Bayonne Packaging

OPERATIONS MANAGEMENT 2012/2013 1. Introduction to BAYONNE PACKAGING, Inc BAYONNE PACKAGING, Inc is a $43 cardinal printer and paper alterer go with that issues customized paper-based packaging, for industrial customers, for promotional materials softwargon, luxuriousness beverages, gift good and gift sackdy. Presently, the business is leaded by Dave Rand and the add-in of the companionship is constituted by family members, a local banker and outside counsel. This company is implemented in the paper packaging industry that was featured by the quickly growth between 1980? s and early 1990? . Due to organizations that hobby the desire to make a greater squeeze with their promotional materials or affectd their promotional budget from print media and broadcast forms to the package itself at the moment of purchase. From the combination of this growth and Bayonne? s aspiration to improve, the company is dual-lane in six major stages Composition, flat soliding, Printing, Die-Cut , Fold& glue, Finishing and Shipping dock. In the first unrivalled, the goal is to develop the printing and the package design, printing p latterlys argon made and die-cutting dies argon layed.Tbiddy in the Sheet Department, the paper is sheeted from the roll commonplace and stacked on skids to be printed. The fol commencementing hotshot is the Printing department that is where the art give is printed on 4- and 6-colour presses. In the Die-Cut department, the sheets are printed into blanks1. After that, the Fold&Glue department consists in turning the die-cut blanks into the finished product. Here thither are ii options, if it is a large give it goes to the imperial/ tabby cat which is an higher(preno instantuteal) speed machine. If it is a low volume perilion is goes to Staudes machines.After this, a selection is made to decide which products are sent to the 3A machines2. According to the analysis of this company, on that point are three major problems that adjudge a huge invasion on the way the organization functions which are flavour, preservation prison term and constitute of the final product. Taking to account the quality control problems we realized that the master(preno bital) issue was concentrated in Fold & Glue department where thither was either a lack in the glue lines or extravagance of glue. Consequently, 6% of products were defective and to a greater extent than 1% of final product were spurned by he customers, for example boxes popping open beca determination it with not affluent glue or no glue at all. Besides the quality control problems, thither is a signifi discharget inefficiency concerning delivery on duration of the customized packages- they were late more than 20% of the succession. Initially, customers learned not to entirely as marrow squashption in the delivery clock eon of the product. They also realized that some early(a) component of the marketing project might become avail fitting preli momentary than anticipated and this gave a sense of hope that all would be delivered on clipping to some(preno arc minuteuteal) of the parties.In addition, customers often were posited by their own clients to anticipated the delinquent date scheduled by the marketing project, but on the an different(prenominal) hand Bayonne did not fork up the marrow to respond promptly. Therefore, they want to move up or expedite a due date to encounter product sooner than they had originally been promised. 2. electrical condenser utilisation in piss touch ons Machine/Work Center total hours per machine Capacity employment Composition 255 73,50% Jagenburg sheeter 279 80,40% Heidelberg press 348 blow% Bobst die-cut 272 78,39% Int. regal/ faggot F&G 156 44,96% Int.Staude F&G 179 51,59% Int. 3A window/patch 145 41,79% In assign to organize the information provided, we built a capacitance utilization? s table of each force for bear on where we, according to the provided information and by s hrewd ability utilization of each work center, we measure how some(prenominal) the exhibit actually does cook relative to how much it roll in the hay recruit if it were hiening at in force(p) speed (Process Capacity). Capacity Utilization = stream rateCapacity Given the information in Exhibit 2, October of 2011 had 347 scheduled work hours and this authority the utmost add of hours in each work center that an be reached. Considering that the coarctation is the resource with the lowest capacitance and that the flow rate of the resources is identical, the bottleneck is the resource with the highest might utilization. After computing these care fors above, we discontinue that further one work center ensnarls at full capacity which is the both(prenominal) machines of Heidelberg press (bottleneck). Here are the main reasons that could justify why the other work centers didnt reach snow% of capacity utilization * If demand is less than supply, the process allow not devolve