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Logistics And Operations Management Marketing Essay

Level 1 begins with matching objectives, strategy and implementation. From the previous section it is clear that our objectives was to provide 100 per cent service level to customer with a lean approach on raw materials and keeping a stable amount of finished goods. When it comes to implementation of strategy there is one key important factor that we did not consider which is the most important part to devise a valid strategy, which is financial constraints such as minimum expected demand and unused capacity. Because we did not consider these factors in level 1 eventually we made a mistake when an order from OEM emerge in week 15 where our level of inventory is not enough and we did not produce any goods that leads to penalty of lost sales and a high number of fixed cost for the reason that we do not produce anything. We failed to concur with our own objectives in the early weeks. In the operational side, in week 13 we decided not to order any material which affected week 15 where we do not have any accessories to produce finish goods. For the reason that we thought the inventory is enough for the early weeks and will satisfy the demand according to our weekly forecast; as it turns out in the following weeks, it is harder to plan anything from weekly forecast. From this mistake we can see it is easier to forecast demand monthly, not because it is easier to accurately match the number of forecast and demand rather we can plan how much material we need and how much finished goods are we planning to store for inventory. Because we have a forecast for 1 month we know the average number of goods we are going to sell in every week, therefore it will make planning so much easier rather than seeing weekly demand where it resulted in reactive management and although it can guide us through minimizing losses nevertheless it is harder to create a master production schedule to reach a higher profit.

Level 2

By level 2 we were starting to get how it is supposed to be. We have succeeded to do the forecast in a monthly basis, and then we start calculating the financial aspect of how much we produce and order for this level but we have not yet implied the minimum demand. Consequently there is an impact felt here from the implication of unused capacity, that is our financial condition does not support our plan to have a steady production and inventory, therefore what we do in this second level is mostly reacting what has happened, adjusting calculation including updating forecast then decide on how many to produce and order. This approach still does not go well because it is time consuming where every week has to be decided and the result does not show a good flow of material and cash, because we are missing demand and penalty. The final decision was to order more material for the final level so that we do not replicate the mistakes that happen in the first level. To sum up, in this level we have devise a plan but due to the mistakes that made unnecessary cost we have to adapt to how much money that we have left, produce the maximum number that we can produce and preparing material for level 3.

Level 3

In the final level we have managed to get our plan going apart from the mistakes that happen in the first level. First thing that we do is master production schedule. This tool helps us greatly, mostly because we will see every detail calculation in every week such as forecast, minimum demand, production, material ordering, and inventory. With this tool we manage to estimate how much are we going to borrow from the bank if we went bankrupt from minimum revenue, and because we are allowed to borrow from the bank, we can produce to a number where it match our monthly forecast. The result is in level 3 we did not experience any penalties regarding lost sales and the amount of money that we need to produce have been authorized by the bank because it has a basis which is from the master schedule plan. Our biggest mistake is sticking excessively much to the master schedule plan, whereas supposedly we also should update it weekly, but there are fewer decisions to be made however data are visible to all group members. The mistake was we ended up with an excessive amount of inventory for accessories, we do have a strategy of having stable production, by having MPS we can maintain a stable production with a stable inventory and yet we have an excessive amount of accessories. This is due to accessories is the cheapest of all raw material, therefore has the cheapest holding cost so in trade-off between inventory cost and risk of a penalty, in that moment we decided not to reduce our material ordering plan. Looking back there should be adjustment to the plan about how many accessories should we hold, because the amount we ended up with is very big.

Conclusion

In Figure 1 below is the simplified version of what is the biggest mistake that our group has made in the entire exercise. It seems as a domino effect which starts on doing weekly forecast. In this case a weekly forecast eventually did not suit us, because we cannot see the total demand in each month to quantify how much do we need to produce and order. Therefore what we should have done is to arrange the previous year and this year data cumulatively thus we can forecast for a month and that will be the based for our stable production and material ordering plan.

