Pepsi Americas Case Study

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We want you to practice a business summary style – writing in a clear, structured manner, but not in paragraph form. If you feel you must write in a paragraph, then address the question at hand in bullets or tables (as appropriate) and then summarize your points in an executive summary-type paragraph. In general, you are attempting to efficiently and effectively make your arguments for a particular solution, describe the associated implementation tasks (“to dos”), identify the benefits and costs of the undertaking, and then drive your point home with additional considerations (risk management).

Reading materials and fill out "homework" attachment.

The thing I need to repeat is that do not use whole sentences to answer, use bullet point instead.

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1 BUSINESS INTELLIGENCE (PART 1): DECISION SUPPORT SYSTEMS MISM2301 Thursday May 31, 2018 Associate Teaching Professor Martin Dias 2 Agenda • Why think about decision-making processes? • What are decision-support systems? • Big data business intelligence • PepsiAmericas debrief • Next Session Planning 3 Wide range of BI & Analytics providers Guess who paid for this complimentary publication of the BI / Analytics Magic Quadrant? 4 Revisiting “decision making” de·ci·sion (dĭ-sĭzh'ən) n. 1.The passing of judgment on an issue under consideration 2.The act of reaching a conclusion or making up one's mind 5 Deciding = work “The first rule for anyone hoping for improved decision-making is to view decisions as work.” (Thomas Davenport, HBR, 2013) 6 Decision making process (routine) Input Process Goal Was goal met? Output Feedback Decision Adapted by Martin Dias from original by Mike Zack 7 Decision making process (problem-oriented) What is the problem? What are solution alternatives? What is best solution? Performance feedback (adapted from Simon, 1960, as cited in Laudon and Laudon 2011) 8 Decision Making Process • Intelligence: • Specify desired situation (goal/hypothesis) • Observe current situation (feedback) • Describe gap between current and desired situation (control) • Design: • Identify alternative actions to close gap • Determine expected result/payoff of each alternative • With some probability of success (just like other risk analyses) • Choice: • Choose best alternative (weighted matrix) • Based upon highest expected “payoff” / return-on-investment • Implementation: • People, process, and technology considerations Feedback & Learning cycle: repeat a step as needed Adapted by Martin Dias from original by Mike Zack 9 Levels Making Levelsof of Decision Information Processing Learning at scale - capture & disseminate Learning/Strategic Input Process Output Innovate Control/Tactical Input Process Output Informate Transaction/Operational Input Process Output Automate Adapted by Martin Dias from Original by Mike Zack 10 Decisions support by Organizational Level Structure Low Not by accident High 11 Types of Business Decisions - by Level • Strategic - Affect the long-term direction of the firm • Unstructured problems and solutions (“wicked problems”) • Exploring and exploiting value in opportunities and threats • Involve external sources of data and multiple decision-makers • Are we doing the right things? • Tactical - Medium-term outcomes about implementation of strategy • Either structured or unstructured • Discovering methods to optimize resource utilization / value • Are we doing things right? • Operational – Daily, routine, administrative in nature • Generally structured/highly structured • Identifying best means of delivering value (best depends on value discipline) • Is this transaction an exception and what do I if so? Adapted by Martin Dias based upon originals by © 2009 Luth Computer Specialists, Inc. 12 Types of Business Decisions – by structure Structured Unstructured • DM process can be specified and codified • DM process can’t be easily specified or codified • Tend to be repetitive/routine • Often novel or ad hoc What can we digitize for unstructured decision-making? 13 Decisions vary in structure Structured Semi-structured Unstructured Insights • human interaction to specify issue before solving Algorithms • Set formula • Set procedure Heuristics • Rules of thumb • If/then conditions • Automation target • Semi-automation target • Not automated • DSS can compute the • DSS can infer (not compute a solution) optimal solution • E.g., delivery route, recommended product Operational DSS • E.g., Shift scheduling Expert System • DSS can support collaboration • E.g., NPD Group-DSS Adapted by Martin Dias from originals by Michael Zack 14 Decision Support Systems Information What to do? When to? Where to? How to? Decision Support System Source: Michael Zack 15 DSS and Value Disciplines Customer Intimacy Competitive advantage zone Survival zone Improved Decision Making zone 3 Value Product Leadership Operational Excellence (Treacy & Wiersema 1993) 16 Decisions Support Systems Why would you pay more for machine/deep learning capabilities? (Laudon & Laudon 2011) Using DSS • What-if Analysis • Sensitivity Analysis • Goal-Seeking Analysis • Optimization Analysis Source: Michael Zack OLAP and Data mining MIS 19 Online Analytical Processing (OLAP) • Multidimensional: multi-faceted statistical and mathematical analysis using many variables (attributes) • Created especially to handle this efficiently: • Real-time analysis & Output : immediate vs batch • Computation: compute the model-driven combinations and store as an N-dimensional “cube” • Customers with incomes greater than $100,000 and who have a mortgage with us also tend to have car loans with us but not mutual funds.” • Visualization: User can select /view output as a “cube” • Pivot tables in Excel and Access • Outputs as 3-D figures that can be “rotated” • Interactive: user changes the variables, filters, and sorting Adapted by Martin Dias from original by Mike