Operations Planning & Control (PDF)

Operations Planning & Control Dr. Aditya Kumar Sahu IIM Rohtak Why have you taken this course? Why a Firm exists? v To create VALUE Ø Value for Customers Ø Value for Employees Ø Value for Supplier Ø All other stakeholders Two Major Dimensions of Management: 1. Resp...

Operations Planning & Control Dr. Aditya Kumar Sahu IIM Rohtak Why have you taken this course? Why a Firm exists? v To create VALUE Ø Value for Customers Ø Value for Employees Ø Value for Supplier Ø All other stakeholders Two Major Dimensions of Management: 1. Responds to changes in the external environment and allocates the firm’s scare resources to improve its competitive position. 2. Internal responses to new action programs aimed at enhancing the competitive position of the firm. 4 Factors shaping choice of strategy Tests of best strategy G.E. CEO (Jack Welch) on performance test  Competitive and Supply Chain Strategies Competitive strategy defines the set of customer needs a company seeks to satisfy through its products and services. For example, Walmart aims to provide high availability of a variety of products of reasonable quality at low prices. Most products sold at Walmart are commonplace (everything from home appliances to clothing) and can be purchased elsewhere. What Walmart provides is value-for-money and product availability. Functional Strategies Product development strategy: Ø specifies the portfolio of new products that the company will try to develop Marketing and sales strategy: Ø specifies how the market will be segmented and product positioned, priced, and promoted. Operations and Supply chain strategy Ø determines the nature of material procurement, transportation of materials, manufacture of product or creation of service, distribution of product, follow-up service, whether processes will be in-house or outsourced. All functional strategies must support one another and the competitive strategy 8 n Supply chain strategy includes a specification of the broad structure of the supply chain like “supplier strategy,” “operations strategy,” and “logistics strategy.” 9 Operations Planning & Control zone? The Value Chain in a Company Implied Uncertainty (Demand and Supply) Spectrum Chapter 2 Supply Chain Performance: Achieving Strategic Fit an Predictable Predictable supply and uncertain Highly uncertain supply and demand, or uncertain supply and supply and demand predictable demand, or somewhat demand uncertain supply and demand Salt at a An existing A new supermarket automobile communication model device FIGURE 2-2 The Implied Uncertainty (Demand and Supply) Spectrum Supply uncertainty is also strongly affected by the life-cycle position of the product. New products being introduced have higher supply uncertainty because designs and production processes are still evolving. In contrast, mature products have less supply uncertainty. 11 Need for Operations Planning & Control Customer demand from different segments varies along several attributes 1. Quantity of the product needed in each lot 2. Response time that customers are willing to tolerate 3. Variety of products needed 4. Service level required 5. Price of the product 6. Desired rate of innovation in the product 12  Customer is an integral part of the supply chain. Includes movement of products from suppliers to manufacturers to distributors and information, funds, and products in both directions. Typical supply chain stages: customers, retailers, wholesalers, distributors, manufacturers, suppliers. May be more accurate to use the term “supply network” or “supply web”. n Supply Web 13 n inValue Chain VS. Supply Chain 14 The Objective of a Supply Chain n Maximize net value generated. n Supply Chain Surplus = Customer Value - Supply Chain Cost n Successful supply chains manage flows of product, information, and funds to provide a high level of product availability to the customer while keeping costs low.  Supply Chain Profitability n Example: a customer purchases a wireless router from a retail store for Rs. 1500 (revenue) n Supply chain incurs costs (produce components, storage, transportation, transfer funds, convey information, etc.) n Difference between Rs. 1500 and the sum of all of these costs is the supply chain profitability n Supply chain profitability is total profit to be shared across all stages of the supply chain. n Success should be measured by total supply chain surplus, not profits at an individual stage 16 Why you should study this course?  