Figure

Weekly forecast leads us to a reactive response where we starting to discuss about how much to produce after we see the actual demand, as an example in the early weeks we have relatively low demand, so in week 15 decided to not produce anything. Unexpectedly the demand shoots up leaving us with a penalty of £ 34.860 of lost sales added by the actual loss of sales for 6.400 units and worsened by unused capacity which gives us a total loss of £ 70.000. In the following week we produced many goods with no buyers. This would not happen if we have a target production based on monthly forecast, because even though it cannot to predict when the peak is going to be, it can guide us to produce a number of goods so that we do not miss the peak when it comes. In order to satisfy demand when the peak comes, we need order material in advance, when we failed to plan even though we know the next week is going to be the peak demand, consequently we cannot do anything other than minimize lost sales.

The reason behind weekly forecast was the first trigger is because this is the basis from which to build a master production schedule and material requirement planning that can have a stable production and ordering. Without a target production there will be too many discussions every time we want to produce and order even after there is a decision, often we do not have the capability to satisfy demand. Consequently if this is applied correctly it is easier to reach the objective of having 100 per cent service level and gradually leading to lean inventory.

Master Production Schedule

 

Week

25

26

27

28

Monthly

29

30

31

32

Monthly

33

34

35

36

Forecast STD

6375

6375

6375

6375

25500

4462.5

4462.5

4462.5

4462.5

17850

7050.75

7050.75

7050.75

7050.75

XL

3125

3125

3125

3125

12500

2187.5

2187.5

2187.5

2187.5

8750

3456.25

3456.25

3456.25

3456.25

Min. Demand STD

2000

2000

2000

2000

 

1300

1300

1300

1300

 

2500

2500

2500

XL

1000

1000

1000

1000

 

1000

1000

1000

1000

 

1100

1100

1100

Stock

 

 

 

 

 

 

 

 

 

 

 

 

 

Std

662

7662

11062

14462

 

17862

21062

24262

27462

 

30662

33562

36462

39362

XL

6261

5261

4261

3261

 

5861

5761

5661

5561

 

5461

6161

6861

7561

Production

 

 

 

 

 

 

 

 

 

 

 

 

 

Day STD

6000

6000

6000

6000

 

5000

5000

5000

5000

 

6000

6000

6000

XL

0

0

0

0

 

1000

1000

1000

1000

 

0

0

0

Night STD

4000

0

0

0

 

0

0

0

0

 

0

0

0

XL

0

0

0

4000

 

0

0

0

0

 

2000

2000

2000

Eve STD

0

0

0

0

 

0

0

0

0

 

0

0

0

XL

0

0

0

0

 

0

0

0

 

 

0

0

0

Sat STD

0

0

0

0

 

0

0

0

0

 

0

0

0

XL

0

0

0

0

 

0

0

0

0

 

0

0

0

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Material

 

 

 

 

 

 

 

 

 

 

 

 

 

Body

14689

11689

12289

12889

 

9889

10489

11089

11689

 

10489

9289

9289

Aerial

14689

11689

12289

12889

 

9889

10489

11089

11689

 

10489

9289

9289

Acc

44688

59788

54388

48988

 

36388

34088

29788

25488

 

20088

16688

14688

Acc ordered before will arrive 26

24100

 

 

 

 

 

 

 

 

 

 

 

Order

 

 

 

 

 

 

 

 

 

 

 

 

 

Body

6000

6000

6000

6000

 

6000

6000

6000

6000

 

6000

6000

 

Aerial

6000

6000

6000

6000

 

6000

6000

6000

6000

 

6000

6000

 

Acc

0

0

0

4000

 

2000

2000

0

0

 

2000

2000

 

After examining table above there are three main key points that are essential:

Forecast

First key that has to be calculated is forecast demand. In the table above notice there is only monthly forecast demand and then divided equally in each week. There will definitely be a peak demand however as mentioned in the section before it is unpredictable, all we can do is preparing. Therefore monthly forecast is a target production, we must reach that number. Because most peak time is at week three of every month, we will build stock up to that point. Usually in first and second week of every month the demand will relatively small, therefore it is the time to produce to stock. In week 29 until 32 the forecast conducted from last year demand reveals demand will decrease slightly before rising again in week 33 until 36.

Production

Production has to be kept at a steady level and at maximum capacity; anything less than maximum capacity may result in extra costs. In week 25 the production concentrates on standard products to build stock. The reason we did not produce too much XL is because XL will be approximately 50 per cent of demand from standard, so we decided to store around 5000 XL or at maximum 50 per cent of standard available in inventory. In week 29 productions lowered to suit the expected condition that demand will decrease however XL will be replenished every week together with standard in normal shift. We want to keep each of standards and XL stable, will not produce much just enough to go through this week. In week 33 after having built stock from previous week and expecting a rise in demand continue to produce at approximately the same rate as week 25, focusing just a bit more on XL; to raise inventory as high as 50 per cent of standard.

