PWC_7 Capacity and demand management About this free course This content was originally published as an open educational resource on the OpenLearn website http://www.open.edu/openlearn/. This version of the content may include video, images and interactive content that may not be optimised for your device. You can experience this free course as it was originally designed on OpenLearn, the home of free learning from The Open University –https://www.open.edu/openlearn/education-development/capacity-and-demand-management/content-section-0? There you’ll also be able to track your progress via your activity record, which you can use to demonstrate your learning.

Copyright © 2022 The Open University Intellectual property Unless otherwise stated, this resource is released under the terms of the Creative Commons Licence v4.0 http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en_GB. Within that The Open University interprets this licence in the following way: www.open.edu/openlearn/about-openlearn/frequently-asked-questions-on-openlearn. Copyright and rights falling outside the terms of the Creative Commons Licence are retained or controlled by The Open University. Please read the full text before using any of the content. We believe the primary barrier to accessing high-quality educational experiences is cost, which is why we aim to publish as much free content as possible under an open licence. If it proves difficult to release content under our preferred Creative Commons licence (e.g. because we can’t afford or gain the clearances or find suitable alternatives), we will still release the materials for free under a personal end-user licence. This is because the learning experience will always be the same high quality offering and that should always be seen as positive – even if at times the licensing is different to Creative Commons. When using the content you must attribute us (The Open University) (the OU) and any identified author in accordance with the terms of the Creative Commons Licence. The Acknowledgements section is used to list, amongst other things, third party (Proprietary), licensed content which is not subject to Creative Commons licensing. Proprietary content must be used (retained) intact and in context to the content at all times. The Acknowledgements section is also used to bring to your attention any other Special Restrictions which may apply to the content. For example there may be times when the Creative Commons Non-Commercial Sharealike licence does not apply to any of the content even if owned by us (The Open University). In these instances, unless stated otherwise, the content may be used for personal and non-commercial use. We have also identified as Proprietary other material included in the content which is not subject to Creative Commons Licence. These are OU logos, trading names and may extend to certain photographic and video images and sound recordings and any other material as may be brought to your attention. Unauthorised use of any of the content may constitute a breach of the terms and conditions and/or intellectual property laws. We reserve the right to alter, amend or bring to an end any terms and conditions provided here without notice. All rights falling outside the terms of the Creative Commons licence are retained or controlled by The Open University. Head of Intellectual Property, The Open University Good decision-making relies on having the right information presented in the right way. That information comes as data that we obtain either through extraction from existing records or newly collected information that we have gathered for the purpose of decision-making. This makes data analysis one important skill when decision-making. In this course you will take a look at capacity planning decisions as an example of effective data use. It seems obvious that resource plans should not be created without an understanding of the demand for resource, but you will find that demand is not always measured, or collated in a way that is not helpful to decision-making. This course provides a range of perspectives of how data can be understood, in the context of managing capacity and demand. After completing this course, you will be able to: understand how useful data can be to inform practical decision-making see how demand patterns are derived when looking at data understand how seasonal patterns in demand are caused and how this impacts on resource planning understand different perspectives of how demand for services can be assessed see how demand data can be used to plan capacity requirements. One way of understanding demand for much policing activity comes through criminology – the scientific study of crime. At the simplest level, it is useful to know what types of crime occur and how frequently they are committed. For example, crime statistics in England and Wales show the following most commonly reported crimes:

Figure 1 TCSEW estimated 3.1m theft offences in the last 12 months. (England and Wales, based on interviews conducted between May and December 2020.) The diagram is a bar chart that shows the frequency of crimes reported in a survey. The crimes are listed from left to right in decreasing order of frequency starting with fraud as the most common offence, with over 4 million recorded. The other crimes in order are theft, computer misuse, violence, criminal damage and robbery.

