06 Ashish Arya PGDM Marketing 8

8/8/2019 06 Ashish Arya PGDM Marketing 8

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Reducing Gaps in ForecastingReducing Gaps in Forecasting

OBJECTIVE: To increase the efficacy of business

decisions based on Market Forecast Data by reducing

the gaps in forecasting

By:Ashish Arya

Roll No-6

PGDM Marketing



OverviewOverviewy Cisco has highly variable demand and a complex mix of short (for

example, Internet Protocol [IP] telephony) and long (for example,data center infrastructure) product life cycles. Therefore, Cisco'smanagement could not rely only on the forecasting models intraditional demand-planning applications.

y Cisco employs a variety of forecasting models to improve forecastaccuracy, including some that are Cisco-specific.

y At the end of the cycle, the demand-planning team comparesforecasts and the output of the entire process with actual results,feeds that information back into the process, and uses it to assesseveryone's performance.

y The consensus forecasting process, coupled with advancedanalytics, improved Cisco's forecast accuracy, reduced forecast bias,improved supply/demand balancing, increased inventory turns,

reduced excess and obsolete stock, and enabled Cisco to plandemand flexibly ³ in response to market run-ups as well asdownturns.

y From 2001 to 2008, Cisco's forecast accuracy increased by 20% to30%, and forecast bias fell by 60% to 80%.



The ChallengeThe Challengey Cisco outsources 95% of its manufacturing and has over 1,000 suppliers.

y Historically, Cisco based its forecasting on sales and marketing projections

y Cisco's business expanded and the complexity and mix of products increased, this

approach to demand forecasting did not suffice for supply chain execution

y In addition, improved efforts in implementing lean processes across the supply chain

reduced inventory buffers, typically used to hedge against forecast errors. Lean

processes also meant that Cisco's contract manufacturers increasingly relied on

Cisco's ability to communicate demand effectively and accurately.

Solution ApproachSolution Approach Environment had highly variable demand and with short product life cycles. Cisco

concluded that those types of forecasting systems better suited more predictable demand

patterns and longer product life cycles

In early 2006, Cisco's management created a statistical forecasting team. Over the

subsequent 12 months, the team grew to six forecast practitioners, most with advanced

degrees whose academic backgrounds included operations research, engineering and

decision science.Team members also needed strong communication skills.

Through a series of prototypes, the team developed a statistical forecasting capability

that would support the requirements



Inputs include: Weekly demand data

Cost

Product hierarchySales region

Market segment

Product life cycle attributes

Product class

Product road maps

Cisco's Statistical Forecasting Process for DemandCisco's Statistical Forecasting Process for Demand

Data and models feed into the overall analytical forecasting engine to produce anautomated forecast. Cisco can tune a model for a particular time frame or

product. For a given month, the automated process takes the median of allforecasts appropriate for a particular data stream.

The analytics team then considers whether the automated forecastneeds manual exception-handling, based on sample accuracy, the

previous month's forecast accuracy and the inclusion of new productsin the forecast.The team compares the manual and automated forecasts

to create a final forecast, which feeds into a demand-planningapplication used by a consensus demand-planning process

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Cisco's DemandCisco's Demand--ForecastingForecasting

System ArchitectureSystem Architecture

y The best forecasting processesinclude all available data and domainexpertise, and engage stakeholders togenerate, review and sign off the finalforecast. Therefore, Ciscoimplemented a consensus demand-

planning process EDW = enterprise data warehouse

ODS = operational data store

The process occurs on a monthly cadence. In addition to the forecast practitioners,

several other critical players participate At the end of the cycle, the team compares

forecasts and the output of the entire demand-planning process with actual results, feedsthat information back into the process, and uses it to assess everyone's performance

Inputs for the automated forecast are collected in the enterprise's data warehouse andoperational data store, which had been created previously to feed BI applications. The team

publishes its forecasts in the data warehouse, and people can access them via BI and

demand-planning applications.



Analyst Forecast Accuracy Naïve Accuracy Value

Added

A 75% 80% -5%

B 60% 60% -

C 55% 50% +5%

Concept of Value AdditionConcept of Value Addition

y The project included accountability in the process itself, as well as for the peopleexecuting it. Forecasts are checked against actual results, and the team uses these

comparisons to improve its models. The team established simple metrics to judge

performance, the most important of which is value added

y Cisco measures the value of each forecast by comparing it to a "naïve" forecast done in

the easiest way, such as by taking a six-month moving average. Thus, an analyst with a

higher forecast accuracy (Analyst A) may actually provide less value than someone with alower forecast accuracy (Analyst C). Value-added metrics show the team where it should

put resources and where a naïve forecast suffices.



Conclusion, RecommendationsConclusion, Recommendations

y From 2001 to 2008, Cisco's forecast accuracy increased by 20% to 30%, and forecast biasfell by 60% to 80%.

y Consensus (involvement of a cross-functional team-sales, production, inventory, financemanagers) is required to increase accuracy of the forecast and decrease the occurrence of whip-lash/bull-whip effect.

y Investing in a simple but effective forecasting package can also free up the time of valuable

personnel. All basic sales forecasting software packages evaluate the history of yourbusiness, extrapolate pertinent information, and offer a forecast of your company's future.

y Cisco got its priorities and processes right, and technology was the least importantcomponent. In other words, the project might have failed had Cisco focused primarily onimplementing analytic applications. Instead, the organization focused more on getting theright mix of people involved, creating a flexible, comprehensive process and defining theright metrics.

y The concept of Value Added Analysis plays a huge role in determination of success of anyforecasting technique and must be implemented before adopting any new process.

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06 Ashish Arya PGDM Marketing 8