Zack Online Analytical Processing (OLAP) OLAP Server Internal External Business Data Databases sources User Device OLAP Database Software for visualization and interaction IT components: …hardware …software …data …networking Pre-determined models • Database Indexing • Pre-computed data combinations Middleware for data aggregation • Data retrieved from business databases • Staged in OLAP multidimensional database for retrieval by client Adapted by Martin Dias from original by Mike Zack OLAP Examples – looking at trends from different perspectives (time, product, location) Pivot (Pandre, A 2011) 22 DSS Output: Digital Dashboard example *Image from Dotnetcharting 23 Data Mining – looking for success factors Interesting (data-driven) relationships - e.g., • Market basket by demographics • Loan failure indicators • Fraud identification Social Network analysis Classic Data Cube Computed combinations Data mining software Source: Michael Zack 24 Data Mining process – looking for success factors Identifying important factors/predictors Interesting (data-driven) relationships Data mining software Image source: http://www.slidegeeks.com/ 25 Data mining output Source: http://www.decision-making-confidence.com/decision-trees-examples.html 26 Big Data Business Intelligence – 3Vs +1 Source: IBM (2013) 27 Big Data Business Intelligence – 3Vs +1 Source: IBM (2013) 28 Big Data Business Intelligence – 3Vs +1 Source: IBM (2013) 29 Big Data Business Intelligence – 3Vs +1 What’s left? Source: IBM (2013) …this? 30 Big Data Business Intelligence – 3Vs +1 Source: SAP & IDC (2013) …this? 31 Big Data Business Intelligence If this prediction bears out, what does it mean for your information systems investments? IDC predicts that “decision and automation solutions, utilizing a mix of cognitive computing, rules management, analytics, biometrics, rich media recognition software and commercialized high-performance computing infrastructure” If this prediction bears out, what does it mean for your career? “companies will begin to replace or significantly impact knowledge worker roles.” …companies will focus more on the optimal mix between human and machine capability and judgment.” If you take humans too much out of the equation, their decision making will atrophy, warned IIA, asking “If you don’t have experts, who will train the next generation of [machine learning] software?” Source: Press, Gil, "$16.1 Billion Big Data Market: 2014 Predictions From IDC And IIA," Forbes Magazine, December 12, 2013. 32 case discussion Describe the legacy IT architecture at PepsiAmericas before its Customer Optimization initiative. 2. Describe the information problems that drove PepsiAmericas to adopt a more aggressive attitude towards the capture, use, and dissemination of information. 3. Describe what investments in information systems were made to address these information needs (excluding DSS). 4. Explain the role of decision support systems (DSS) in enabling the business success of PAS in terms of customer intimacy and operational excellence. Your answer should include treatment of both structured and unstructured decisions. Describe one additional way that a DSS could potentially enhance information processing within a firm. 1. 33 DSS: Business Considerations When does an investment in DSS make sense? Integration Business Problems Drives Drives Alignment Alignment Information Problems Enables Enables Innovation MIS capabilities People Process Technology 34 case discussion Describe the legacy IT architecture at PepsiAmericas before its Customer Optimization initiative. 2. Describe the information problems that drove PepsiAmericas to adopt a more aggressive attitude towards the capture, use, and dissemination of information. 3. Describe what investments in information systems were made to address these information needs (excluding DSS). 4. Explain the role of decision support systems (DSS) in enabling the business success of PAS in terms of customer intimacy and operational excellence. Your answer should include treatment of both structured and unstructured decisions. Describe one additional way that a DSS could potentially enhance information processing within a firm. 1. 35 Pepsi invested significantly to optimize their customer interactions and value Figure 1 – IT Architecture PepsiAmericas Source: Beath & Ross (2010) 36 Takeaways for today? • What are different types of decisions and how can DSS be used to improve decisionrelated outcomes? • What are the different technology components for DSS? • What are business considerations for implementing DSS? 37 Next session planning • Due: • Textbook reading: Belanger et al (2016) Chapter 14 • Diagnostic quiz • Submit CDM-Smith case Session 15: PepsiAmericas Case Study Questions 1. What drove PepsiAmericas to adopt a more aggressive attitude towards the utilization of transaction data to run the business? a. . b. . c. . d. . e. add more liens as needed….. 2. What investments in information management capabilities were taken in this regard? Why were they obtained and how did they actually contribute? Information System Investment Information Management Need Contribution of the System in Operation 3. Establish for yourself a picture of PepsiAmericas before and after the decision to exploit the use of information to inform business processes across the enterprise. PepsiAmericas Before Its Investment in BI Operations: • . • . • . Management and Control: • . • . • . Planning, Corporate Learning, and Innovation: • . • . • . prepared by rmk 102518 PepsiAmericas After Its Investment in BI Operations: • . • . • . Management and Control: • . • . • . Planning, Corporate Learning, and Innovation: • . • . • . Page 1 PepsiAmericas Case Study Case description: In 2009 PepsiAmericas (PAS) was the world’s second largest manufacturer and distributor of Pepsi beverages, operating in the U.S. (69% of sales), central and Eastern Europe (26% of Sales) and the Caribbean (5% of sales). Net sales in 2008 totaled