Management Process Is Key 1 Management Process How

Management Process Is Key 1. Management Process: How It Impacts the Organization 2. Best Practice of IT Strategy Driven by Management Process 3. - Pepsi-Cola Case Example 4. 3. Where to Position the Analytical System 5. 4. The Critical Success Factors Approach 6. - GE Power Systems Case Example 7. 5. The Balanced Scorecard 8. 6. Market-Driven Performance Measurement 9. - Dannon Yogurt Case Example 10. 7. Planning vs. Control Systems 11. 8. 12. 13. 9. 14. Expert Systems: Potential for Automating Processes - Ocean. Spray Case Example: Extracting News from Data Automated Decision Technologies: The New Frontier - Deep. Green Financial Case Example Dr. Lakshmi Mohan 1

Linking Strategy to Operations - A Closed-Loop Management System Stage 1 Develop the Strategy Stage 2 Stage 6 Translate the Strategy Into Specific Objectives And Initiatives of a Strategic Plan Reassess the Strategy And Update it Stage 3 Stage 5 Plan Operations -Map out the Operational Plans and resources to achieve objectives Monitor & Learn Stage 4 Execute the Strategy & Operational Plans Dr. Lakshmi Mohan 2 Source: R. S. Kaplan & D. P. Norton, “Mastering the Management System, ” HBR, Jan. 2008, p. 65

Management Process - The Glue that Holds the Organization Together EXTERNAL ENVIRONMENT: Social, Economic, Political The Organization’s Strategy Competitors Organization Structure and the Corporate Culture MANAGEMENT PROCESSES: Planning, Control, Rewards Individuals and Roles EXTERNAL ENVIRONMENT: Technological Technology Customers Adapted from M. S. Scott Morton & J. F. Rockart, “Implications of Changes in IT for Corporate Strategy, ” Interfaces, Jan-Feb, 1984 Dr. Lakshmi Mohan 3

Management Processes That Merit Examination • • Planning and Budgeting Performance Measurement and Reporting Resource Allocation Human Resource Management LESSON: Investments in providing good quality information are a waste if managers do not use the system. Hence, improve the management process first and then develop the information system to support the improved management process. Dr. Lakshmi Mohan 4

PEPSI-COLA CASE EXAMPLE PEPSI-COLA COMPANY Sell "Concentrate" PEPSI BOTTLERS Convert "Concentrate" into Products Bottles & Cans - Stores Fountain Syrup - Restaurants Vending CONSUMERS The Bottler has to be well-managed for the Pepsi-Cola Company to prosper. Dr. Lakshmi Mohan 5

Focus on the Management Process Identify the Revenue Drivers and Cost Drivers of the Bottler's operations. Develop the Management Process or "how-to" procedures for managing these drivers. An Information System to implement the "how-to" procedures. Dr. Lakshmi Mohan 6

PEPSI - COLA'S Thinking in 1982 - All Bottlers face essentially the same problems with regard to improving their operating efficiency and profitability. - Why should each Bottler reinvent the wheel? - Pepsi-Cola Company will make the investment in developing an information system that can be used by all Bottlers. - The PC will enable a "standard" software package to be developed for the Bottler Information System. Dr. Lakshmi Mohan 7

A Comprehensive Information System to Support the Management Process Developed over a three-year period: Phase I: Sales Analysis & Tank/Cylinder Monitoring Phase II: Equipment Tracking & Service Analysis Phase III: Financial Analysis Dr. Lakshmi Mohan 8

Information Provided by the System - Some Examples • How are My Route Drivers Performing? • What is the Average Drop Size for Each Route? • Are the Trucks being Overloaded? • What is the Sales Trend in Different Channels for Pepsi-Free? • Where are My Tanks? • How Many Pieces of Equipment are on Loan? • What is the Productivity of My Mechanics? • Which Makes of Equipment are Less Dependable? Dr. Lakshmi Mohan 9

Where to Position the Analytical System ? Initial Target of Most BI Systems: - Corporate Financial Data Why? - Area of great interest for top management - Data readily available - Easy to implement Is a Corporate Financial BI Really “Useful” ? Dr. Lakshmi Mohan 10

A Corporate Financial BI System Is “Useful” IF. . . It has Micro-Level Data e. g. Expenditures at the Detail Level Data on Individual Debtors by Ageing Period BI System can then Trace Problems to the Root Causes Dr. Lakshmi Mohan 11

The CSF Approach What Is It? A method to determine the information requirements to improve management effectiveness. Why? Existing MIS systems are “data-rich but information-poor” - too much financial data - unfiltered data - irrelevant operational data - no external environment data We need information about what really counts; just because data is available doesn’t mean it’s important. Dr. Lakshmi Mohan 12