Ordering

Ordering will focus on replenishing how much we use for production for main body and aerials; however we will minimize the order for accessories, we will keep around 15000 or as high as twice the quantity of main body and aerials. Therefore in the early weeks there will be no order for accessories.

Non-Manufacturing Environment

This discussion will briefly explain how the function that has been used in many manufacturing environment also used and practise by many non-manufacturing environment such as retailer, military, hospital to solve problems arise in their environment. In this work we will examine through case studies of hospital and how they use the same method as manufacturing environment to solve their problems.

Hospital is a public service environment that is meant to heal, cure, and diagnose people whilst maintaining good cash and patient flow. Recent studies conducted by Rechel (2010) from World Health Organization (WHO) indicates that the current way to measure capacity and performance is through bed numbers, this studies highlighted that there are several problems with this approach, the most important one according to Rechel (2010) is it does not represent any other services provided by the hospital and it cannot predict future demand. There is a demand for change to capacity planning system. Therefore some of the countries have tried to apply manufacturing way of thinking into a non-manufacturing environment, United Kingdom and France is the pioneer in applying this methods. One example given by Rechel (2010) is lean thinking which its objectives are to add value to each process for the patients and to minimize wastes which are not adding value to the patients. To some extent, some of it has already been applied in the healthcare facilities; the old ways has similarity to a batch size process where it is a push condition. Healthcare facilities are now moving toward lean thinking which resembles a pull condition. Rather than seeing capacity as merely the size of warehouses or in this term bed numbers, hospital are trying to see the whole process map that a patient will have to go through.

These studies from WHO can be seen from real case studies from United Kingdom NHS in the next section. There will be two main scopes that will be discussed from the case studies in this report, those are:

Inventory Management scope.

Capacity Management scope.

Inventory Management

This section discusses inventory management in a hospital especially in the United Kingdom; the reason was because United Kingdom health organization has pioneered this kind of thinking in a health care facility which we will discuss through the use of case studies. Inventory management helps NHS to produce better service with much healthier cash flow. In manufacturing environment, the first thing every investor will check is turnover ratio, to see whether the company is healthy or not. Much like in manufacturing environment, we can see performance of a hospital from their turnaround times. However there are differences in the way hospital perform their inventory management, NHS Institute (2008) defines inventory in this case as patient; a patient will require bed, machine, room, doctor and nurses, compared it in manufacturing environment where inventory have to be moved similar to patient moved around hospital to be treated. Therefore has been classified as a form of waste that should be reduced. However they do not really have a large inventory to cope with most of the time, they will improve processes with staff and information flow. The case study below will show how lean thinking can improve their performance resulting in reduced turnaround time.

This is a case study conducted in Hereford Hospital from NHS Trust by Westwood (2006), the problem originated from pathology, for the reason that 70 per cent of clinical decision depends on pathology therefore it is crucial to maintain a steady patient and information flow throughout the hospital as failure to do so may result in an increase length of stay. The problem then escalates where test results face delays that causing patient discharge and inefficient use of staff. Hence by reducing turnaround times this will eventually remove waste, improve flow and free up beds which will save the hospital a significant amount of money.

There are some major improvements that have been done by the hospital such as the introduction of FIFO (First in First Out) system so that all specimens are processed in the same way, which resulted in an improved turnaround times and reduced variation. This is a similar standardization from manufacturing environment where the same products should be produce first to speed up process or late customization. Another improvement that they made is labelling, centrifuges and booking relocated in specimen reception and synchronised. The implication was removal of double handling and centrifuge can be optimised to improve flow during busy times. Rework reduced at scanning stage with fewer staff required, quieter working area, less interruptions and more productive staff. Leading to faster turnaround times, minimized staff movement and an extra space has been created because of the lean system. The other thing that they improve is manned the specimen reception, resulting in almost none delays and the important and urgent specimen can be processed faster than before. There is still some improvement that will not be detailed in here. Consequently the overall performance of these improvements are shown by the turnaround time from 62 minutes to 38 minutes a 40 per cent reduction and approximately 365.000 per year.