Activity 1 Crime statistics Allow approximately 5 minutes How do these crime statistics help plan policing capacity? What are their limits of how they help? The crime statistics can act as a guide to what crimes police deal with and the skill sets needed within forces to deal with those crimes. The statistics are also useful when observed over time as trends in crime types can be observed. Planning can then address how to reduce or prevent these crimes or look at increasing particular skill sets. There are a number of limitations. First the statistics themselves can only be a guide as much crime is unreported. Secondly, up to 80% of police work is not crime-related (Boulton, L. et al., 2017) so when we look at decisions such as numbers of police needed we are missing most of the workload. Thirdly, it does not usually tell us when or where officers are needed. The next section looks at an alternative way of understanding patterns in crime. Criminologists can look at why crimes occur and both where and when they happen. Crime pattern theory looks at the geometry of where crime occurs, based around the notion of where crime activity takes place and how offenders travel through likely places for crime and develop knowledge of those locations. Activity 2 Locations of crime Allow approximately 5 minutes Reflect on your own known locations for crime or other police demand. Are there different locations dependent on the time of day? Most of this would be personal to your situation but there are some obvious night-time economy sources of demand (public order and violence) which will occur in specific locations. This type of analysis helps us understand demand from a policing perspective. In the next section we see how we can take a management perspective to understanding demand. In this section we take a look at some of the managerial perspectives of demand and capacity which focuses on understanding demand for decision-making. Activity 3 Capacity management Allow approximately 10 minutes Pick any service that you are familiar with, such as a supermarket, airport or restaurant. Imagine that service in a situation where it does not have anywhere near enough capacity to meet demand. What sorts of things will go wrong? How will the capacity shortage affect the efficiency of the service?

Figure 2 People queuing inside an airport The figure is a photograph of a crowded area of an airport where hundreds of passengers are queuing to go through a gate.

Now imagine the same service but with far too much capacity. Is this a better position to be in? Will it be efficient?

Figure 3 Airport car park The figure is a photograph of a large airport car park where there are hundreds of empty parking spaces with a few parked cars spread across the parking area.

A service that is short of capacity will clearly have some very visible problems of queues and delays. We’ve often been in a position of queueing for hours to check in for a flight, been crammed into a train with no room, told that there is no table at the restaurant and so on. The performance of such services often rapidly declines once demand significantly exceeds (or even closely matches) the capacity to serve customers. In many cases the service quality suffers and staff become more stressed, often making the situation worse in the long run. In emergency services the response time to go to incidents rapidly declines when there is a shortage of capacity. Services that have excess capacity sometimes also experience problems. For example, the atmosphere in an empty restaurant can be uncomfortable, especially if the staff are constantly hovering over you. Sometimes the service seems too rushed. Staff motivation can suffer if they don’t have enough to do. There is also the obvious problem that there is a lot of waste of resources, both in terms of under-utilised staff but possibly wasted food etc. For emergency services the responsiveness can be good, at a cost of sometimes unused staff. Given these two extremes, is there a balancing point where capacity is enough to do the job without there being excess wasted resource and maintaining good quality? One technique that can be used to analyse local policing demand blends the SARA decision-making model of policing with demand analysis. The example below shows how a demand problem might be broken down into steps: ScanningWhat are the recurring problems of concern to the public and police?Can we confirm the problem exists?Do we know how frequently the problem occurs? AnalysisWhat data do we need to analyse the problem and derive solutions?What ideas do we test to see why the problem is occurring? ResponseWhat interventions to reduce the problem can we test?Do we have a plan to implement solutions and test their effectiveness? AssessmentDid our interventions work?What evidence do we have to understand how well our interventions worked?Do we need to do more work? This approach can be used to identify many types of demand problems in policing, such as: Understanding the nature and scale of repeat offending Identifying where people are repeat victims and why this is the case Repeat locations Anti-social behaviour Case study: Repeat attendances at A&E In November 2021 the BBC reported new analysis by the British Red Cross of NHS data on regular Attendance at A&E departments. They discovered that less than 1% of the population account for 16% of all A&E attendances, with just 0.7% of the population accounting for 29% of ambulance journeys and 26% of unplanned hospital admissions. The data also showed that many of the regular attenders were dealing with ‘mental health problems, relationship breakdown, housing insecurity or loneliness.’ Some vulnerable people use A&E weekly or more BBC News hiu-summary-report-final.pdf (redcross.org.uk) Activity 4 Accessing services Allow approximately 5 minutes Think about your own work. Do you come into contact with people who regularly access services for similar reasons or problems such as drug addiction? The next section looks at how we can apply the SARA decision-making model to help us understand demand management problems. Capacity planning is a process of understanding demand and organising the right resources to meet the demand cost effectively. Conventional approaches divide the capacity management tasks into three time horizons: Long-term planning (18 month or more time horizon):Long-term planning usually involves the planning of new facilities or locations and sometimes the recruitment and training of specialist staff. For example, the NHS has to plan the training of doctors many years in advance as it takes over 8 years to train and provide enough work experience to develop a new junior doctor. Medium-term planning (3-18 month time horizon):When you look at demand patterns over a year or so you will usually see seasonal patterns to demand that require any operation to make adjustments to the availability of resources over that time, with peaks and troughs in demand. Organisations have to develop plans, such as shift patterns, hiring of seasonal or temporary staff etc. to be able to cope with these fluctuations. Short-term planning (less than 3-month planning):Much of the planning work is to ensure that the right people are available at the right time and place. Often this is a scheduling role – which often also needs to include some reactive work to cope with unexpected events. Activity 5 Capacity planning Allow approximately 5 minutes Think about how far in advance you may have to look if you are to plan for the following types of decisions: Building a new facility such as a police contact centre The full training and development of highly skilled and specialist staff Both of these decisions would come under long-term planning as the time horizon in each case is many years. In the case of the new facility it can take many years to obtain the right permissions and funding. To develop a highly skilled workforce again the lead time can be many years through degree education and then training. This can present problems as situations can change, leading to over- or under-supply of critical resources. The next section looks at the influence of timescales on how demand and capacity decisions are made. Simple models of demand break down the total demand into four components: Long-term growth or decline patternsDemand is rarely completely stable. We often find that some types of demand are undergoing growth and others have significant declines over time. There can be “life-cycle” models of demand where demand for something grows, stabilises and later falls again. Cyclical or seasonal patternsMost demand is cyclical in some way – going through peaks and troughs – with a variety of causes. We’ll explore this a bit more. Random variationSometimes things are busier or quieter than we have expected, with no known explanation. This is often seen as random, natural variation. Special cause or one-off eventsOne-off events, planned or unplanned, can cause demand to increase or decrease significantly, usually as a temporary effect. Activity 6 Patterns of demand Allow approximately 5 minutes Which areas of your own demand would you see declining or increasing? In policing there are several areas where more demand is expected in the future in general. These include cybercrime and online fraud, with some offences being under-reported, such as domestic violence. In the future it is likely that some of these types of offences will be reported more. There is a trend towards some additional non-crime demand for resources, especially that related to incidents involving mental health. Some crimes, such as burglary have fallen. You may have some very specific local trends in demand that others might not see so much. In this section we look at the idea of demand seasonality. This is where there are cyclical patterns in the demand data. Seasonality can occur over a range of timeframes. For example we will always expect some annual demand seasonality, but many services will see daily or hourly demand cycles they have to manage. Activity 7 Causes of demand seasonality Allow approximately 5 minutes Take a look at the following products. What are the underlying causes of demand seasonality over a year? To what extent will demand change?

