Planning for Small Manufacturing Projects

By Fareed Nasr

Since the financial position and cash flow of the small manufacturing projects may not allow them to employ expert planners, the main objective of this book is to enable non-specialists to plan for their small manufacturing projects without the need to recruit or hire experts. This Book presents the planning techniques of sales, production orders, end items components, and production capacity in a simple description. Because the firms need to convert their quantitative plans to financial budgets, the book illustrates how to prepare such budgets and use them in planning and control of financial transactions.

• Language: English
• Publisher: Fareed Nasr
• Release date: Sep 29, 2018
• ISBN: 9780463074251

Fareed Nasr

Bachelor in commerce, Accounting section. MS Dynamics GP Implementer and consultant for fifteen years Financial manager for twenty years. Certified in MS Dynamics GP Project Accounting

Book preview

Planning for Small Manufacturing Projects

Fareed Nasr

Copyright 2018 by Fareed Nasr

Smashwords Edition

Smashwords Edition, License Notes

This book is protected under the copyright laws of the United States of America. Any reproduction or other unauthorized use of the material or artwork herein is prohibited.

Table of Contents

PREFACE

CHAPTER 1 SALES PLANNING

Sales Forecasting

Qualitative Methods

Quantitative Methods

CHAPTER 2 PRODUCTION PLANNING

Aggregate planning

Linear Programming

Aggregate planning Application

Master Production Schedule

Inputs of Master Production Schedule

Processes of Master Production Schedule

Output of Master Production Schedule

Rough-Cut Capacity Planning (RCCP)

Capacity planning using overall factors (CPOF)

Capacity Planning Using Bill of Resources (BOR)

Capacity Planning Using Resource Profile

CHAPTER 3 PRODUCTION COMPONENTS PLANNING

Material Requirement Planning (MRP)

Inputs to MRP System

MRP System Processing

Outputs of MRP System

MRP Application

Capacity Requirement Planning (CRP)

Objects used in capacity requirement planning

Capacity requirement planning subsystem

CRP Application

Shop Floor Control

Order Release

Order Scheduling

Priority Control

Order Progress

Reorder Point Planning

Level of Reorder Point

Economic Order Quantity (EOQ)

CHAPTER 4 OPERATING BUDGETS

Using Budgets in Planning

Standard Costs

Sales Budget

Production Budget

Materials Purchasing Budget

Direct Labor Budget

Manufacturing Overhead Budget

Cost of Goods Sold Budget

Administrative Expense Budget

Budgeted Income Statement

Using Budget in Control

Direct Material Variance

Direct Labor Variance

Manufacturing Overhead Variance

Flexible Budgets

CHAPTER 5 FINANCIAL BUDGETS

Capital Budget

Identifying the alternatives

Capital Projects Evaluation Techniques

Development of Capital Budget

Cash Budget

Preparation of Cash Budget

Cash Budget Form

Budgeted Balance Sheet

Preparing of Budgeted Balance Sheet

Budgeted Cash Flow

Cash Flow from Operations

Cash Flow from Investments

Cash Flow from Financing

REFERENCES

Preface

Small manufacturing businesses may not have sufficient financial capacity for hiring experienced planners to perform the various planning tasks. This is why; we address in this book the basic planning topics to enable the management of small firms to plan the different aspects of activity. The topics discussed in this book relate to manufacturing activity but we can use them also in the other activities. We designed the book for persons with some knowledge of planning techniques, accounting, and manufacturing operations as well as those with none. Readers of this book need simple knowledge of MS Excel to execute the presented examples.

Since sales is the main function in any business firm, the book addresses the planning of sales first then address the other activity functions that relay on sales function. The book consists of the following five chapters:

Chapter 1 Sales planning addresses the sales planning techniques involving time series techniques and correlation regression analysis. A time series represents a sequence of observations on a variable measured at successive points in time or over successive periods. The objective of time series analysis is to obtain future forecasts based on historical observations. Correlation indicates a relation degree between one dependent variable and one or more independent variables. Regression indicates the quantity of change in the dependent variable when the independent variable changes by one unit. The planners use that relation to predict future values of dependent variable when independent variables change.

