Case Study: McCormick Opportunity Analysis

Worth $2 billion McCormick commonly known as manufacturer of food and beverage ingredients. Based solely on publicly available information, this section shows you step by step how McCormick might analyze its Business Intelligence (BI) opportunities and applied that analysis to improve its profits and operating effectiveness. McCormick sells materials to the food and beverage processors, after processing, they sells them to retailers of food and beverages. The food industry is a mature, fragmented, international industry that is undergoing substantial structural changes typical of industry evolution.

Changes in the food and beverage retailing industry affect the food and beverage processing industry (McCormick’s customers). The resulting changes to the food and beverage processing industry affect McCormick’s business. From a BI strategy perspective, this case study most interested in changes that affect McCormick’s customers and how they make money. The nature and extent of those changes may create opportunities for McCormick to use BI to its strategic and competitive advantage.

Evolution of McCormick’s Relevant Industries
There are three key industries were relevant to McCormick’s BI planning: the food and beverage retail industry, the food and beverage processing industry, and the food and beverage ingredients industry. The food and beverage retail industry has historically been fragmented and regional. By 1999, however, it had become increasingly concentrated and global. In the United States, the top 10 supermarket players generated 33% of industry sales in 1995, but by 1999 that figure stood at 45%, not counting Wal-Mart’s 12% market share.

By 2004, the industry structure had the top 10 players holding between 55% and 70% of the market. Overall, the industry is a mature, consolidating, slow-growth industry with intense competition based on price, which means food and beverage processors receive pressure from the retailers to reduce prices, improve supply chain effectiveness, and differentiate themselves on more than just brand image. It is typical that mature industries spawn more aggressive competition based on cost and service, and that is certainly the model that Wal-Mart has used effectively in the consumer packaged goods industry. It is also typical that profits in the mature industry often fall, sometimes permanently.

The food and beverage processing industry is affected by trends at the retail level. The McCormick company’s primary interest is in identifying the major trends and their likely impact on the bases of competition in its ingredients businesses. This will suggest potential areas where McCormick can leverage BI.

The balance of power between food retailers and food processors shifted from 1995 to 2005 in favor of the retailers, which continues to put pressure on pricing and profits. The food processing companies most threatened by retailer consolidation are those with lower-ranking brands. In addition, slow domestic economic growth has intensified competition, motivated global expansion, and driven business process reengineering projects seeking improved margins.

Many industry leaders spent the mid-90s engaged in cost-cutting initiatives and backward integration into the ingredients industry, and such initiatives have returned as much benefit as they are likely to in the short term. Thus, the food and beverage processing industry is consolidating, which increases buyer power in relation to McCormick and its competitors. Looking forward, McCormick can continue to expect pricing pressures and demands for increased efficiency as its customers seek to maintain their own profitability in the face of slow growth and retailer consolidation.

The food and beverage ingredients industry is similar in structure to that of the related downstream industries: mature, slow growth, fragmented, and increasingly global. Faced with increasing customer power owing to concentration and supplier consolidation programs, price pressures due to customer industry dynamics, and the threat of backward integration, ingredients industry firms are themselves merging in an attempt to maintain some balance of power.

Although the overall growth rate of the ingredient industry is low, opportunities for growth in excess of the industry average are present. Industry players segment the market into what might be called macro-categories, for example, beverages, baked goods, dairy, candy/confection, and snack foods. These macro-categories have different growth rates, different leading brands, and different rates of new product development, all of which contribute to different opportunity profiles and growth potential.

Consistent with this overall environment, McCormick has successfully executed a strategy that is at once focused, differentiated, and based on cost leadership. McCormick is focused because it is only in the ingredients business. It is differentiated because its customer-based product development paradigm was at one time a singular position in the industry, and because it offers a broader product line than its competitors. The McCormick strategy is also based on cost leadership because it consistently focuses on margin improvement, global sourcing, and supply chain management (SCM) as means to achieve low-cost producer status.

Summary of Food Industry Drivers and Trends
Given the multiple levels of consolidation in the industry, each customer relationship takes on increased importance. At the same time, it’s also imperative to improve costs, pricing, customer selection, and customer revenue management. This suggests that growth and profitability could be enhanced by effective use of BI that supports those objectives. It also suggests that customer-focused business strategies and operating policies will be at least as important as, and probably more important than, they have traditionally been. Accordingly, BI capabilities that promote top-caliber customer service and make it easy to do business with McCormick are also important. A summary of the food industry drivers and trends is shown below:

Click on image to enlarge

Application of the Business Intelligence Opportunity Analysis Framework at McCormick
Working with the publicly available facts described above, the BI opportunity analysis framework can be applied to systematically identify specific opportunities to use BI to improve profits at McCormick. Both top-down and bottom-up BI opportunity analysis techniques can be used. Although top-down techniques begin with a strategic view and work down into an operational view, many business users are more comfortable discussing operational priorities. In this case, bottom-up techniques are used to discuss BI in relation to business processes, and determine how it can be used to support business strategies and the achievement organizational goals and objectives. The analytical results, abbreviated for sake of illustration, might look like this:

Business Drivers:
  • Consolidation
  • Wal-Mart factor
  • Increased pricing pressures
  • Slow growth
  • Global expansion
  • IT as a competitive weapon
McCormick Business Strategies, Goals, and Objectives:
  • Retain/increase revenue and market share through developing a broad line of differentiated products and services.
    Reduce costs and improve service through strengthening supply chain collaboration and improving sales forecasting.
  • Improve profits by utilizing customer segmentation approaches to identify the most profitable customers and retain these customers by providing high-quality, differentiated service and support.
  • Preserve margins by refining pricing strategy to determine the potential short-term and long-term cost/benefit of adjusting prices for different customers and segments; make pricing decisions based on cost/benefit analysis.

McCormick Business Design
Value Disciplines
  • Customer knowledge
  • Consumer-focused product development
    Leveraged IT
    Continuous process improvement
    Niche focus
Core Business Processes
  • Product development
  • Customer service
  • Supply chain management (SCM)
  • Manufacturing
  • Financial planning and control

Using Business Intelligence to Capture Business Value

In economic terms, the business value of an investment (an asset) is the net present value of the after-tax cash flows associated with the investment. For example, the business value of an investment in a manufacturing plant is the sum of the incremental after-tax cash flows associated with the sale of the products produced at the plant. Similarly, an investment in BI creates an asset that must be used to generate incremental after-tax cash flow. Accordingly, BI investments should be subjected to a rigorous assessment of how the investment will result in increased revenues, reduced costs, or both.

Although there are hundreds of ways to express business benefits, no business value is associated with an investment unless the benefits achieved result in increased after-tax cash flows. Again, there is no business value associated with an investment unless the benefits achieved connect to strategic goals. For business, the focus is on primarily increased after-tax cash flows; for government agencies, improved performance and service to citizens. These principles apply to investments in factories, equipment, and BI.

For example, it is common for BI vendor value propositions to emphasize business benefits such as agility, responsiveness, customer intimacy, information sharing, flexibility, and collaboration. But investing in BI to achieve such business benefits may actually destroy business value unless those attributes can be defined in operational terms and realized through business processes that affect revenues or costs. For example, a $2 million investment in a BI application must result in incremental after-tax cash flow of at least $2 million or the organization will suffer a reduction in assets.

To illustrate this point, many companies use BI to improve customer segmentation, customer acquisition, and customer retention. These improvements can be linked to reduced customer acquisition costs, increased revenues, and increased customer lifetime value, which translate to increased after-tax cash flows. However, a BI investment that improves demand forecasting will not deliver business value unless the forecasts are actually incorporated into operational business processes that then deliver reduced inventory, reduced order expediting costs, or some other tangible economic benefit. In other words, the business benefit “improved forecasting” is useless unless it is somehow converted into incremental after-tax
cash flow.

Looked at more broadly, the quest for delivering business value via BI can be seen
as a matter of determining how an organization can use BI to:
  • Improve management processes (such as planning, controlling, measuring, monitoring,
    and/or changing) so that management can increase revenues, reduce costs,
    or both
  • Improve operational processes (such as fraud detection, sales campaign execution,
    customer order processing, purchasing, and/or accounts payable processing) so
    that the business can increase revenues, reduce costs, or both


The Origins of Business Intelligence

Now that we have a better understanding of what BI is, let’s take a brief look at its origins. This examination will help show where BI fits with other parts of the IT portfolio, such as enterprise transactional applications like enterprise requirements planning (ERP), and will help differentiate BI uses from other IT uses. It’s also important to understand that enabling BI technologies are mature, low-risk technologies that have been used successfully by major companies for more than a decade.

Although recently the term BI has become one of the new IT buzzwords, the organizational quest for BI is not new. Approaches to BI have evolved over decades of technological innovation and management experience with IT. Two early examples of BI are :

  1. Decision support systems (DSSs): Since the 1970s and 1980s, businesses have used business information and structured business analysis to tackle complex business decisions. Examples include revenue optimization models in asset-intensive businesses such as the airline industry, the hotel industry, and the logistics industry, as well as logistics network optimization techniques used in industries that face complex distribution challenges. DSSs range from sophisticated, customized analytical tools running on mainframe computers to spreadsheet-based products running on personal computers. DSSs vary enormously in price and sophistication and are application-specific. Accordingly, they have not systematically addressed integration and delivery of business information and business analyses to support the range of BI opportunities available to companies today.
  2. Executive information systems (EISs): These were an early attempt to deliver the business information and business analyses to support management planning and control activities. Principally used on mainframes and designed only for use by upper management, these systems were expensive and inflexible. As BI applications and high-performance ITs have come to market, EIS applications have been replaced and extended by BI applications such as scorecards, dashboards, performance management, and other “analytical applications.” These applications combine business information and business analyses to provide custom-built and/or packaged BI solutions.



