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Business forecasting is a process used to estimate or predict future patterns using business data. Some examples of business forecasting include estimating quarterly sales, product demand, customer lifetime value and churn potential, inventory and supply-chain reorder timing, workforce attrition, website traffic, and predicting exposure to fraud and risk. Several powerful estimation functions are commonly used to perform business forecasting: time series analysis, causal models, and regression analysis. Business forecasting supports executives, analysts and end users in decision-making using decision support systems such as business intelligence. It involves predicting the future outcome of various business decisions. This includes the future of the business as a whole, the future  of an existing or proposed product or product line, and the future of the industry in which the business operates, to name a few. Forecasting is used to answer important questions, such as:

 

• How much profit will the business make?

 

• How much demand will there be for a product or service?

 

• How much will it cost to produce the product or offer the service?

 

• How much money will the company need to borrow?

 

• When and how will borrowed funds be repaid?

 

Businesses must understand and use forecasting in order to answer these important questions. This helps the company prepare for the future. It also helps the organization make plans that will lead to becoming a financially successful business. This article will discuss the reasons financial forecasting is important to an organization.

 

Why is Forecasting Important to an Organization?

 

Financial forecasting is important for several reasons. First, it enables management to change operations at the right time in order to reap the greatest benefit. It also helps the company prevent losses by making the proper decisions based on relevant information. Organizations that can create high quality and accurate forecasts are able to “see what interventions are required to meet their business performance targets” (Vadasz).

 

Forecasting is also important when it comes to developing new products or new product lines. It helps management decide whether the product or product line will be successful. Forecasting prevents the company from spending time and money developing, manufacturing, and marketing a product that will fail.

 

Business forecasting has always been one component of running an enterprise. However, forecasting traditionally was based less on concrete and comprehensive data than on face-to-face meetings and common sense. In recent years, business forecasting has developed into a much more scientific endeavor, with a host of theories, methods, and techniques designed for forecasting certain types of data. The development of information technologies and the Internet propelled this development into overdrive, as companies not only adopted such technologies into their business practices, but into forecasting schemes as well. In the 2000s, projecting the optimal levels of goods to buy or products to produce involved sophisticated software and electronic networks that incorporate mounds of data and advanced mathematical algorithms tailored to a company’s particular market conditions and line of business.

Business forecasting involves a wide range of tools, including simple electronic spreadsheets, enterprise resource planning (ERP) and electronic data interchange (EDI) networks, advanced supply chain management systems, and other Web-enabled technologies. The practice attempts to pinpoint key factors in business production and extrapolate from given data sets to produce accurate projections for future costs, revenues, and opportunities. This normally is done with an eye toward adjusting current and near-future business practices to take maximum advantage of expectations.

 

In the Internet age, the field of business forecasting was propelled by three interrelated phenomena. First, the Internet provided a new series of tools to aid the science of business forecasting. Second, business forecasting had to take the Internet itself into account in trying to construct viable models and make predictions. Finally, the Internet fostered vastly accelerated transformations in all areas of business that made the job of business forecasters that much more exacting. By the 2000s, as the Internet and its myriad functions highlighted the central importance of information in economic activity, more and more companies came to recognize the value, and often the necessity, of business forecasting techniques and systems.

 

Business forecasting is indeed big business, with companies investing tremendous resources in systems, time, and employees aimed at bringing useful projections into the planning process. According to a survey by the Hudson, Ohio-based Answer Think Consulting Group, which specializes in studies of business planning, the average U.S. Company spends more than 25,000 person-days on business forecasting and related activities for every billion dollars of revenue.

 

Companies have a vast array of business forecasting systems and software from which to choose, but choosing the correct one for their particular needs requires a good deal of investigation. According to the Journal of Business Forecasting Methods & Systems, any forecasting system needs to be able to facilitate data-sharing partnerships between businesses, accept input from several different data sources and platforms, operate on an open architecture, and feature an array of analysis techniques and approaches.

 

Forecasting systems draw on several sources for their forecasting input, including databases, e-mails, documents, and Web sites. After processing data from various sources, sophisticated forecasting systems integrate all the necessary data into a single spreadsheet, which the company can then manipulate by entering in various projections–such as different estimates of future sales–that the system will incorporate into a new readout.