nearly $5 billion or 20% of PepsiCo’s total US beverage sales. A recession hit the U.S. economy, but PepsiAmericas was also faced with two more important long-term challenges: (1) a declining U.S. market for carbonated soft drinks, and (2) increasingly powerful retailers who were squeezing PAS profit margins. In addition, PepsiAmericas product line had moved from 35-40 products in the mid-1990’s to nearly 400 products by 2009. These developments forced PepsiAmericas to embrace a completely new operating model. In the past, distribution was handled by the local delivery person, who “owned” a particular route of retail customer stores. The delivery person would load his/her truck in anticipation of what was needed at each of his/her assigned locations. Over time, the delivery person knew what to expect and could pretty much address customer needs on a day-to-day basis. However, as Pepsi moved from 35 to 400 products and as the packaging for these products became less uniform, it proved difficult to know about, let alone carry in inventory, the right mix of products in the truck. Furthermore, chains like Wal-Mart, CVS, and Mobil Gas Stations, preferred highly centralized procurement arrangements and national contracts. Pepsi was therefore obliged to create a three-tier distribution platform that would address the needs of (1) national, (2) regional, and (3) local customers. In response to these pressures and challenges, PepsiAmericas invested heavily in supply-chain management (SCM) and enterprise resource planning (ERP) systems. With these systems the firm integrated its core business processes (i.e. procurement, manufacturing, selling, and warehousing and distribution) and automated data capture at every key step along its value chain. To PepsiAmericas, one of the biggest benefits of its ERPs was the collection and measurement of business process outcomes for better performance assessment, forecasting, and risk mitigation. The company used these rich data resources and related process knowledge to negotiate better contracts for raw materials, lower supply chain operating costs, more accurately monitor consumer demand, and ultimately strike more profitable deals with its large retail customers. In effect PepsiAmericas employed customer data as a competitive asset, collecting vast amounts of data as part of daily operations (transacting) and then employing that data for management and control as well as for innovation in product development and customer service. This transformation process was dubbed the “Customer Optimization to the 3 rd power – Planning + Selling + Delivery” program and was intended to reduce inventory management issues, increase productivity across PepsiAmericas’ production platforms, and improve overall customer service. For example, national customers, like Wal-Mart, fed point-of sales data directly into PAS’s SCM system, informing the detailed product mix and quantities going from PAS to particular sales location. And at the other end of the spectrum, those PAS employees serving the small local prepared for MISM 2301 by RMK, rev’d by MD Fall 2014 Page 1 PepsiAmericas Case Study shops had access to detailed historical sales data to forecast the requirements for local stores and to provide the right mix of products day in and day out. The ability to consistently adjust prices was also significantly improved. Prior to having integrated systems and aligned operations, PAS customers within the same ZIP code would have different prices depending upon who they dealt with in PAS and the pricing data available to that sales person. In addition, productivity for PAS customer service representatives rose noticeably. Figure 1 – IT Architecture PepsiAmericas (Customer Optimization to the 3rd Power) Source: Beath & Ross (2010) Continuous data feeds from PAS SCM and ERP systems to the firm’s decision support systems provided PepsiAmericas executives with ready access to real-time data to fine tune business processes and to promptly address performance and customer servicing issues. This same approach drove decisions concerning the acquisition of both additional production capabilities and new lines of products. It also contributed to the continuous improvement of ongoing firm business processes and services, and the shift to a more data-driven decision making culture across the organization. PepsiAmericas leadership employed their data assets to build competitive knowledge in three areas that were critical to their long-term success, namely: customer alignment and relationship management, supply-chain process improvement, and enabling more dynamic pricing across the company. PAS continues to mine data across the enterprise as a means to measure business results and to inform best practices. prepared for MISM 2301 by RMK, rev’d by MD Fall 2014 Page 2 PepsiAmericas Case Study Source: Beath, Cynthia M. and Jeanne W. Ross, “PepsiAmericas: Building an Information Savvy Company”, Feb. 2010, MIT Sloan School of Management Journal, accessed online http://dspace.mit.edu/bitstream/handle/1721.1/68550/Pepsi%20BeathRoss2.pdf Case Questions: For the below questions think first about the appropriate representation for your answer (bullet list, multi-part bullet list, table) and then the appropriate framework to ensure both accuracy and completeness. 1. Describe the information problems that drove PepsiAmericas to adopt a more aggressive attitude towards the capture, use, and dissemination of information. 2. Explain the role of decision support systems (DSS) in enabling the business success of PAS in terms of customer intimacy and operational excellence. Your answer should include treatment of both structured and unstructured decisions. Describe one additional way that a DSS could potentially enhance information processing within at PAS. prepared for MISM 2301 by RMK, rev’d by MD Fall 2014 Page 3
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OUTLINE
Introduction
Body
Conclusion
Reference