Critical Success Factors are. . . The key areas where “things must go right” for the business to flourish They should receive constant and careful attention from management - Determine the Key Indicators Measure the performance of each indicator Determine the data needed for each indicator IDENTIFY MUST - DOs FOCUS BI ON MUST Dr. Lakshmi Mohan KNOWs 13

The Critical Success Factors Approach Identify the specific factors most responsible for the organization to achieve its goals Determine the KPIs for monitoring each CSF Define the data requirements for measuring each KPI Develop prototype system. Modify prototype based on user feedback Dr. Lakshmi Mohan 14

Goals vs. CSFs For-Profits GOALS • • CSFs • Automotive Industry Earnings per share – Styling Return on investment – Quality dealer system Market share – Cost control – Meeting energy New product success Dr. Lakshmi Mohan standards • Supermarket Industry – Product mix – Inventory – Sales promotion – Price 15

Measures for Tracking CSFs -- An Example Dr. Lakshmi Mohan 16

Merits of the CSF Approach 1. Focuses on what management absolutely needs to do and know -- not those which would be merely nice to do and know 2. Sets priorities in the Information Systems Plan 3. Limits data collection to what is necessary 4. Avoids the trap of building systems on data which is readily available or easy to collect 5. Forces consideration of external and soft data 6. Highlights the need for change in information systems in response to environmental and business changes Dr. Lakshmi Mohan 17

GE Power Systems: Focus BI on “MUST - DOs” Changing Business Focus: from new machine to replacement parts market from U. S. to international more price sensitive Increase Productivity Reduce Costs Improve "Customer Service" Dr. Lakshmi Mohan 18

What Are the “MUST - KNOWs” ? Quote Performance Shipment Performance New Data on "DATES" Dr. Lakshmi Mohan 19

Data On “Dates” Needed For BI · "QUOTE CYCLE" FROM: - Date of request for quote - Date of quote release · "REQUESTED CYCLE" FROM: - Date of order - Customer "want" date · "SHIP CYCLE" FROM: - Date of order - Date of shipment Tracking Cycle Times: Important For Changing Organization Culture From Technology-driven to Market-driven Dr. Lakshmi Mohan 20

Traditional Performance Measurement Systems Are Inadequate. . . • Each function has its own set of “results measures”. For example: Marketing - Market Share Operations - Inventory Finance - Costs These narrow functional measures could lead to overall system Suboptimization. • The few cross-functional results measures are financial: – Revenues, Gross Margins, Operating Income, Return on Investment, etc. – Report on what happened last period without indicating how managers can improve performance on the Dr. Lakshmi Mohan next 21

The Balanced Scorecard • Translates a company’s strategic objectives into a coherent set of performance measures with four different perspectives: – Customer – Financial – Internal Business Processes – Innovation and Learning • A limited number of critical indicators should be selected within each perspective - about 15 to 20. • Real benefit: Scorecard is the core of the management system - the way the business is run. Dr. Lakshmi Mohan 22

Rockwater’s Balanced Scorecard Financial Perspective Return-on-Capital Employed Cash Flow Project Profitability Profit Forecast Reliability Sales Backlog Customer Perspective Internal Business Perspective Pricing Index, Tier II Customers Customer Ranking Survey Customer Satisfaction Index Market Share Business Segment, Tier I Customers, Key Accounts Hours with Customers on New Work Tender Success Rate Rework Safety Incident Index Project Performance Index Project Closeout Cycle Innovation & Learning Perspective % Revenue from New Services Rate of Improvement Index Staff Attitude Survey # of Employee Suggestions Revenue per Employee Dr. Lakshmi Mohan 23

Market-based Performance Measures. . . MUST be linked to Reward Systems Measurement and reward systems are critical in developing a market-oriented business. Just as managers will emphasize those things that top management’s statements of beliefs and values focus their attention on, they will also do those things for which they are evaluated and rewarded Source : F. Webster, “Rediscovering the Marketing Concept, ” Marketing Science Institute Working Paper, 1988. Dr. Lakshmi Mohan 24

The Dannon Yogurt Case Example ü Critical Success Factor: Customer Service ü Key Performance Indicators : For Each Customer q Fill Ratio of Each SKU = Shipments Orders q Freshness of Product of Each SKU = Shelf-Life of Shipments ü New Data Requirements: q Fill Ratio of the SKU: Orders for that SKU from Customer q Freshness of Product of SKU: Code Dates on Containers of SKU Shipped to Customer (Available Shelf-Life = Code Date minus Ship Date) Result : The legacy Invoicing System, which only had data on Shipments, had to be replaced with a new system that captured the additional data needed for management control. Dr. Lakshmi Mohan 25

A Market-Driven Performance Measurement System ü Customer Service became the metric in Dannon for measuring performance of: Marketing Sales Distribution Production AND ü Uniform Performance Measurement is critical for integrating the important functional areas in the business process. Dr. Lakshmi Mohan 26

