Aggregate Production Planning (App) for Blister Packing Machines System in Pakistan and the Constraints Effecting App

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1 Aggregate Production Planning (App) for Blister Packing Machines System in Pakistan and the Constraints Effecting App M.A Iqbal a, S. Badshah b and N.Rahman c a razahr117@gmail.com, b saeed.badshah@iiu.edu.pk, c noor.rahman@iiu.edu.pk Department of Mechanical Engineering, International Islamic University Islamabad, Pakistan Abstract Since industrial evolution production planning is practicing in various industries of the world. In 1950 aggregate production planning was created which realized that inadequate planning is responsible for the underutilization of production capacity. This paper describes the aggregate planning for blister packing production in an engineering firm located in Hat tar Industrial Estate, Khyber Pakhtunkhwa, Pakistan. The firm manufactures different products for national and international markets. The paper also describes the socio economic constrains which effect the aggregate production plan for the blister packing machine. We adopted different strategies for the aggregate production planning and performed an analysis on each strategy. The strategies were level strategy with inventory and backorders, chase strategy with hiring and firing and hybrid strategy using combination of level and chase strategy for blister packing machine. The aggregate production plan for these strategies were developed using the spread excel sheet format. The result showed that production cost of hybrid strategy using combination of level and chase strategy for blister packing machine is the lowest while maintaining product quality and production time within the specific limits. The constraints of the Aggregate Production Planning and socio economic constraints which effect the aggregate production planning were addressed by developing a mathematical model using the regression analysis. Index Terms Aggregate Production; manufacturing; engineering firm; industrial evolution; socio economic I. INTRODUCTION The Aggregate production planning is basically a medium term planning mainly concerned with the lowest cost for the production planning and fulfilling the varying production demands within the time frame of the planning to manufacture and produce different products in the factory for the customers. The aggregate production planning was introduced in 1950 for minimizing the production cost, controlling inventory level and number of workers while maintaining the product quality. The aggregate production planning is the better utilization of the available resources for the better and economical production of different products. The research studies in the field of aggregate production planning for different sectors concentrates on most common tasks like the lowering of cost, minimizing the fluctuation in workers numbers and the stock/inventory quantity. The industry is challenged by high competition in the market.[1] Now days due to this high competitive environment focus on a few special objectives cannot satisfy the customer demands in the market. That s why the researchers also focus on maintaining the quality. [2] This paper provides the guideline for the development of an optimal aggregate production plan based on excel spreadsheet.[3] II. Industry Selection A discrete manufacturing industry having three sister concerns approximately 250km apart engaged in manufacturing of various products such as Back Hinge (BH),Suspension System(SS),Grinding System(GS),Lift Operating System(LOS),Blister Packing system (BPS) and Vacuum system(vs) is selected for the current study. Our study is limited to aggregate production planning for the Blister Packing Machine Production. III. Input and Output to Aggregate Production Planning 1

2 Aggregate planning has certain inputs which are inevitable. They include 1. Information about the resources and the facilities available. 2. Demand forecast for the period for which the planning has to be done. 3. Cost of various alternatives and resources. This includes cost of holding inventory, ordering cost, and cost of production through various production alternatives like subcontracting, backordering and overtime. 4. Organizational policies regarding the usage of above alternatives. Aggregate planning develops real time production schedules and helps in: 1. Minimizing over and under loading which results in overall reduction in cost of product 2. To meet demands 3. Fully load the facilities 4. Best utilization of the resources 5. Plan for orderly and systematic change of production capacity to meet the peaks and valleys of expected customer demand 6. Inventory control There are two types of decision option available to clarify the aggregate planning problem 1- Those altering demand 2- Those altering supply Demand can be altered and influenced through following techniques 1- Price motivation 2- Back log 3- Complementary products and services 4- Advertisement and promotion campaign Supply can be influenced through following techniques 1- Hiring or firing of employees 2- Using over time and slack time 3- Using part time labor 4- Subcontracting V. General Procedure for Aggregate Planning: 1. Find out the demand for each period. 2. Find out the capability for each period. 3. See the company policies about inventories and man power. 4. Find out the cost of working in regular hours, over time work, inventories, backlog and subcontracting. 5. Develop different plans and after their comparison select optimal plan which is cost effective. VI. Aggregate Planning Techniques Fig.1. Inputs and Outputs to Aggregate Production Planning[4] From the above discussion we can conclude that APP has the following characteristics: 1- A time span of 3-18 months. 2- It gives the aggregate level of product demand which may consist of single or few categories of product. 3- The option of changing both the supply and demand. 4- Management objectives of low inventory, good labor relations and good customer service. 5- Facilities that are considered fixed and cannot be extended. IV. Decision choice There are various techniques which can be used by planners to solve the aggregate planning problem. These include simplistic methods, graphical methods, linear programming techniques, parametric production-planning method. Some of the refined techniques can be categorized as optimization, heuristic and dynamic methods. Inside each one of these categories there are numerous alternative approaches, resulting in an abundance of the procedures of theoretical solution.[5] The objective is typically to find the lowest-cost-plan. Industries mainly focus on long range and shop floor planning rather than aggregate production planning. Organizations do not focus on consumption and utilization of different resources in terms of aggregate plan, such as 1. Utilization of man / machines 2. Consumption of raw material 2