at full capacity, but only produce at the rate of demand. If there is meager supply of the input of the process, the process provide not be able to operate that capacity. * If there is one or several processes that only encounter a limited availability (e. g. maintenance and breakdowns) , the process might operate at full capacity dapple it is forcening, but then go into periods of not producing any output while it is not running. This is the case in the Die-Cut work center where output signal is stopped in say to interchange dies. 3. Capacity in pieces per day measurelight for the Die-Cut centerTaking into consideration the present principal, we were asked to find the capacity in pieces per day for the Die-Cut work center, specifically the Bobst Die-Cut. We also cave in to hit that one holy order is ccc00 pieces. a) None of the orders spate be ganged When none of the orders jackpot be ganged, meaning that each clock measure we process an order we will hav e a single frame-up date. In order to sock the number of orders of the process per calendar calendar month (Q), we have to lucifer the correspond term forthcoming per month to the number of orders multiplying by the radical succession to produce one order. sequence open per month = Q x frame-up time + Q x ply timeWe solve the kernel time usable per month taking into account that October 2011 had 347 scheduled work hours net of breaks and that the Die-Cut department has 2 machines. conviction obtainable per month = 347 hrs x 60 min x 2 machines period unattached per month = 41640 min Although the Standard apparatus time is 30 min/sheet, in reality the setup time (time to change dies) is 2/3 hours. So we assumed, an average of 2,5 hours which corresponds to 150 minutes. apparatus time per job = 150 min To compute the run time per order we have to multiply the run time per sheet by the number of sheets that compose the order.Assuming that sheets averaged 3 pi eces, each order has myriad sheets. draw in time/order = lead time/sheet x N? of sheets/order playact clip/order = 0, 0075 min x ten thousand sheets tribulation Time/order = 75 min After calculating the values above, we conclude that total time to produce one order which is divided by setup time and run time per order is 225 minutes. Time to produce one order = frame-up time + contribute Time Time to produce one order = 150 min + 75 min Time to produce one order = 225 min Now, we are in conditions to find Q number of orders per month which is 185,06667. Time forthcoming per month = Q x Setup time + Q x Run timeTime available per month = Q x (Setup Time + Run Time) Time available per month = Q x Time to produce one order Q = Time available per monthTime to produce one order Q = 41 640 min 225 min Q= 185,06667 orders/month Taking into consideration that 1 order equals to 30 000 pieces and consequently, equals to 10 000 sheets we fire convert Q capacity per order per month into capacity per sheets and also per pieces both per month. Value Calculations Capacity/order/ month 185,06667 Made above Capacity/sheets/month 1850666,667 185,06667 x 10 000 sheets Capacity/pieces/month 5552000 185,06667 x 30 000 piecesIt was required to get the capacity per pieces per day. So as we k outright that per day there are two shifts of 7, 5 hours each with a repast break of 30 minutes for every worker, in the end of one day there is 15 hours of work time. Knowing that the time available for month is 347 hours, each month has 23, 1(3) days. Therefore, after the transmutation of months into days, we conclude that capacity per pieces per day equals 240000, meaning the maximum meat the resource, in this case the machines from the Die-Cut department, groundwork produce per unit of time (per day, in this question). b) Pairs of orders can be gangedFacing this late situation, where distichs of orders can be ganged, the setup time must be allocated in a different way. Now, we are going to have one setup for every two orders. The time available per month will be the same, 41640 minutes calculations on the sub question above. Time available per month = 41640 min In order to hunch forward the number of orders ( equals of orders, in this case) per month ( Q ), we have to match the total time available per month to the number of pairs of orders multiplying by the total time to produce one pair of orders. Time available per month = Q x Setup time + Q x Run timeTime available per month = Q x (Setup time + Run time) Time available per month = Q x (Time to produce a pair of orders) The setup time per job (calculations in the sub question above) is 150 minutes, and each time we process 2 orders 150 minutes will be fagged to change dies. Setup Time per job = 150 min To compute the run time per pair of orders we have to multiply the run time per sheet by the number of sheets that compose a pair of orders. As we are assuming that orders averaged 10 000 sheets, we will have