Make in India Transforming India into a Global Manufacturing Powerhouse Vision: Make in India & Sell Anywhere  Launched on September 25, 2014, by Prime Minister, the "Make in India" initiative is completing 10 years as a pivotal step in India's nation-building efforts. Make in India has been unsuccessful at achieving its stated targets. Under this programme, the share of manufacturing in GDP was projected to reach 25% by 2022. However, the GDP share of manufacturing has actually fallen from 16.7% in 2013-2014 to 15.9% in 2023-2024  “Make in India 2.0" phase encompassing 27 sectors, the program continues to drive forward with significant achievements and renewed vigour, reinforcing India's position as a major player in the global manufacturing landscape. Main Objectives of Make in India 1. Increase the manufacturing sector's growth rate to 12–14% per year. 2. Create 100 million additional industrial jobs by 2025. 3. Increase the manufacturing sector's contribution to GDP to 25% by 2025 (from 14%-15%). 4. Create a business-friendly environment to attract Foreign Direct investment. 5. Promote sustainable manufacturing practices (Zero Defect Zero Effect)  Pillars of ‘Make in India’ New Processes: The "Make in India" initiative identified 'ease of doing business' as a crucial factor for promoting entrepreneurship. Several measures were implemented to enhance the business environment, making it more conducive for startups and established enterprises alike. Tax Reforms: Enabling a Business-Friendly Environment Unified Market through Goods and Services Tax (GST) Reduced Production Costs The implementation of GST on July GST rate on over 200 products was 1, 2017, unified India’s 36 states reduced from 28% to 18%, and union territories into a single enhancing overall efficiency and common market. productivity. 2. New Infrastructure: The government focused on developing industrial corridors and smart cities, integrating state-of-the-art technology and high-speed communication to create world-class infrastructure. Innovation and research were supported through streamlined registration systems and improved intellectual property rights (IPR) infrastructure. Efforts were made to identify industry skill requirements and develop the workforce accordingly. PM GatiShakti: Building a Seamless Network 1 Vision Launched in 2021, PM GatiShakti aims to achieve Aatmanirbhar Bharat and a US $5 trillion economy by 2025. 2 Multi-Modal Connectivity The program focuses on creating multimodal and last-mile connectivity infrastructure by promoting holistic planning and coordination across 36 Ministries/Departments. 3 Economic Growth Engine Driven by 7 key engines: Railways, Roads, Ports, Waterways, Airports, Mass Transport, and Logistics Infrastructure. 3. New Sectors: Foreign Direct Investment (FDI) was significantly opened up in various sectors including Defence Production, Insurance, Medical Devices, Construction, and Railway infrastructure. This expansion also included easing FDI regulations in Insurance and Medical Devices, encouraging international investment and growth. 4. New Mindset: The government embraced a role as a facilitator rather than a regulator, partnering with industry to drive the country’s economic development. This shift aimed to foster a collaborative environment that supported industrial growth and innovation. Production linked Incentive (PLI) Schemes Aligned with India’s vision of becoming 'Atmanirbhar' (self-reliant), the Production Linked Incentive (PLI) Schemes were introduced to enhance the country’s manufacturing capabilities and boost exports. With an impressive outlay of ₹1.97 lakh crore (over US$26 billion), these schemes cover 14 key sectors aimed at fostering investment in cutting-edge technology and promoting global competitiveness. Goal of PLI Scheme The 14 sectors covered under the PLI Scheme include: Semiconductor Ecosystem Development Program Objectives Significant Investment Landmark Projects The Semicon India programme aims Approved in 2021, the program The first major project with Micron to foster the development of a involves a financial outlay of INR was sanctioned for nearly Rs 22,000 sustainable semiconductor and 76,000 crore. crores. Tata's joint venture with display ecosystem in India. Taiwan’s Powerchip in Dholera is another promising development. Major Achievements under Make in India  Global Recognition of Indian Products Bicycles Kashmir Willow Bats Indian bicycles are exported Kashmir willow bats are a to the UK, Germany, and the global favorite, highlighting Netherlands, signifying India's craftsmanship and international recognition of influence in international Indian engineering and cricket. design. Boots Dairy Products 'Made in Bihar' boots are Amul has