basic operational goals / metrics (revenue, capacity, inventory, billings, bookings, backlog, …)

– Can an off-line decision making model that requires actual figures be accurate if not integrated with ERP?

– At what point does the mixture of on-line and off-line manual systems become unreliable?

Outcomes • Framework for assessing the value and impact

of “integration” across business functions• Decision process orientation in analysing

managerial requirements (not transactional / data oriented)

• (Management by Objective) MBO strategies to include education on decision making processes and tools for their achievement

• Creation of technostructure