that each pair of orders has 20 000 sheets (2 orders x 10 000 sheets).Run time/ pair of order = Run time/sheet x N? of sheets/ pair of order Run Time/ pair of order = 0, 0075 min x 20000 sheets Run Time/order = 150 min So, the total time to produce a pair of orders which is composed of setup time and run time both per pair of orders will be 300 minutes Time to produce one order = Setup time + Run Time Time to produce one order = 150 min + 150 min Time to produce one order = 300 min We are able to compute Q number of orders (pair) per month which equals 138, 8 orders per month Time available per month = Q x Time to produce one orderQ = Time available per monthTime to produce one order Q = 41 640 min 300 min Q= 138, 8 orders/month guardianship in mind that 1 order = 30 000 pieces =10 000 sheets we can convert Q capacity per order per month into capacity per sheets and also per pieces both per month. Value Calculations Capacity/ pair of orders/ month 138,8 Made above Capacity/sheets/month 2776000 138,8 x 20 000 sheets Capacity/pieces/month 8328000 138,8 x 60 000 pieces This last value is the capacity per pieces per month but as we are asked to compute the capacity per pieces per day we must make the conversion.As each day has 15 hours of work time (calculations in the sub question above) and the time available for month is 347 hours, dividing this value by the 15 hours per day, we conclude that each month has 23, 1(3) days. Therefore, after the conversion of months into days we conclude that capacity per pieces per day equals 360000, meaning the maximum gist the resource, in this case the machines from the Die-Cut department, can produce per unit of time, in this question, day. c) on the whole the others can be ganged In the case that all orders are ganged, the total process will include only one set up time.In order to know the number of orders of the process per month (Q), we have to match the total time available per month to the number of orders multiplying by the total time to produce one order. Time available per month = Setup time + Q x Run time The total time available per month remains the same, 41640 minutes. The setup time will be sovereign from the number of orders because there will be a single one for all of them considering that they are all ganged. Time available per month = 41640 min Setup Time per job = 150 minKnowing that one order has one C00 sheets, the run time per order will be 75minutes. Run time/order = Run time/sheet x N? of sheets/order Run Time/order = 0, 0075 min x 10000 sheets Run Time/order = 75 min Regarding all of these values, its now possible to calculate Q number of orders per month. Time available per month = Setup time + Q x Run time 41640 min = 150 min + Q x 75 min Q = 41640 min-150 min75 min = 553, 2 orders/month Keeping in mind that 1 order = 30 000 pieces =10 000 sheets we can convert Q capacity per order per month into capacity per sheets and also per pieces both per month .Value Calculations Capacity/order/ month 553,2 Made above Capacity/sheets/month 5532000 553,2 x 10 000 sheets Capacity/pieces/month 16596000 553,2 x 30 000 pieces As we are asked to compute the capacity per pieces per day we must make the conversion. all(prenominal) day has 15 hours of work time (calculations in the sub question above) and the time available for month is 347 hours, dividing this value by the 15 hours per day, we conclude that each month has 23, 1(3) days. Therefore, after the conversion of months into days we conclude that hen orders can be ganged capacity per pieces per day equals 717406, 3, meaning the maximum amount the resource, in this case the machines from the Die-Cut department, can produce per unit of time, in this question, day. 4. call for that 40 of the orders partialed in October each cause one broken toil run in the royal/ magnate work center, payoffing in two setups for these orders kinda of one a) Capacity in October without these superfluous setups Assuming that 40 of the orders were partialed in October and that each induced one broken production run in Royal/ faggot work center, resulting in two setups for these orders instead of one.First we cipher the capacity without these additional setups. Initially, we worked with the status of the process without these additional setups. We have already calculated the capacity production run, in pieces, in the Royal/Queen work center, an essential value for the computation of the average time per order. So we calculated the ratio between the pieces scheduled and the orders scheduled of Royal/Queen machine, then we multiplied that value by the respective run time. Finally, we did the sum of the previous value with the respective setup time.Pieces plan per Orders Scheduled = Sheets per Pieces scheduledOrders scheduled Pieces Scheduled per Orders Scheduled = 6. 209. 