The Management Cycle Establish Objectives Formulate Plan Implement Plan Revise Objectives Replan Monitor Performance Control the Implementation, React to Deviations between Plan and Actual Dr. Lakshmi Mohan 27

Management Process: Planning and Control Plan • Deciding “Today” what to do “Tomorrow” • The Planning Horizon: Strategic Planning may be Years Budgeting may be a Year Production may be Weeks Implement • Get Organizational Buy-in • Restructure Organization if necessary Monitor • Measuring Actual Performance is Critical for Control • How has Actual Performance deviated from Plan? • What corrective action must we take? – Deviations may also represent an opportunity Dr. Lakshmi Mohan 28

A BI TO SUPPORT PLANNING IS NOT EASY TO DESIGN (1) Large number of available alternatives how to determine the "best" plan (2) Uncertainty about the outcomes how to evaluate the consequences (3) Multiple criteria for measuring outcomes how to combine "apples" and "oranges" Dr. Lakshmi Mohan 29

"SATISFICING" vs. "OPTIMIZING" IN DECISION-MAKING Choose a solution that is good enough using manager's rules of thumb or heuristics. Benefits: Saves time and cost Easy to implement versus Search for the best solution using an optimizing model Problems: Model may not fit the problem More data needed More time and cost Higher intellectual cost Dr. Lakshmi Mohan 30

MONITORING PERFORMANCE (1) WHAT ? What "readings" of actual performance should be taken? (2) HOW ? Where should the "meters" for taking the readings be placed? (3) HOW OFTEN ? How often should the "meters" be read? (4) HOW CURRENT ? How quickly should the "readings" be transmitted to management? (5) HOW ACCURATE ? How accurate do the "readings" have to be? Dr. Lakshmi Mohan 31

MONITORING THE MARKET What ? Sales: Volume? Value? Shares: Total Market? Segments? How ? Internal data Market research Call reports of sales force Dr. Lakshmi Mohan 32

MONITORING THE MARKET How Often ? Daily? Weekly? Monthly? How Timely ? Enough advance warning to steer around the iceberg How Accurate ? Satisficing versus Numerical accuracy Dr. Lakshmi Mohan 33

Aggregating Data Evens out Market Fluctuations BUT… must be balanced with burden of collecting data Dr. Lakshmi Mohan 34

REQUIREMENTS OF A BI TO SUPPORT CONTROL (1) Summary reports not useful they can hide problems (2) Exception reports a "must" for reducing data overloads (3) Drill-down capability to access detailed data to trace a problem to the root cause (4) Graphics capability facilitates comparisons (5) "What happened" is not enough analysis needed for "what if " (6) Intelligent drill-down to signal problems at a detail level Dr. Lakshmi Mohan 35

“WHAT IF” ANALYSIS - An Example • Standard Sales Performance Report – Actual vs. Target vs. Last Period – Variances – % Achievement of Target What are the implications of performance to-date? • THE Question : Will Annual Targets be achieved? • Compare Current Sales Speed with Balance-To-Achieve Sales Speed • BTA Speed = BTA Target / Balance # of Months Dr. Lakshmi Mohan 36

Standard vs. “What If” Sales Reports Standard Year-to-Date Sales Report for December Product Actual Target % of Target P 1 100 120 83% P 2 100 180 56% P 3 100 90 111% Totals 300 390 77% “What If” Sales Report for December Product Actual Annual Target BTA Current Speed P 1 100 150 50 11. 11 16. 67 50% P 2 100 240 11. 11 46. 67 320% P 3 100 120 20 11. 11 6. 67 (39%) Totals 300 510 210 33. 33 Dr. Lakshmi Mohan Fiscal Year: April-March BTA Change Speed 210% 37

What are Expert Systems? Ø A technology that enables expertise to be distributed in a firm without the presence of the human expert Ø Rule-Based System ―If “This”, Then “That” ―Rules are determined from expert knowledge and programmed in the software ―Feedback Loop to Improve Rules An HR Application Ø Screening a large number of resumes for relatively lowlevel positions with well-defined and precise skill requirements - e. g. , Call Center Agents Ø Expert System can weed out applicants who do not meet the requirements Dr. Lakshmi Mohan 38

Using Expert Systems: Extract News from Scanner Data The Promise: Better Data – Compared to Retail Store Audits – Frequency: Weekly vs. Bimonthly – Level of Detail: UPCs vs. Brands – Scope: Top 50 Markets vs. Regions The Problem: Too Much Data – At least 100 times more data The Result: Impossible to Use Quality Data Dr. Lakshmi Mohan 39