expanded its now part of the Russian presence by launching its Army’s equipment. dairy products in the US. Electronics Sector Transformation 155 99% Billion USD Domestic Production of smartphones India's electronics sector in FY23 India is the second-largest mobile manufacturer in the world Value Addition Smartphone PLI Scheme Generates Revenue 19 Times The Incentives Disbursed  80 Vande Bharat Trains, India’s first indigenous semi-high-speed trains, are a shining example of the success of the 'Make in India' initiative. India is achieving remarkable milestones in defence production, exemplified by the launch of INS Vikrant, the country's first domestically made aircraft carrier. India produces an impressive 400 million toys annually, with 10 new toys being created every second. India recorded merchandise exports worth $437.06 billion in FY 2023- 24, reflecting the country's growing role in global trade. Companies benefitted from PLI Scheme What is Production Planning and Control Production ? Planning ? Control ? Production: Production activity constitutes the transformation of raw materials in to a desirable output (Products). Example: products could be Biscuits, chair etc. Planning: What to Produce - Product planning and Product design Where to Produce - Facilities and Capacity Planning When to Produce - Production scheduling and Machine scheduling Who will Produce - Manpower Planning How to Produce - Material, Process and Tools and equipments planning How much to Produce - Quantity planning Source: Martand T. Telsang Levels of PPC Strategic Planning (Long Range): It is the process of thinking through the organizations current mission and environment and setting a guide for future decisions and results. It is done by top level management. e.g. Technology forecasting and choice of appropriate technology for the long range time horizon. Procurement or development of new technology Levels of PPC.. Tactical Planning (Intermediate Range): It is done over an intermediate term or medium range time horizon (1 year or 2 year basis) It is done by middle level management. E.g. Aggregate production planning for most optimal utilization of resources Levels of PPC.. Operational Planning (Short Range): It is done over a short range time span, day to day activities, It is done by low level management. E.g. concerned with utilization of existing facilities rather than creation of new facilities. Control: Control includes functions such as dispatching programming, inspection, expediting and evaluation. Control keeps track of the activities and sees whether everything is going as per schedule or not. Production control will be in action when production activity begins. Control is concerned with communicating information and producing reports like output reports, productivity, rejection rate, etc. Control involves in taking corrective steps in case of error to match actual performance against the planned performance. Production Control Inspite of planning to the minute details, yet always (most of the time) it is not possible to achieve production 100% as per the plan. There may be innumerable factors which affect the production system and because of which there is a deviation from the actual plan.  Some of the factors that affect are - 1.Non availability of materials (due to shortage etc.) 2.Plant, equipment and machine breakdown. 3.Changes in demand and rush orders. 4.Absenteeism of workers. 5.Lack of co-ordination and communication between various functional areas of business.  Production control cannot be same across all the organization. Production control is dependent upon the following factors: ØNature of production (job oriented, service oriented, etc.) Ø Nature of operation ØSize of operation Objectives of Production Planning and Control Typical organisation structure for PPC in a firm Centralised and Decentralised PPC Centralised planning is more effective in case of multi-product, multi-plant organisations and it takes away the burden of planning from line function to allow them to concentrate on manufacturing. In centralised planning, a staff specialist controls production planning functions. The decentralised planning involves the line staff in planning the production and this is going to take away the majority of their time in performing functions. Production procedure Hierarchy of Production Decisions Forecast of Future Demand Long range future planning Aggregate Planning Master Production Schedule Schedule of production quantities by product and time period Material Requirement planning System Explode to master schedule to obtain requirement for components and final product Detailed Job shop Schedule TO meet specification of production quantities from MRP System Operations Planning & Control Dr. Aditya Kumar Sahu Demand Forecasting Learning Objectives 1. Understand the role of forecasting in an organization 2. Identify the components of a demand forecasting. 