3 3. Subcontracting 4. Inventory This becomes the main cause of under and over utilization of the valuable resources and hence results in the overall loss to the organization in terms of: 1. Manufacturing Value Added (MVA) 2. Cost to Income Ratio 3. Return on assets(roa) 4. Return on equity(roe) 5. Industry, Value Added(IVA) Moreover the already established APP methodologies by different researches are not appropriate for third world countries environment due to socio-economic constrints. VII. Economic Indicators Following economic indicators are used for gauging the industrial performance:- i. Industry, value added (IVA) ii. Manufacturing value added (MVA) iii. Return on Assets (ROA) iv. Return on Equity (ROE) v. Cost to Income ratio Bench mark values of these indicators for Pakistan and South Asia region are attained by the economic survey data provided by World Bank in VIII. APP Parameters Following factors associated with the performance measurement and enhancement of production activities in industry for the implementation of APP is taken into account:- Work force a. Hire and lay off workers b. Overtime / slack time c. Part-time workers Production rate d. Machine resources e. Machine down time f. Available time Inventory level g. Raw material h. Finish goods i. Tools & equipment j. Storing Capacity Backorders k. Returns from Customer l. Recall of product m. Warranty / Guarantee Sub-Contracting n. Quality of supplied product o. Cost p. Time For APP implementation, three strategies are evaluated and the most cost effective is implemented. The firm s performance will be gauged against the international acceptable values of different parameters of performance after APP implementation and if bench mark values are not achieved then the study will be revised to further investigate for achieving target results and factors disturbing optimal plan. Regression analysis will be carried out for development of a mathematical model describing the underutilization of the total production capacity due to socio economic restraints. This model will enable the industrialists to help calculate percentage underutilization of the production resources and devise APP model under light of these restraints. IX. Aggregate Production Planning Strategies Following are three Aggregate Production Planning Strategies: 1. Level Strategy 2. Chase Strategy 3. Hybrid Strategy X. Forecasted demand for Blister packing machine in year 2013 for 7 months Table 4.6: BLISTER PACKING MACHINE PRODUCTION PLANNING IN YEAR 2013 FOR 7 MONTHS Forecaste d Sales for year 2013 for 7 months Current Capacit y In Hand Inventor y Productio n (Year 2013) Sub- Contractin g Cost / Unit Availabl e Work Force / Month Table 4.7: Blister packing machine production and APP associated parameters in year 2013 for 7 months BLISTER PACKING MACHINE PRODUCTION PLANNING IN YEAR 3

4 2013 FOR 7 MONTHS Regular Time Labor Cost / Unit Over Time Labor Cost / Unit Labor Cost Inventory Handling cost Back Order Handling Cost / Unit Hiring Cost of Labor Lay Off Cost of Labor Table 4.8: Forecasted demand for Blister packing machine in year 2013for 7 months. Month JUN JUL AUG SEP OCT NOV DEC Demand Forecast Production XI. Aggregate Production Planning Calculations: Level Strategy: Calculating APP using level strategy with inventory and backorders for Blister Packing system. This strategy will produce at a constant rate, and simply accumulate and deplete inventory throughout the year. A first step is to determine what the monthly production needs to be (in order to produce enough to satisfy the demand for the year), and determine whether our initial work force size is sufficient to produce at this rate. Total demand for 7 months= 2,898 units. Average monthly production needed = 2898 units / 7 months = 414 units per month. Current work force size is 16 workers. This is just enough (because productivity = 26 units per worker per month). Strategy No.1: AGGREGATE PRODUCTION PLANNING (USING LEVEL STRATEGY WITH INVENTORY AND BACKORDERS FOR BLISTER PACKING SYSTEM) Beginning Inventory 41 Beginning Workforce 16 Labor Standard (units/worker) 26 (PKR) Units (PKR) Regular Time Labor Cost 2, ,833,280 Overtime/Subcontracting $0 Inventory Holding Cost 3, ,573,700 Backorders Hiring 20, Layoff, Total Cost (PKR) 13,6,980 Period Demand Demand Net Demand Production/Inventory Planning Production Production Inventory (Excess Units) Backorders (Units Short) Capacity Planning Workers Hired Workers Laid Off Workforce Available Regular Time Capacity (units) Overtime/Subcontr acting (units) Total Production Capacity (units) Total cost of APP for this strategy is PKR 13,6,980 Costs Cost per Unit Total Total Cost Strategy No.2: AGGREGATE PRODUCTION PLANNING 4