32977 Pieces Scheduled per Orders Scheduled =80640,(63) pieces Average time per order = Standard Setup Time + Pie ces scheduled per Orders Scheduled x Standard Run Time Average time per order = one hundred eighty+80640,(63) x 0. 0023 Average time per order = 365, 4734 min After that, we had to fancy the capacity per order, where we calculate the ratio between total work time scheduled of the three machines in minutes and the average time per order.Total work time scheduled of the three machines in minutes= 347 x 60 x 3=62460min Capacity per order = 62460365,4734 = 170, 9016 orders/ min In the end, we calculated the capacity per piece multiplying the ratio between the pieces scheduled and orders scheduled by the capacity per order. Capacity per piece = 80640, (63) x 170, 9016 = 13781613, 7(68) pieces / min Therefore, we analyzed the capacity production run, in pieces, in the Royal/Queen work center but considering the additional setups. The company with the introduction of these setups the company loses time in the overall process. ) Capacity in fact In order to word form out what happened wi th the introduction of the additional setups, meaning that at this point the company had 40 orders partialed and as we have the information that there are 2 setups per order we consequently know that Bayonne had 80 setups in this work center. On the other hand, if there were no partialed orders, the work center would only have 40 setups. We conclude that when the setups increase the run time available will decrease. We know that there was a reduction of the capacity in this work center, affecting the overall process.Moreover we calculated the time played out in the production of those 80 orders partialed (we assume that they are equally distributed so 40 x 2), which is 21818,939 because we had to take into account the setup time and the run time of the 80 orders partialed. As we can see Time to produce 80 partials = (80 x 180 + 800,0023 x 620932977) Time to produce 80 partials=21818,939 min We also calculated the time available to the company to produce the orders, considering the t otal time available in minutes, the time necessary to produce the 80 orders partialed and the additional orders produced in the available time of the total time per orderAvailable time = 62460 21818,939 Available time = 40641,061 Additional orders produced = 40641,061365,47355 =111,2011 Therefore, we calculated the additional number of pieces Additional number of pieces =111,2011 x 80640,63 = Additional number of pieces = 8967329,736 So the total number of pieces produced in the end of the month was 12192955,191, since we had to consider the sum between the additional pieces produced and the pieces scheduled multiplied by the 40 orders partialed. Total number of pieces = 8967329,736 + (4080640,63) Total number of pieces = 12192955,191 pieces/month 5.Size of orders route to the Royal/Queen work center and to the Staude work center Given the information on exhibit 2, we could calculate de size of orders to the Royal/Queen work center and to the Staude work center. In other words, we considered the setup standard times and the run standard time (the slope of line) of each work center while essential tools to create a graph where it is easier to take very usable conclusions about the size of the batch of these work centers. To have a decease comprehension of the graph, we considered two lines, one blue and one purple that represent, respectively, he Royal/Queen work center and the Staude work center. Royal/Queen Machine Y=0,0023x +180 Staude Machine Y=0. 015x + 40 We know that Royal/Queen machine has a higher setup exist but in the other hand has a lower run cost. Comparing to the Staude machine, it has a higher run cost but a lower setup cost. With this data we can say that the Royal/Queen machine is indicated for big batches and the Staude machine for lower ones. As a result of these calculations, we obtained the intersection of the two lines (break- pull down point), with a value of 11. 23,62, that represents the point where is indifferent to use between t he Royal/Queen machine and the Staude machine in the overall process. So, for batches with a size below than 11023,6 we choose the Staude machine, but if the batch has a size above the break-even point we will then choose the Royal/Queen machine. 6. Yield at each of the work centers Sheet, Print, Die-cut, and Royal/Queen and the additive acquit for an other which the sheets vexs with 40000 sheets Here we took into consideration the definition of submit which is the percentage of units lost of each work center.It was required to compute the yield of the following work centers Sheet, Print, Die-cut, and Royal/Queen. In the tablet below, we organized the data provided and determined the values of the yield of each work center mentioned earlier. We can see that all of the work centers have a very low percentage of units lost because the values of the yield are very goal to 100%. And as we know when the yield is 100% it means that there are no losses at all and the process reaches t he maximum of efficiency possible. Work Center Pieces in Pieces out Yield Sheet 9555097 9488211 99,300%Print 9488211 9326912 98,300% Die-Cut 9326912 9233643 99,000% Royal/Queen 6209329 5588396 90,000% Besides this, we also had to compute the additive yield which is 86,972%. This value was calculated by multiplying the yields of each work center. In order to calculate the cumulative yield for another which the sheets start with 40000 sheets, we had to convert the number of pieces into sheets. If one sheet corresponds to 3 pieces, then 40000 sheets x 3 pieces = 120. 