3. What are the steps involved in designing forecasting system? 4. What are the various types of forecasting models? 5. Time- series forecasting methods 6. How to access the accuracy of forecasting method? 72  Forecasting n Forecasting refers to predicting what will happen in the future by gathering and analyzing past and current data. n In the simplest terms, forecasting is the attempt to predict future outcomes based on past events and management insight.  Role of Forecasting n It is a decision-making tool used by many businesses to help in: ü Accounting: New product/process cost estimates, profit projections, cash management. ü Finance: Equipment/equipment replacement needs, timing and amount of funding/borrowing needs. ü Human resources: Hiring activities, including recruitment, interviewing, and training; layoff planning, including outplacement counseling. ü Marketing: Pricing and promotion, e-business strategies, global competition strategies. ü MIS: New/revised information systems, internet services. ü Operations: Schedules, capacity planning, work assignments and workloads, inventory planning, make-or-buy decisions, outsourcing, project management. ü Product/service design: Revision of current features, design of new products or services. Characteristics of Forecasts 1. Forecasts are always inaccurate and should thus include both the Systematic component and Random component 2. Long-term forecasts are usually less accurate than short-term forecasts 3. With increase in the number of stages in supply chain the forecast inaccuracy increases  Patterns in data n Trend ? n Cycle ? n Seasonality ? 77 Components of Demand  Forecast number of mobile users worldwide from 2020 to 2025 Source: Worldwide; The Radicati Group; 2020 to 2021 (Statista) 79 Nestle Sales (June 10-March 15) Number of passengers travelled in US airlines The Walt Disney World forecasting department has 20 employees who formulate forecasts on volume and revenue for the theme parks, water parks, resort hotels, as well as merchandise, food, and beverage revenue by location. 82  Components of Demand 1. Average demand for a period of time 2. Trend 3. Seasonal element 4. Cyclical elements 5. Random variation 84 How to predict future: Forecasting Approaches — Qualitative forecasting — Qualitative techniques permit the inclusion of soft information such as: — Human factors — Personal opinions — Hunches — These factors are difficult, or impossible, to quantify — Quantitative forecasting — These techniques rely on hard data — Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast 85  Forecasting Methods 1. Qualitative n Executive Opinions n Delphi Method n Sales force polling n Customer surveys 2. Quantitative: Time Series (time component of Past Demand) n Naïve Method n Moving Average n Weighted moving average n Exponential smoothing 3. Quantitative: Causal/ Associative ¨ use equations that consist of one or more explanatory variables that can be used to predict demand (Regression) Time Series Method Time-Series Components  Moving Average Model Ft = The moving average forecast for period t n = The number of periods for moving average Di = Actual demand during period i If different periods do not have different weights, the forecast obtained will be based on a simple moving average model, given by: Dt -1 + Dt - 2 + Dt -3 +... + Dt - n Ft = n  Weighted Moving Average Model n Wi = Weight for the ith period demand data n The generalized formula for forecasting using MA method is given by: Dt -1Wt -1 + Dt - 2Wt - 2 + Dt -3Wt -3 +... + Dt - nWt - n Ft = Wt -1 + Wt - 2 + Wt -3 +... + Wt - n 90 Caselet 1 91 92  Linear Trend Regression LEAST SQUARE METHOD OF FORECASTING (Regression Analysis) 93 n If a time series exhibits a linear trend, the method of least squares may be used to determine a trend line (projection) for future forecasts. n Least squares, also used in regression analysis, determines the unique trend line forecast which minimizes the mean square error between the trend line forecasts and the actual observed values for the time series. n The independent variable is the time period and the dependent variable is the actual observed value in the time series. 