From the case study above, we can conclude that the problem solver was lean implementation, as stated before inventories are patients who are waiting to be processed. Hence in this case they have to shorten the process time of in some part of the patient flow in order to be lean thus decreasing unnecessary wait for a result and therefore can free up beds for any other urgent use. The results show that they managed to reduce the turnaround time by 40 per cent means good utilization of staff and equipment. Compared to a manufacturing environment the result is similar, performance measured by turnaround time and improved by conducting a lean approach which to cut cost and waste. Consequently lean can also be practised by non-manufacturing environment.

Next key issue regarding inventory management is runners, repeaters and strangers. An approach has been adapted by NHS Institute (2008) originally comes from Ian Glenday therefore they named the approach Glenday Sieve. This approach begins by identifying common groups of procedure, condition or activities within the healthcare system to be later sorted by the volume of activity. There will be three main groups for this process; Pareto named it ABC, Glenday named it green, yellow, blue and red. The overall objective was to examine which part should be improved; therefore effort will not be wasted on an activity that does not affect the whole flow.

From the approach that NHS Institute (2008) and Ian Glenday, we can say that the use of Pareto analysis is much similar to the manufacturing environment. Pareto will separate the runners, repeater and strangers, so the decision maker can focus their attention on a particular group of product that will improve the overall performance of the company. In Pareto analysis this will be group A where 80 per cent of the revenue comes from 20 per cent of items, whereas in Glenday sieve the green group will represent the process that needs to be improved. The difference is when in manufacturing the data is shown by units in a service company it will be shown in procedures or processes. This approach effectively proves in time that there are specific groups of processes that should be focused on which may improve the overall performance. Consequently Pareto analysis can also be used in a hospital environment to help identify key procedures that should be improve for greater benefit.

Capacity Management

Capacity management in a hospital heavily relate to four main things, demand, capacity, activity and backlogs. These four points will guide us to examine the capacity management in a hospital. In order to examine the capacity management of the hospital first thing that must be done is to make a process flow of patients NHS Institute (2008). This way it will be easier to forecast how many resources do we have to prepare. After listing all processes patient will be going through, notice that in certain parts of the process there will be bottlenecks, backlogs and waiting list. To prevent this from escalating NHS Institute (2008) has a method called the process template which can help the staff to react when bottlenecks and backlogs occur.

According to NHS Institute (2008) there are several steps to implement a solution:

Map patient journey.

Locate the longest delay that occurs in the journey.

Identify and search for any detail information about the delay.

Search for constraints such as room time, consultant time.

Using a tool called “5 Why” to identify the bottom of the problem.

After doing these five key steps, the next thing to do is to measure demand and capacity. Therefore the objective is to see how many resources we have compared to how many resources we have to provide. Bear in mind that in any industry there should not be an overwork as this can contribute to the lack of judgment from the staff that may create more problems.

To measure demand, simply multiply the number of appointments / referrals to the number of consultation time. For example there are four referrals and the time to make one consultation is 30 minutes therefore the number of demand is 120 minutes. The next step is to measure capacity, this is done by multiplying the number of equipment and the number of staff hours, so if there are two machines and eight hours a day to care for a patient, the capacity will be 16 hours available each day. It is easier to calculate true capacity than true demand, again NHS Institute provide the guidance:

Calculate the overall supply service time.

Capacity will sometime changes because of maintenance or sick staff, therefore it is important to consider how the capacity will change because of this.

Can the service be provided in a shorter time period?

Checking if the service provided is really necessary for patient.

After having the demand and capacity numbers, the next step is to measure activity, it is easy to miscalculate activity over capacity or demand, therefore NHS Institute (2008) define activity as the amount of actual work that is carried out by the staff. As an example, if there are 10 patients each requiring 15 minutes of processing therefore the amount of activity is 150 minutes. Bear in mind that this number does not represent demand; it is easy to exchange one another. There may be a backlog or unutilized staffs this is the objective calculating the number of activity. The last item that needs to be calculated is the number of waiting list patients or backlogs. If there are 20 patients on the waiting list with 40 minutes treatment time then the backlog will be 800 minutes or 14 hours’ worth of backlog. Staffs have to make sure they do not count the same patient twice, because some patient needs to undergo a couple of treatment, so they only count in the early stage of the waiting list.