Chapter 2 Production planning addresses three topics: aggregate planning, master production schedule, and rough-cut capacity. Aggregate planning is the process used to obtain a preliminary and approximate schedule of the overall production operations of a particular firm. Aggregate production plan schedules product families or types in relatively long time intervals, normally one month. Master production schedule is a planning tool that schedules individual products in shorter time intervals, normally one week. Rough-Cut Capacity calculates a rough estimate of critical resources workload required by the proposed MPS to ensure the existence of sufficient capacity.

Chapter 3 Product components planning addresses four topics: materials requirement planning, capacity requirement planning, shop floor control, and reorder point. Materials requirement planning creates new orders for the components and materials required by end items. Materials requirement planning uses the negativity event of net requirement to trigger creation of new order. Capacity requirement planning is a short-term capacity planning technique that entails evaluating the ability of current resource levels to meet projected and current orders. Shop floor control releases production orders to the factory, monitors and controls the orders progress through the plant, and acquires current information on the status of the orders. Reorder point is a consumption based planning technique, which depends on consumption of stock over time to reach the point of creating new order.

Chapter 4 Operating budgets addresses preparing the operating budgets and calculating variances. Operating budgets include sales, production, direct labor, direct materials, overhead, inventory, and cost of goods sold, administration, and income statement. Operation budgets represent the financial versions of the quantative plans prepared for the functional departments. Calculating variances between actual and standards requires the existence of standard cost system.

Chapter 5 Financial budgets addresses capital budget, cash budget, budgeted balance sheet, and budgeted cash flow. Capital budget is concerned with investing in capital projects to improve the competitive position and increase the profitability of the firm. The cash budget is a short-term plan of expected cash receipts and disbursements during the period. Budgeted balance sheet includes projections of assets and liabilities for future financial period. Budgeted cash flow includes projected cash flow from operations, investments, and finance for future financial period.

The mentioned chapters also include examples and solutions for the previous topics. The book uses these examples to enforce assist in understanding the different subjects.

Fareed Nasr

Chapter 1 Sales Planning

Sales plan is the first implemented function plan before all other activity plans. Sales plan depends on the forecast, which is the projection of expected demand. The sales forecast originates in the demand side of supply chain and the other function plans originate on the supply side. Generating sales plan involves defining of future time horizon. Sales plan is prepared on yearly basis then disaggregated into monthly forecasts.

Sales Forecasting

Planners use number of forecasting techniques to predict future Sales. Planners separate forecasting techniques into two groups: Qualitative forecast Methods and quantitative forecasting methods. Qualitative methods relay on individuals experience while quantitative methods relay on statistical models to obtain forecasts. Qualitative methods are proper when there are new products or when introducing new firms to the market. Quantitative methods are proper for firms and products with historical data.

Qualitative Methods

Qualitative methods depend on personal views of experts and involve two approaches: The first one is to hire one or more experts to predict the future sales of the firm. The second approach is to use Delphi Technique.

Hiring Experts

This approach involves hiring of one or more experts to monitor the market and then estimates the future demand for the firm products. Since the information collected by one person can be limited and may tend to be biased, the company may resort to the employment of a group of experts. The recruitment of a group of experts may increase costs, but leads to consensus on future forecasts for a product or a group of products. This method tends to involve multiple groups of staff in the process of predictability to avoid inclination such as resorting some managers to low sales estimates to avoid future responsibility for achieving the forecasts on the ground. However, bringing different ranks in the firm together may lead to submission of the least ranks to the views of the highest ranks. Therefore, Delphi technique emerged to overcome these problems.

Delphi Technique

The objective of this Delphi technique is to overcome the bias of the least ranks staff to the views of the highest ranks when obtaining consensus estimate of future sales. This method relies on the use of successive surveys then summarizing them to get collective view. The method requires the appointment of a staff member to play the role of administrator who select the explored team, coordinate surveys, and summarize the results. The procedures for performing this method follows:

1. The survey administrator the selects the team who will be explored. The team must have the reasonable experience in the field of survey.