94  Simple Linear Regression Equation Positive Linear Relationship E(y) Regression line Intercept Slope b1 b0 is positive x 95  Simple Linear Regression Equation Negative Linear Relationship E(y) Intercept b0 Regression line Slope b1 is negative x 96  Simple Linear Regression Equation No Relationship E(y) Intercept Regression line b0 Slope b1 is 0 x 97  Regression Plot Y E[Y]=b0 + b1 X Yi { Error: ei } b1 = Slope } 1 b0 = Intercept X Xi 98  Least Squares Method n Slope for the Estimated Regression Equation ∑ 𝑥" − 𝑥̅ 𝑦" − 𝑦! 𝑏! = ∑ 𝑥" − 𝑥̅ # where: xi = value of independent variable for i th observation yi = value of dependent variable for i th observation 𝑥̅ = mean value for independent variable 𝑦! = mean value for dependent variable 99  Least Squares Method y-Intercept for the Estimated Regression Equation 𝑏! = 𝑦# − 𝑏" 𝑥̅ 100  Simple Linear Regression Reed Auto periodically has a special week-long sale. As part of the advertising campaign Reed runs one or more television commercials during the weekend preceding the sale. Data from a sample of 5 previous sales are shown below. Determine the no. of cars sold when 5 TV Ads are shown. Number of Number of TV Ads (x) Cars Sold (y) 1 14 3 24 2 18 1 17 3 27 101 Number of Number of TV Ads (x) Cars Sold (y) 1 14 3 24 2 18 1 17 3 27 Sx = 10 Sy = 100 𝑥̅ = 2 𝑦! = 20 102 17  Estimated Regression Equation Slope for the Estimated Regression Equation ∑ 𝑥" − 𝑥̅ 𝑦" − 𝑦! 20 𝑏! = # = =5 ∑ 𝑥" − 𝑥̅ 4 y-Intercept for the Estimated Regression Equation 𝑏$ = 𝑦! − 𝑏!𝑥̅ = 20 − 5 2 = 10 Estimated Regression Equation 𝑦/ = 10 + 5𝑥 No of Cars sold when 5 TV Ads are shown: 𝑦/ = 10 + 5 ∗ 5 = 35 𝐶𝑎𝑟𝑠 104  Using Excel’s Chart Tools for Scatter Diagram & Estimated Regression Equation Reed Auto Sales Estimated Regression Line 30 25 20 Cars Sold y = 5x + 10 15 10 5 0 0 1 2 3 4 TV Ads 105 Predicted Y 10  Exponential Smoothening Method Weights of past data exponentially decreases. Forecast is calculated on the basis of forecast of current period and actual demand of current period. Lower Value of alpha means forecast is not responsive to the demand Higher value of alpha means forecast is responsive to demand Ft+1 = Exponentially smoothened forecast for period t+1 Ft = Exponentially smoothened forecast for period t Dt = Actual demand during period t a = Smoothening coefficient Ft +1 = Ft + a ( Dt - Ft ) A lower value of a indicates that the forecast is not responsive to the demand. E.g. try putting a = 0 Caselet 2 n a = 0.5 Week Demand Forecast 1 820 820 2 775 820 3 680 811 4 655 785 5 750 759 6 802 757 7 798 766 8 689 772 9 775 756 10 ? 108 n F10= 765.5 109  Exponential Smoothening Method Caselet 3 An example with a = 0.20 An example with a = 0.80 Model Parameter Model Parameter Smoothening Constant (a) 0.20 Smoothening Constant (a) 0.80 Period Forecast Actual Demand Period Forecast Actual Demand January 100 90 January 100 90 February 98 95 February 92 95 March 97 105 March 94 105 April 99 110 April 103 110 May 101 100 May 109 100 June 101 130 June 102 130 July 107 90 July 124 90 August 103 110 August 97 110 September 105 100 September 107 100 October 104 140 October 101 140 November 111 November 132 A lower value of a indicates that the forecast is not responsive to the demand  An example with a = 0.20 An example with a = 0.80 Model Parameter Model Parameter Smoothening Constant (a) 0.20 Smoothening Constant (a) 0.80 Period Forecast Actual Demand Period Forecast Actual Demand January 100 90 January 100 90 February 98 95 February 92 95 March 97 105 March 94 105 April 99 110 April 103 110 May 101 100 May 109 100 June 101 130 June 102 130 July 107 90 July 124 90 August 103 110 August 97 110 September 105 100 September 107 100 October 104 140 October 101 140 November 111 November 132 A lower value of a indicates that the forecast is not responsive to the demand Exponential Smoothing with Trend Adjustment n Holt’s Linear Exponential Smoothing or Double Exponential Smoothing 112 n Step 1: Ft = a (Actual demand last period) + (1 - a)(Forecast last period + Trend estimate last period) Ft = a(At-1) + (1 - a)(Ft-1 + Tt-1) n Step 2: Tt = β(Forecast this period - Forecast last period) + (1 - β)(Trend estimate last period) Tt = β(Ft - Ft-1) + (1 - β)Tt-1 n Step 3: FITt = Ft + Tt 113 Exponential Smoothing with Trend Adjustment Step 1: Compute Ft Step 2: Compute Tt Step 3: Calculate the forecast FITt = Ft + Tt Caselet Exponential Smoothing with Trend Adjustment A large Portland manufacturer wants to forecast demand for a piece of pollution-control equipment. A review of past sales, as shown below, indicates that an increasing trend is present: MONTH ACTUAL MONTH (t) ACTUAL DEMAND (At) (t) DEMAND (At) 1 12 6 21 2 17 7 31 3 20 8 28 4 19 9 36 5 24 10 ? α =.2 β =.4 The firm assumes the initial forecast average for month 1 (F1) was 11 units and the trend over that period (T1) was 2 units. n Simple exponential smoothing is often referred to as first-order smoothing, and trend- adjusted smoothing is called second-order smoothing or double smoothing. n Other advanced exponential-smoothing models are also used, including seasonal-adjusted and triple smoothing. 