Read the case study below and answer ALL the questions that follow.

BRINGING A REAL$-$WORLD EDGE TO FORECASTING

CFOs know what a $$good$$ forecasting process should look like: it should be accurate and comprehensive but flexible

enough to inform a range of critical business decisions$$capital reallocation, hiring, strategy, sales, production, and more.

But CFOs also recognize that there is no $$typical$$ forecasting process: it will look different in different organizations based

on sector$-$specific factors, feedback cycles, and, most critically, how the forecast is being used. A maker of packaged foods

that is releasing new products every quarter will rely on the forecast to keep a close watch on inventory, while a mining

company that is considering new plant construction over the next three years will use the forecast to predict capacity and

pricing. What$$s more, companies$$ access to ever$-$larger data sets continues to complicate the forecasting process as much

as it enlightens it$,$ leading to even more variety in how forecasts are built. Many of the $130$ CFOs surveyed in a recent

study say they now run more than one type of forecasting process in their organizations$$rolling forecasts to manage the

business, and ad hoc processes to make specific decisions.

But while many of the CFOs surveyed expressed general satisfaction with the results of their forecasting efforts, some $40$

percent also indicated that their forecasts are not particularly accurate and that the process takes far too much time. That$$s

likely because they use financial measures rather than operational outcomes as indicators of forecasting effectiveness$$if

they review the success of their forecasting efforts at all. A focus only on financial inputs can mask big issues with

companies$$ forecasting processes. By contrast, incorporating real$-$world operations insights into the financial$-$forecasting

process can help CFOs and finance teams predict bottom$-$line issues early, based on a careful assessment of quality,

operations, and customer$-$retention measurements. Senior leaders can then address performance issues before they

become big problems, and the incentives of even the smallest subunit of the business would be targeted toward long$-$term

value creation.

Integrating operations data within forecasts won$$t be easy, of course. Finance and business leaders will need to let go of

traditional budgeting mindsets and explore new ways of working together. The good news? Automation and other digital

technologies now make that easier. And four criteria show promise for injecting more accuracy and reliability into

forecasting models, regardless of industry: build a momentum case separate from the business plan, use a variety of

operational and external inputs, automate the forecast, and measure effectiveness with a fine$-$grained level of detail.

The typical forecasting process follows a pattern that contributes to inaccurate projections and a defeating, self$-$reinforcing

cycle.

At one large industrial manufacturing and services company, for instance, managers in the business units and subunits are

held to earnings targets that are rolled up into the overarching forecast. Over the years, these managers have become

adept at finding the one or two things that will help them make their number, often at the expense of longer$-$term investment

in quality, customer retention, and operational efficiency. Under the typical finance$-$focused forecasting exercise, no one

checks operational metrics so long as the bottom line comes in strong, which it had for a while. Now the company$$s performance is stuck in low gear: the most successful business units keep committing to higher numbers that eventually lead

to a deterioration in quality, while the least successful businesses get scrutinized, adjusted, and fixed. The winners and

losers flip, and the cycle repeats itself. Some companies have worked at breaking this disappointing pattern. They have

begun rethinking how they measure the success of their forecasting processes$$focusing on some of the following

questions:

Have we built a momentum case? Many financial$-$planning and analysis $($FP&A$)$ teams spend most of their time looking at

historical data to explain current outcomes. When they do get to look forward, they are likely focused on the budget, or on

rolling up commitments from different business units into the overall business plan. Sometimes the business plan itself

passes for the forecast. This, of course, just creates an echo chamber. No one is explicitly discussing how external factors

and impending market shifts could affect forecasts. A better approach is to create a market$-$momentum case that relies on

internal and external data as well as end$-$market trends to build the forecast. Once this unbiased momentum case is in

place, senior managers can layer new and additional market information on top. Then any initiatives, investments, and

strategic moves can be assessed relative to the base