 

A flexible and sound architecture is crucial, particularly in the fast-paced, rapidly developing Internet economy. If a system’s base is rigid or inadequate, it can be impossible to reconfigure to adjust to changing market conditions. Along the same lines, according to the Journal of Business Forecasting Methods & Systems, it’s important to invest in systems that will remain useful over the long term, weathering alterations in the business climate.

 

Business forecasting systems often work hand-in-hand with supply chain management systems. In such systems, all partners in the supply chain can electronically oversee all movement of components within that supply chain and gear the chain toward maximum efficiency. The Internet has proven to be a panacea in this field, and business forecasting systems allow partners to project the optimal flow of components into the future so that companies can try to meet optimal levels rather than continually catch up to them.

 

In integrated supply chain networks, for instance, a single company in the supply chain can enter slight changes in their own production or purchasing schedules for all parties to see, and the forecasting system immediately processes the effects of those changes through the entire supply chain, allowing each company to adjust their own schedules accordingly. With business relationships and supply chains growing increasingly complex–particularly in the world of e-commerce, with heavy reliance on logistics outsourcing and just-in-time delivery–such forecasting systems become crucial for companies and networks to remain efficient.

 

PERT and Steps in the planning process

Complex projects require a series of activities, some of which must be performed sequentially and others that can be performed in parallel with other activities. This collection of series and parallel tasks can be modeled as a network.

 

In 1957 the Critical Path Method (CPM) was developed as a network model for project management. CPM is a deterministic method that uses a fixed time estimate for each activity. While CPM is easy to understand and use, it does not consider the time variations that can have a great impact on the completion time of a complex project.

 

The Program Evaluation and Review Technique (PERT) is a network model that allows for randomness in activity completion times. PERT was developed in the late 1950’s for the U.S. Navy’s Polaris project having thousands of contractors. It has the potential to reduce both the time and cost required to complete a project.

The Network Diagram

 

In a project, an activity is a task that must be performed and an event is a milestone marking the completion of one or more activities. Before an activity can begin, all of its predecessor activities must be completed. Project network models represent activities and milestones by arcs and nodes. PERT originally was an activity on arc network, in which the activities are represented on the lines and milestones on the nodes. Over time, some people began to use PERT as an activity on node network. For this discussion, we will use the original form of activity on arc.

 

The PERT chart may have multiple pages with many sub-tasks. The following is a very simple example of a PERT diagram:

PERT Chart

 

 

 

 

The milestones generally are numbered so that the ending node of an activity has a higher number than the beginning node. Incrementing the numbers by 10 allows for new ones to be inserted without modifying the numbering of the entire diagram. The activities in the above diagram are labeled with letters along with the expected time required to complete the activity.

Steps in the PERT Planning Process

 

PERT planning involves the following steps:

 

1. Identify the specific activities and milestones.

2. Determine the proper sequence of the activities.

3. Construct a network diagram.

4. Estimate the time required for each activity.

5. Determine the critical path.

6. Update the PERT chart as the project progresses.

 

 

1.  Identify Activities and Milestones

 

The activities are the tasks required to complete the project. The milestones are the events marking the beginning and end of one or more activities. It is helpful to list the tasks in a table that in later steps can be expanded to include information on sequence and duration.

 

2.  Determine Activity Sequence

 

This step may be combined with the activity identification step since the activity sequence is evident for some tasks. Other tasks may require more analysis to determine the exact order in which they must be performed.

 

3.  Construct the Network Diagram

 

Using the activity sequence information, a network diagram can be drawn showing the sequence of the serial and parallel activities. For the original activity-on-arc model, the activities are depicted by arrowed lines and milestones are depicted by circles or “bubbles”.

 

If done manually, several drafts may be required to correctly portray the relationships among activities. Software packages simplify this step by automatically converting tabular activity information into a network diagram.

 

4.  Estimate Activity Times

 

Weeks are a commonly used unit of time for activity completion, but any consistent unit of time can be used.