000 pieces The input of the production process is 120000 sheets (100%). However we have found that the cumulative yield is 86,972%.So this tells us that 13,028% of the input is lost during the production process. Phases Calculations Values (in sheets) infix 120000 120000 Losses 120000 x 13,028% -15633,6 Output 120000 x 86,972% 104366,646 7. The data in exhibit 4 (value of actual shipments in October) After evaluating the graphic below that we have reached with the values of the variables Orders shipped, Late and Partialed, we can see that there is a pick out effect of the number of orders Shipped to the Late ones, in other words, the more orders there are the more time is needed to deliver it, making them even more late.We can also extract that the number of orders partialed influences the number of late orders because when an order is divided, it turns into two and this means that there would be another setup to be made instead of just one. These new setups interrupt the process flow, which consequently distinguish capacity. This intensifies depreciation of the machines and consequently increasing the costs. If Bayonne has several partialed orders that means that some quantity of those orders are still being produced, taking up resources that could be used to produce new orders.Because of this, these new orders will start the production process late, making almost impossible for them to be deliv ered at the scheduled time between Bayonne and the customers. 8. Recommendations of short-term and long-term After this report, one of the main goals of the BAYONNE PACKAGING, Inc is to boil down or minimize the problems that the company faces itself. Quality control, delivery time and cost problems are the major obstacles in the progress of the company. So in order to improve the management and the planning of the overall process, Dave Rand and the plug-in of the company have to take some actions in short-term and forte term.So firstly, we suggest that they should have a closer careful oversight in Fold&Glue department, because there are in fact a significant tidy sum of products that are defective and, consequently some of that portion is rejected by the customers. This issue have a huge impact in the image and reputation of the company, so this oversight is imperative and it can translated in more time spend in the inspection of this department. Also, we conceptualize that t he defective units need to be reworked or give-up the ghostd from the process.The company should to support the musical theme of reworking the defectives units, in the way that avoid the waste of the raw materials and the labor spent in the process of that units. In our opinion, Bayonne need to recycle the wastages and reutilizing them for further production. Also, we calculate that the company should be more organized in time schedules and deadlines, because it implicates delays in all the departments and therefore in the overall of the process. Finally, the company need to eliminate the setup time or at least try to reduce the time it takes to perform in the process, for the obvious reason that consequently it steal capacity.In other words, as nothing is produced at a resource during setup, the more frequently a resource is set up, the lower its capacity. So we imagine that the company should be the increasing of the orders in a batch, with the objective that as more units the re are in a batch, the more we can spread out the setup costs. And so to take advantage of the economies of home plate in the entire process. In conclusion, if the company follows our suggestions with the expected results, maintaining everything else constant, they can mortify better results and diminish the difference between what is expectable and what actually occurs.Annex 255hrs ? 100% 347hrs=73,487031% Capacity utilization of composition 279hrs ? 100%347hrs = 80,40345821% Capacity utilization of Jagenburg 348hrs? 100%347hrs=100,2881844% Capacity utilization of Heidelberg press 272hrs ? 100%347hrs=78,386167% Capacity utilization of Bobst Die-Cut 156hrs ? 100%347hrs=44,9567723% Capacity utilization of Int. Royal/Queen 179hrs ? 100%347hrs=51,5850144% Capacity utilization of Staude Machines 145hrs ? 100%347hrs=41,7867435 % Capacity utilization of Int. 3A window