"Cover. Story"- An Expert System Replaced The Human Analyst Before. . . Companies circulated top-line reports, including tables and charts from the retail store audit data. An analyst prepared the cover memo highlighting important news in the data. Now. . . Not feasible to have an army of analysts to sift through the mountain of scanner data. Instead, "Cover. Story" automatically writes this memo ! – A model-imbedded expert system extracts the news from the data – Another expert system composes the memo using a built-in thesaurus to eliminate repetitious wording Dr. Lakshmi Mohan 40

Case Example: Ocean Spray Cranberries – A $1 billion grower-owned agricultural cooperative – Lean IS staff – Only one marketing professional for analyzing the scanner data – Scanner data for juices is imposing -- 400 M numbers covering up to 100 data measures, 10, 000 products, 125 weeks and 50 geographic markets -- Grows by 10 million new numbers every four weeks Dr. Lakshmi Mohan 41

Impact of Cover. Story – Enables a department of one to alert all Ocean Spray marketing and sales managers to key problems and opportunities and provide problem-solving information – Being done across 4 business units handling scores of company products in dozens of markets representing hundreds of millions of dollars of sales – System is totally integrated into business operations because it delivers information of competitive value in running the business Dr. Lakshmi Mohan 42

Automated Decision Technologies… … Are Coming of Age! - Best suited for decisions that must be made frequently and rapidly Information for making decisions is available electronically Knowledge and decision criteria must be highly structured Good Example: Bank credit decisions - … Repetitive, Uniform Criteria; Plentiful Customer Credit Data Available Poor Choice: Whom to Hire as CEO - … Rare Occurrence, Non-Standard Criteria (e. g. , personal chemistry) that cannot be captured in a computer model Benefits: … More Consistent Decisions … Respond Faster to Customers … Leverage Scarce Expertise … Reduce Labor Costs … Enforce Policies Dr. Lakshmi Mohan 43

Case Example: Deep. Green Financial Competitive Advantage – Automated Credit Decisions ü Targeted Customers: High-end Borrowers - Want a standardized loan for a small fraction of the value of their home ü Created an Internet-based system in 2000 - Customer completes home-equity loan application online in 5 minutes - Receives decision within 2 minutes in 80% cases ü No upfront paperwork from borrowers - Traditional appraisal of credit worthiness not required ü USP: Speed, Service and Convenience - NOT Interest Rates - Can tailor loan terms to needs of individual borrowers • Skimming off customers with the best credit • 8 Employees process 400 applications a day • 5 years: Originated over $4. 4 B of home-equity lines of credit Dr. Lakshmi Mohan 44

Automated Credit Decision Process Joan applies online for loan ( 5 mins. ) Joan’s Credit Report pulled out from Online Database - Joan’s Credit Score is calculated by system Checks for Fraud & Flood Insurance Joan’s Property Evaluation - Based on Online Data Loan Approved? (2 mins) NO End Dr. Lakshmi Mohan YE S System selects a Notary Public near to Joan’s home Joan chooses Closing Date Loan documents printed from system - express-mailed to the Notary visits Joan’s home to get signatures on loan documents 45

Deep. Green’s Shrewd Strategy Feasible Because … … All data needed to make home-equity loan decisions available online … Relatively easy to make credit decisions involving affluent, low-risk borrowers - Factors to be considered are well understood - Experts can readily codify the decision rules … High-end customers tend to be Internet-savvy - Offer them what they need online, and they will come Dr. Lakshmi Mohan 46

Lessons Learned 1. Identify Revenue Drivers and Cost Drivers with Significant Bottom-Line Impact – the “MUST DOs” 2. Examine the Current Management Process for Managing These Drivers - Improve the Process - Develop Detailed “HOW-TO” Procedures 3. Determine the Key Performance Indicators (KPIs) for Each Driver 4. Measure the KPIs - “Satisficing” Information is Enough - But, Macro Information is Useless - Micro Information Needed to Pinpoint Root Causes to Enable Corrective Action - “DE-AVERAGE” 5. Reward System Must Be Aligned to KPIs Dr. Lakshmi Mohan 47

What Is Not The Solution 1. Give Users Access to the Data - They will Drown in All That Data 2. System has Everything - An Overdesigned System: NOT Usable 3. “Build It and They Will Come” - Users need Help “What to Do with the Information”? Dr. Lakshmi Mohan 48

What Is The Solution - Some Pointers 1. A Rich Data Base - Business Problems Cannot be Solved in the Aggregate - Detailed Data Needed from Different Sources to Trace Problems/Opportunities to the Root Causes - Soft Data Vital in Marketing & HR 2. “Data” must be Converted into “Actionable” Information - System Design Must Enable Users to Glean the “News” in the Data 3. A Compact System - Ability to Drill Down to Detailed Data Where Needed 4. Problem-Finding Capabilities Built into the System - Ranking, Exception Reports, Trend Graphs 5. System Must be Inviting to Use Dr. Lakshmi Mohan 49