5 (CHASE STRATEGY WITH HIRING AND FIRING FOR BLISTER PACKING SYSTEM) Beginning Inventory 41 Beginning Workforce 16 Labor Standard (units/worker) 26 Available Regular Time Capacity (units) Overtime/Subcontr acting (units) Total Production Capacity (units) Total cost of APP for this strategy is PKR 9,361,590. Costs Cost per Unit (PKR) Total Units Total (PKR) Cost Planned Production cost of strategy 2 < Planned Production cost of strategy 1 Regular Time Labor Cost 2, ,833,280 Overtime/Subcontracting $8,310 Inventory Holding Cost 3, Backorders Hiring 20, ,000 Layoff, ,000 Total Cost (PKR) 9,361,590 Period Demand Demand Net Demand Production/Invento ry Planning Production Production Hence it is clear that with strategy 2 (i.e using chase strategy with hiring and firing) the overall production cost is low as compared to strategy 1 (Using level strategy with inventory and backorders). This plan yields virtually no inventory carrying cost since it produces exactly what is needed. Moreover, customer service is high since no backorders are needed. However, from a planning perspective this plan would be difficult to implement due to the high variability of resources needed each period. Moreover, employee morale would tend to be fairly low with such a plan due to lack of job security. Strategy No.3: AGGREGATE PRODUCTION PLANNING (HYBRIDE STRATEGY USING COMBINATION OF LEVEL & CHASE STRATEGIES FOR BLISTER PACKING SYSTEM) Beginning Inventory 41 Beginning Workforce 16 Labor Standard (units/worker) 26 Inventory (Excess Units) Backorders (Units Short) Capacity Planning Workers Hired Workers Laid Off Workforce

6 Firms Performance parameters (%) S # Indicat ors For Pakist an Bench Mark values of South Asia Regio n Firm s A Industrial value added (IVA) Manufacturin g value added (MVA) Retur n on Asset s (ROA) Return on equity (ROE) Cost to Income Ratio Capacity Planning Workers Hired Workers Laid Off Workforce Available Regular Time Capacity (units) Overtime/Subco ntracting (units) Total Production Capacity (units) Total cost of APP for this strategy is PKR 9,204,480 Costs Cost per Unit (PKR) Total Units Total (PKR) Regular Time Labor Cost 2, ,833,280 Overtime/Subcontracting $0 Inventory Holding Cost 3, ,200 Backorders Hiring 20, ,000 Layoff, ,000 Total Cost (PKR) 9,204,480 Period Demand Cost Demand Net Demand Production/Inventor y Planning Production Production Inventory (Excess Units) Backorders (Units Short) Planned Production cost of strategy 3 < Planned Production cost of strategy 1 & 2 Hence it is clear that with strategy 3 (i.e using combination of Level & Chase strategies) the overall production cost is low as compared to strategy 1 & 2. Hence we will adopt strategy # 3 for implementation effective APP in the firm A for production of Blister Packing System. XII. Performance measurement of firm after APP Firms A Performance evaluation after implementation of APP for Blister Packing System. Keeping all the established constraints in place and by adopting strategy # 3, the production of BPS was started. After laps of three months, the performance of the firm with respect to blister packing system was evaluated and the results are presented below. COMPARISON OF FIRMS A PERFORMANCE BEFORE AND AFTER APPLICATION OF APP Fig.2. Comparison of Firms A performance before & after implementation of Aggregate Production Planning Model (Using combination of Level & Chase strategies for Blister Packing System) However in spite of implementation of aggregate production planning target value of performance measures could not be achieved. Hence efforts were made to investigate the causes due to which the desired results could not be attained. 6