116  Seasonal Pattern n Seasonal patterns are recognized by seeing the same repeating pattern of highs and lows over successive periods of time within a year. n A seasonal pattern might occur within a day, week, month, quarter, year, or some other interval no greater than a year. n A seasonal pattern does not necessarily refer to the four seasons of the year (spring, summer, fall, and winter). 117 Steps in the process for monthly seasons: 1. Find the average historical demand each season (or month in this case) by summing the demand for that month in each year and dividing by the number of years of data avail- able. Ø For example, if, in January, we have seen sales of 8, 6, and 10 over the past 3 years, average January demand equals (8 + 6 + 10)/3 = 8 units. 2. Compute the average demand over all months (i.e. by dividing the total average annual demand by the number of seasons). Ø For example, if the total average demand for a year is 120 units and there are 12 seasons (each month), the average monthly demand is 120/12 = 10 units. 118 3. Compute a seasonal index for each month (i.e. by dividing that month’s historical average demand (from Step 1) by the average demand over all months (from Step 2)). Ø For example, if the average historical January demand over the past 3 years is 8 units and the average demand over all months is 10 units, the seasonal index for January is 8/10 =.80. 4. Estimate next year’s total demand. 5. Divide this estimate of total demand by the number of months, then multiply it by the seasonal index for that month. 119 Caselet: Seasonal Index n A Des Moines distributor of Sony laptop computers wants to develop monthly indices for sales. Data from the past 3 years, by month, are available on the next slide. n It is expected that the annual demand for computers will be 1,200 units next year, use the seasonal indices to forecast the monthly demand for each month of next year. 120 121 Seasonal Index Seasonal Index Example Seasonal forecast for Year 4  Multiple-Regression Analysis If more than one independent variable is to be used in the model, linear regression can be extended to multiple regression to accommodate several independent variables ŷ = a + b1x1 + b 2 x 2 Computationally, this is quite complex and generally done on the computer  Selecting a Forecasting Method n The underlying pattern in the time series is an important factor in selecting a forecasting method. n Thus, a time series plot should be one of the first things developed when trying to determine what forecasting method to use. n If we see a horizontal pattern, then we need to select a method appropriate for this type of pattern. n If we observe a trend in the data, then we need to use a method that has the capability to handle trend effectively. 128 129 n Three ways to calculate the systematic component: n Multiplicative S = level × trend × seasonal factor n Additive S = level + trend + seasonal factor n Mixed S = (level + trend) × seasonal factor 130 Initial Seasonality, Level and trend 131 132  IMPACT OF BAD FORECASTING! n Accurate forecasts are very important for the supply chain. n Inaccurate forecasts can lead to shortages and excesses throughout the supply chain. n Shortages of materials, parts, and services can lead to missed deliveries, work disruption, and poor customer service. 133  Accuracy of Forecasts n Forecast Error (FE): e t = Dt - Ft n n Sum of Forecast Errors (SFE): åe i =1 i 1 n n Mean Absolute Deviation (MAD): MAD = * å e i n i =1 MAD = å Actual t - Forecast t MAD weights all errors n evenly  å (Actual t - Forecast t ) 2 MSE weights errors according MSE = n -1 to their squared values Actualt - Forecast t å Actualt ´100 MAPE weights errors MAPE = according to relative error n 135  Forecast Error Calculation Caselet 4 136  Forecast Error Calculation Actual Forecast (A-F) Period (A) (F) Error |Error| Error2 [|Error|/Actual]x100 1 107 110 -3 3 9 2.80% 2 125 121 4 4 16 3.20% 3 115 112 3 3 9 2.61% 4 118 120 -2 2 4 1.69% 5 108 109 -1 1 1 0.93% Sum 13 39 11.23% n=5 n-1 = 4 n=5 MAD MSE MAPE = 2.6 = 9.75 = 2.25% 137 Summary of Learning Objectives n Role of forecasting in an organization n Components of a demand forecasting. n Time- series forecasting methods n Accuracy of forecasting method Thank You