After all the data has been completed and calculated accordingly the final step is to match demand to capacity, there will be two key strategies NHS Institute (2008) the first one is improve capacity within the system, which means that in a system there will be a continuous improvement to add more capacity without having to add staff and machines. The second one is regarding flexibility of the capacity. In a hospital there is a tendency to protect today’s limited capacity and pushing demand in the future. What they should have done is to protect tomorrow’s capacity therefore they need to pull demand into today.

There is one case study NHS Institute (2008) that shows the effect of matching demand to capacity. A GP practice which has an average waiting time of 4.79 days, after seeing this they then reviewed the demand: the number of appointment requested on a daily basis and its capacity: the number of appointments available on daily basis. This information helps the practice to change appointment system to satisfy the demand through the introduction of different ways of accessing care. Based on the demand and capacity information skill mix was introduced and a phlebotomist appointed to ensure that patients have the most appropriate member of the healthcare team. The results were staggering from 4.79 days waiting time to 0.32 days waiting time, an improvement of 93 per cent.

From the case study above, there are similarities as to how to measure capacity in manufacturing environment. There is always a need for flexibility, in order to meet demand and capacity, however in this case there is an ethical issue arising, if the hospital has 100 per cent bed occupancy rate does not necessarily mean that the hospital is performing well. This is the main difference that needs to be examined, hospitals have priorities which patient that needs to be treated in the hospital, they can easily told every patient with a common cold to be treated in the hospital just to get a 100 per cent bed occupancy rate however this is not the right way to match capacity and demand. As stated before by Rechel (2010) bed numbers or bed occupancy rate is not a dependable tool for measuring capacity in a hospital.

In the earlier paragraph, discuss about how to measure demand, capacity, activity and backlogs in a consultation process, the next question will be what about patients that have to be treated in the hospital? In consequence there is way to measure capacity; it is from length of stay (LoS). NHS Institute (2008) states that there are more variations in discharge than predicting the number of admission; this is due to the process that the hospital managed. Moreover this situation is worsened by time of admission which is seven days a week compared to discharge five days a week. To solve this problem NHS Institute suggest a proactive discharge planning. This can only be achieved if only the hospital has a clear pathway of care or flow model through the system for every different conditions patient has.

There is one example taken from NHS Institute (2008) from Northumbria Healthcare NHS Foundation Trust. The average LoS for laparoscopic cholecystectomy is 2.6 days to six days, if this number can be reduced by one day; the amount of annual savings can reach as high as £8 million based on saving on 35,400 bed days. Therefore they changed to a system where day case cholecystectomy was the default unless there were valid clinical and social reasons to admit a patient. The impact was a rise in trust’s day around 30 per cent with a high and consistent patient satisfaction.

There is a difference between measuring capacity by bed numbers and by length of stay. LoS can easily predict patient discharge at the point of admission therefore it can free up the capacity from bed numbers that hospital has. However there is one important issue to address before LoS can be used in such way, it is making clear pathways for patients and reducing delays, therefore it can easily predict how long a patient will stay in the hospital; that eventually can free up beds for other needs. This technique has been applied by NHS Institute (2008). In the end it is almost pointless to see capacity from the size of the warehouse, because if you fill it up does not necessary mean that it has perform better, it can reduce the holding cost per unit however there are consequences in having unnecessary inventory. Therefore LoS can be compared to material flow in manufacturing environment; it is easier to predict how much we need if we know how the flow supposed to be worked.

Conclusion

Capacity management and inventory management have been adapted to be used in healthcare facility and there have been real life cases from previous section that can shows improving performance in hospitals. There are some important points from implementing it, those are:

Map patient flow; this will be the basis of knowing the problem and solving it.

Identifying wastes in any form.

Improving process that creates value.

Prioritize improvement by using Glenday Sieve.

As mentioned by Rachel (2010) there is a tendency to move towards manufacture way of thinking to help improve service, and from real life case studies provided by NHS Institute (2008) we can see that it has succeeded adapting manufacturing approach to hospitals and improving performance with steps that also has been undertaken before in manufacturing with similar tools. This proves with right kind of implementation and adjustments, theories that have been practised by experts in manufacturing can be applied and implemented between two environments that do not relate to one another.

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