7 Average Production Short Fall Percent % under utilization of current capacity Percent International Journal of Science, Engineering and Emerging Technologies Volume 2, November 2014 XIV. Normality Test After measuring different factors stage comes to analyze the same to have a better visibility of the problems creating shortfall in the production of BPS as compared to the in hand capacity. The first step before further analysis is being carried out it is very necessary to see the trend of our data points. In this regard we wish to see if the data follow a normal distribution, so we perform Anderson darling normality test of various attributes. The detail production data of BPS was collected to analyze the main causes of the lack in the firm s performance in comparison with the bench mark values of South Asia Region in spite of effective implementation of APP. The data of the socio economic factors (electricity shortage, gas shortfall and work force absentees due to health problems and war on terror situation) causing lower production than capacity are tabulated in Annexure C. This data is taken from the production being carried out at firm using strategy three. However for the purpose of analysis, hypothetical data of strategy one and two has also been taken in to account. Now the next step is to analyze the data for finalizing the factors causing lower production than current capacity. The software used for the said purpose is Minitab. XIII. Pareto Charts Pareto Charts of the various data variables is being carried out to analyze that which variable is causing more financial loss and percentage delay in shortfall PARETO CHART OF FACTORS EFFECTING PRODUCTION SHORT FALL PROBABILITY PLOT OF UNDER UTILIZATION OF CURRENT CAPACITY Normal Under utilization of Current Produciton Capacity 60 Mean StDev N 14 AD P-Value Fig.4. Normality test of percentage underutilization of current production capacity of BPS. XV. Interpreting the results The graphical output is a plot of normal probabilities versus the data. The plot of the underutilization of the production capacity data shows that the points fall reasonably close to the reference line, indicating that the data follow a normal distribution. XVI. Multivariate Test Multivariate analysis is used to estimate the effects of various causes on the production shortfall. MULTI-VARI CHART FOR %under utilization of current capacity by available workforce/year FACTORS Electricity Break Down U/A WF Gas Short Fall erage Production Short Fall Percent Cum % Fig.3. Pareto Chart of various factors effecting production short fall It s clear from the above given Pareto chart that frequent electricity failure and unavailable workforce contributes 80 percent towards the production shortfall Available Work Force / Year Fig.5.Multivariate analysis of percentage underutilization of current production capacity of bps w.r.t availability of work force A very interesting result is acquired by the analysis of percentage underutilization of current production capacity with respect to available workforce. The underutilization of production capacity is maximum with the minimum work force. This is because the workforce gets under

8 % under utilization of current capacity % under utilization of Current Capacity International Journal of Science, Engineering and Emerging Technologies Volume 2, November 2014 pressure to execute multiple tasks and this hampers the production rate. Moreover with the induction of more workforces a decreasing trend is observed in the underutilization of production capacity up to a workforce level of 202 workers per year. This seems to be the optimal level of workforce for the production of BPS to a capacity of units per year. Beyond this level again an increasing trend of underutilization of production capacity is observed. The reason being resulting in the shifting of responsibilities from one person to another. Fig.7.Multivariate analysis of percentage underutilization of current production capacity of bps with respect to regular labor time cost / year. From above fig., it is concluded that increase in the underutilization production capacity increases the regular labor time cost. Multi-Vari Chart for % under utilization of Current Capacity by Gas Shut Down MULTI-VARI CHART for %UUCC by delay in production due to electricity failure Shortfall of number of units due to electricity failure Fig.6. Multivariate analysis of percentage underutilization of current production capacity of bps w.r.t. electricity failure As clear from above mentioned chart frequent electricity failure has a strong relationship in the increase in the underutilization of current production rate of BPS Delay in Production due to Gas shut down Fig.8. Multivariate analysis of percentage underutilization of current production capacity of bps w.r.t gas shut down rate per year. As clear from above mentioned chart frequent gas shut down has a strong relationship in the increase in the underutilization of current production rate of BPS. XVII. Regression analysis After initial analysis, now we are ready to formulate the mathematical model of our APP having the inbuilt characteristics of addressing all the constraints of APP and socio economic constraints causing the underutilization of production capacity. The same is done by using linear regression analysis. The detail is presented below. For simplicity of analysis and for presenting the model in an easy form, following abbreviations are being used: a. UUCPC: Percentage underutilization of current production capacity b. APP ST: APP Strategy Type c. WF HC: Work Force Hiring Cost / Year d. WF LOC: Work Force Layoff Cost / Year e. RTLC: Regular Time Labor Cost / Year f. IHC: Total Inventory Handling Cost / Year g. BOHC :Total Back Orders Handling Cost / Year 8