What Is Business Intelligence?

If that’s what BI is not, then what is it? BI combines products, technology, and methods to organize key information that management needs to improve profit and performance.
  • A single product. Although many excellent products can help you implement BI, BI is not a product that can be bought and installed to solve all your problems “out of the box.”
  • A technology. Although DW tools and technologies such as relational databases ETL tools, BI user interface tools, and servers are typically used to support BI applications, BI is not just a technology.
  • A methodology. Although a powerful methodology (such as the our BI Pathway) is essential for success with BI, you need to combine that methodology with appropriate technological solutions and organizational changes.
More broadly, we think of BI as business information and business analyses within the context of key business processes that lead to decisions and actions and that result in improved business performance. In particular, BI means leveraging information assets within key business processes to achieve improved business performance. It involves business information and analysis that are:
  • Used within a context of key business processes
  • Support decisions and actions
  • Lead to improved business performance


For business, the primary focus is to increase revenues and/or reduce costs, thereby improving performance and increasing profits. For the public sector, the primary focus is service to citizens, coping with budget constraints, and using resources wisely in support of an agency’s mission.

Toyota: From Excel to Business Intelligence

This article illustrates a typical case in which information flow could not meet the needs of managers. Information was late sometimes inaccurate and not shared by all. The old system did not meet the needs to make fast decisions, evaluate large amounts of information that was stored in different locations and collaboration. The solution is a technology called business intelligence (BI) which is based on data warehouse and provides a strategic advantage.

Problem
Toyota Motor sales USA (TMS) sells its vehicles to Toyota dealers across the USA used to take 9 to 10 days in transit and an average vehicle costs about $8 per day to keep while in transit. The financial charge was $72 to $80 per car. For 2 million cars per year the cost to the company was $144 to $160 million. The company faced increased problems in its supply chain, operations and car-keeping mounted in the late 1990s. Disappointed with the inability to deliver cars to the dealers unhappy customers purchasing cars from competitors such as Honda. The competition became intensified in 2003 and 2004 when hybrid cars were introduced by Honda. TMS managers used computers that generated huge numbers of directionless reports and data also unable to use such data and reports strategically. Internal departments regularly failed to share information or they did it too slowly. Actionable reports were often produced too late and overlapping reporting systems provided data that were not always accurate. Managers were unable to make timely decisions because they were not certain what portion of the data was accurate. The situation was especially dire in the Toyota logistic Services (TLS) division which is manages the transport of vehicles. The TLS managers require precision tracking and supply chain management to ensure that the right cars go to the right dealers in a timely manner. Manual scheduling and other related business processes that were conducted with incorrect information caused additional problems. If one individual made a data entry mistake when a ship docked the mistake would endure throughout the entire supply chain. The mistake maybe indicated to managers that ships never made it to a port weeks after the ships had safe docked. The information technology (IT) organization was unable to respond to the growing needs to the business.

Solution
A new chief information officer (CIO) was hired in 1997 in order to fix the problems. Barbara Cooper the new CIO started by trying to identify the problems. Cooper realized that a data warehouse was needed. A data warehouse is a central repository of historical data organized in such way that it is easy to access using a web browser and it can be manipulated for decision support. Cooper also saw that software tools to process, mine and manipulate the data were needed. A system therefore set up to provide real-time, cal data input into the system included years of human errors that had gone unnoticed including inconsistent duplicated data as well as missing data. The new system lacked capabilities to provide what managers needed. By 1999 it had become clear that the solution did not work. It was the right concept but used the wrong technology from the wrong vendors. In 2000 Toyota switched business intelligence platform. The system also included Hyperion’s dashboard feature which allows executives to visually see hot spots in their business units and investigate further to identify problems and their causes. With the new TLS system which uses colors meaningfully (red for danger) a business manager can see in real-time such as when delivery times are slowing and can immediately find the sources of the problems and even evaluate potential solutions by using ‘what-if’ analysis.

Results
Within a few days the new TLS system started to provide eye-popping results. The system helped managers discover that Toyota was getting billed twice for a specific rail shipment an $800,000 error. Overall Toyota USA managed to increase the volume of cars it handled by 40 percent between 2001 and 2005 while increasing head count by just 3%. In addition in-transit time was reduced by more than 5%. Word of the success TLS new business intelligence quickly spread throughout Toyota USA and then all over the company and many other areas of the company started to adopt BI. The more people who use data analysis tools the more money Toyota can earn. The TLS system was upgraded in 2003 and 2005 and tools are continuously added as needed. Toyota Motor Corporation reached the highest profit margins in the automotive industry in 2003. Toyota’s market share has increased consistently.
An independent study by IDC,Inc. indicates that Toyota achieved a 506% return on its BI investment.
 
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