Session 15: PepsiAmericas Case Study Questions
1. What drove PepsiAmericas to adopt a more aggressive attitude towards the utilization
of transaction data to run the business?
a. Declining market share of carbonated soft drinks
b. Increased products variety inventory challenges
c. Resources optimization
d. Effect of powerful retailers reducing trade margins
e. Power of data in planning, selling and delivery integration
f. Shared resources SCM & ERP
2. What investments in information management capabilities were taken in this regard?
Why were they obtained and how did they actually contribute?
Information System
Investment
ERP
SCM
HRM
Pricing system
Delivery sales system

Information Management
Need
Central data storage
Information and products flow
Human assets optimization
Control pricing in the system
On time delivery of order

Contribution of the System
in Operation
Ease of access and usage
Ease flow of activities
People are assets
Master data, deals &prices
Invoices, orders, fulfillment

3. Establish for yourself a picture of PepsiAmericas before and after the decision to exploit
the use of information to inform business processes across the enterprise.
PepsiAmericas Before Its Investment in BI
Operations:
• 35-40 products
• Lack of data, poor decisions making
• Inconsistencies & delays
Management and Control:
• Large inventories
• At the mercy of large retailers
• Missed opportunities
Planning, Corporate Learning, and Innovation:
• Ad hoc and limited
• Complacency no innovation
• Inconsistent, inefficient

prepared by rmk 102518

PepsiAmericas After Its Investment in BI
Operations:
• Better efficiency and controls
• Predictable operations
• Data driven decisions
Management and Control:





Production matched to market need

Cu...


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