9 h. PDEF: Production delay due to Electricity failure / Year i. PDGF: Production delay due to Gas failure / Year j. PDUAWF: Production delay due to U/A Work Force /Year k. PDSEC: Production delay due to socio economic Constraints l. TFL: Total financial loss to the firm due to underutilization of Production. Regression Analysis: Results for Regression Analysis The regression equation is UUCPC = APP ST WF HC RTL BOHC PDEF PDGF PDUAWF PDSEC TFL Predictor Coef SE Coef T P Constant APP ST WF HC WF LC RTLC IHC BOHC PDEF PDGF PDUAWF PDSEC TFL S = R-Sq = 75.1% R-Sq(adj) = 70.1% R-Sq(pred)=72.7% Adjusted R-Sq accounts for the number of predictors in our model and is useful for comparing models with different numbers of predictors, whereas R-Sq is coefficient of determination. It indicates how much variation in the response is explained by the model. The higher the value of R2, the better the model fits your data. The R-Sq value for our model came out to be 75.1%. It means that our established model when used to calculate the percentage underutilization of current production capacity during APP will give an accurate result up to 75.1 percent of the total capacity loss due to socio economic constraints along with the other essential constraints of APP. XVIII. CONCLUSION The aggregate production planning for a small blister packing industry was performed using excel spread sheet analysis. Three strategies were adopted and each one was analyzed. The First strategy was level strategy with inventory and backorders for blister packing system. Aggregate production cost for level strategy is PKR 13,6,980. The second strategy was chase strategy with hiring and firing. This Aggregate production cost for this strategy is PKR 9,361,590 lower than the first level strategy with inventory and backorders. The third strategy was hybrid strategy using combination of level and chase strategies. The aggregate production cost for this strategy is PKR 9,204,480 which is the lowest cost. Hence we recommended the Aggregate production planning for blister packing system using hybrid strategy using combination of both level and strategy because production cost for this strategy is the lowest while maintaining the product quality and time limits. The performance of the blister packing system becomes almost double after implementing the aggregate production planning. The pareto chart analysis showed that the electricity breakdown/failure and workforce unavailability contribute for 80% of the production short fall while gas short fall also contribute to 18% of the production fall. Using aggression model a mathematical model was formulated. The model was used to calculate the underutilization of current production capacity during the aggregate production planning. The result showed that 75.1 % of the total capacity loss due to socio economic constraints along with the other essential constraints of APP. The overall results showed that the blister packing system at Hattar Industrial Estate, Khyber Pakhtunkhwa, Pakistan performance became better after implementing the aggregate 9

10 production planning whoever some constraints like electricity shortfall/failure, unavailability of workforce and gas shortfall effect the production of the blister packing system. REFERENCES: 1. Sillekens, T.D.A., Koberstein, Achim, Suhl, Leena, Aggregate production planning in the automotive industry with special consideration of workforce flexibility. International Journal of Production Research, : p. 1 of Najmeh Madadi, K.Y.W., A deterministic aggregate production planning model considering quality of products. IOP Science, Yenradee, A.T.a.P., Aggregate Production Planning Using Spreadsheet Solver: Model and Case Study. ScienceAsia, : p Aggregate Planning 101 What You Need To Know. [English] [cited th March 2012]; Available from: -planning-101-what-you-need-to-know/. 5. Nowak, M., AN INTERACTIVE PROCEDURE FOR AGGREGATE PRODUCTION PLANNING. Croatian Operational Research Review (CRORR), Muahmmad Ali Iqbal is currently pursuing master s degree program in Engineering Management in IIU,Islamabad, Pakistan BS Mechanical Engineering from UET Taxila Saeed Badshah is currently the chairman and Associate Professor in Mechanical engineering Department in IIU, Islamabad, Pakistan, BS Mechanical Engineering MS Mechanical Engineering PhD Mechanical Engineering from Austria Noor Rahman is currently lab engineer in Mechanical engineering Department in IIU, Islamabad, Pakistan, BS Mechanical Engineering MS Mechanical Engineering 10