2. The administrator sends surveys to the selected group. The Surveys include the request for the predictions and justifications of those predictions.

3. The Administrator receives the results of the polls then summaries and reports to the survey group without mentioning the names of predictors.

4. The administrator sets up a new survey based on the previous prediction summary then send to the group members attached with the summary.

5. The administrator Continues to repeat the steps 3 and 4 until he obtains consensus predictions.

Delphi technique consumes a lot of time and costs but may lead to good estimates of sales and building of forecasts database. Planners believe that three cycles of survey and summarization represent a compromise between the costs and the good results.

Quantitative Methods

The quantitative methods Adopt statistical techniques to obtain sales forecasts for future time phases. Quantitative methods utilize two types of analysis: time series analysis and correlation and regression analysis. In order to obtain forecasts, the forecasters must use supporting system such as MS Excel and MS SQL Server Data Mining. Forecasters use these supporting systems to receive input, process data, and generate output.

Time Series Analysis

A time series is a sequence of observations on a variable measured at successive points in time or over successive periods. The forecasters may obtain measurements every hour, day, week, month, or year, or at any other regular interval. The pattern followed by the observed data is an important factor in understanding the behavior of time series in the past. When the forecasters expect such behavior to continue in the future, they can use the past pattern to obtain an appropriate forecasting model. The main function of the forecasting model is to predict future values for the series.

Four components influence the Variation between the time series values. Forecasters obtain these components from the observed data. These components involve trend, cyclical, seasonal and irregular components. The general tendency of a time series to increase, decrease or stagnate over a long period is termed as Trend. The cyclical variation in a time series describes the changes caused by circumstances occurs in cycles. The duration of a cycle extends over longer period, usually two or more years. Seasonal variations in a time series are fluctuations over seasons within a year. The important factors causing seasonal variations include climate and weather conditions, customs, traditional habits, etc. Seasonal variations tend to repeat themselves each year.

The forecasters divide Irregular fluctuations into episodic and residual. Episodic fluctuations are unpredictable, but we can identify them. After removing the episodic fluctuations, the remaining is the residual fluctuations. The residual fluctuations are unpredictable and we cannot identify them. Predictors cannot project neither episodic nor residual fluctuations into the future.

To recognize the underlying pattern in a time series data, the first step is to construct a time series plot. A time series plot is a graphical presentation of the relationship between the time series values on the vertical axis and periods on the horizontal axis. We can identify common types of data patterns when examining a time series graph. The time series patterns we address in this context involve trend pattern, seasonal pattern, trend and seasonal pattern, and horizontal pattern.

Trend Pattern

We can identify the trend of a time series by analyzing multiyear fluctuations in historical data. We can detect the trend by inspection of the time series plot. The trend can be upward, downward or stagnate, depending on the slope or angle of the trend-line.

Figure ‎1-1: Trend Pattern

A trend is usually the result of long-term factors such as changing in population, population demographic characteristics, technology, and/or consumer preferences. To illustrate this pattern, we present the figure number (1-1), which display an excel sheet. The presented sheet contains a table of ten years sales figures and the associated graphical plot. The trend line of the plot shows the tendency of value increasing with the passage of time.

Seasonal Pattern

Forecasters recognize Seasonal patterns through observing the repeating behavior over successive periods. Seasonal data tends to increase or decrease during certain periods and recurs every year or every period less than year such as day, week, and month. If the recurring period is longer than year, the pattern is cyclic pattern. Forecasters can detect the seasonal pattern in a time series by comparing quarterly or monthly activity in a number of consecutive years. When the increase or decrease behavior repeats in the same period in each of the compared years, that indicates the existence of this pattern. Seasonal pattern may appear in shorter periods, such as a day. For example, in the passenger transport industry, there are daily peak hours and daily calm hours.

In order to illustrate a sample of this time series pattern, we present figure number (1-2), which contains MS Excel sheet. The Excel sheet contains a time series table, which involve quarterly sales for five years and its associated graph. Inspecting the graph in the