 

A distinguishing feature of PERT is its ability to deal with uncertainty in activity completion times. For each activity, the model usually includes three time estimates:

 

• Optimistic time – generally the shortest time in which the activity can be completed. It is common practice to specify optimistic times to be three standard deviations from the mean so that there is approximately a 1% chance that the activity will be completed within the optimistic time.
• Most likely time – the completion time having the highest probability. Note that this time is different from the expected time.
• Pessimistic time – the longest time that an activity might require. Three standard deviations from the mean are commonly used for the pessimistic time.

 

PERT assumes a beta probability distribution for the time estimates. For a beta distribution, the expected time for each activity can be approximated using the following weighted average:

 

Expected time =  ( Optimistic  +  4 x Most likely  +  Pessimistic ) / 6

 

This expected time may be displayed on the network diagram.

 

To calculate the variance for each activity completion time, if three standard deviation times were selected for the optimistic and pessimistic times, then there are six standard deviations between them, so the variance is given by:

 

[(Pessimistic  –  Optimistic ) / 6 ]2

 

5.  Determine the Critical Path

 

The critical path is determined by adding the times for the activities in each sequence and determining the longest path in the project. The critical path determines the total calendar time required for the project. If activities outside the critical path speed up or slow down (within limits), the total project time does not change. The amount of time that a non-critical path activity can be delayed without delaying the project is referred to as slack time.

 

If the critical path is not immediately obvious, it may be helpful to determine the following four quantities for each activity:

 

• ES – Earliest Start time
• EF – Earliest Finish time
• LS – Latest Start time
• LF – Latest Finish time

 

These times are calculated using the expected time for the relevant activities. The earliest start and finish times of each activity are determined by working forward through the network and determining the earliest time at which an activity can start and finish considering its predecessor activities. The latest start and finish times are the latest times that an activity can start and finish without delaying the project. LS and LF are found by working backward through the network. The difference in the latest and earliest finish of each activity is that activity’s slack. The critical path then is the path through the network in which none of the activities have slack.

 

The variance in the project completion time can be calculated by summing the variances in the completion times of the activities in the critical path. Given this variance, one can calculate the probability that the project will be completed by a certain date assuming a normal probability distribution for the critical path. The normal distribution assumption holds if the number of activities in the path is large enough for the central limit theorem to be applied.

 

Since the critical path determines the completion date of the project, the project can be accelerated by adding the resources required to decrease the time for the activities in the critical path. Such a shortening of the project sometimes is referred to as project crashing.

6.  Update as Project Progresses

 

Make adjustments in the PERT chart as the project progresses. As the project unfolds, the estimated times can be replaced with actual times. In cases where there are delays, additional resources may be needed to stay on schedule and the PERT chart may be modified to reflect the new situation.

 

Benefits of PERT

 

PERT is useful because it provides the following information:

 

• Expected project completion time.
• Probability of completion before a specified date.
• The critical path activities that directly impact the completion time.
• The activities that have slack time and that can lend resources to critical path activities.
• Activity start and end dates.

Limitations

 

The following are some of PERT’s weaknesses:

 

• The activity time estimates are somewhat subjective and depend on judgment. In cases where there is little experience in performing an activity, the numbers may be only a guess. In other cases, if the person or group performing the activity estimates the time there may be bias in the estimate.
• Even if the activity times are well-estimated, PERT assumes a beta distribution for these time estimates, but the actual distribution may be different.
• Even if the beta distribution assumption holds, PERT assumes that the probability distribution of the project completion time is the same as that of the critical path. Because other paths can become the critical path if their associated activities are delayed, PERT consistently underestimates the expected project completion time.

 

The underestimation of the project completion time due to alternate paths becoming critical is perhaps the most serious of these issues. To overcome this limitation, Monte Carlo simulations can be performed on the network to eliminate this optimistic bias in the expected project completion time.

 

 

Q.2 Describe Time-Cost optimization Algorithm.

 

he process of shortening a project is called crashing and is usually achieved by adding extra resources to an activity. The steps involved in the project crashing are the following —

Step 1 — Schedule a project with all its activities at their normal duration as well as identify the critical path and critical activities