Figure 4 Examples of seasonal products The figure is a composite picture showing four types of product that might exhibit seasonal demand patterns. The products are fireworks, suncream, easter eggs and soft drinks.

There are two categories shown in the photos: Products with weather seasonality Products such as sun cream, soft drinks and umbrellas have seasonality based around weather. Sales of drinks and sun cream can go up by 500% in a week during a hot spell – but this is not the time people will buy umbrellas! There are other underlying factors – for instance the sun cream sales will also increase at a time when more people start to take summer holidays. Products with event seasonality Products such as Easter eggs and Christmas cards clearly have a seasonality based around particular festive events. A problem for those making them is that the sales window for such products can be narrow. Other seasonal factors What other seasonal factors exist? Beyond festive events and weather, are there any other causes of fluctuations in demand? Figure 5 below shows some of the underlying causes of demand seasonality.

Figure 5 The underlying factors that influence demand seasonality The figure is a diagram with the phrase ‘demand seasonality’ in the centre in bold writing. Around the outside are six boxes that identify separate drivers of seasonality. These drivers are climate and weather, festive, financial, social, political and behavioural.

The next section looks at situations where demand can spike during ‘one-off’ events.

Sometimes we get special ‘one-off’ events that increase or decrease demand hugely. Sometimes these are planned, such as a music festival. Sometimes these are unplanned or unexpected, such as an industrial fire. Example of special events: Notting Hill carnival

Figure 6 The Notting Hill Carnival The figure is a photograph of people participating in the Notting Hill Carnival procession. They are wearing bright costumes. In the picture are two uniformed police officers joining in with the celebration.

The Notting Hill Carnival in London is regarded to be the second largest carnival in the world, behind the one in Rio de Janeiro, with 1 million people attending over the extended holiday weekend. The carnival has about 40,000 volunteers helping out and usually about 9,000 police. The carnival creates a huge surge in demand for both private and public services. In a normal year about 270 licensed food, drink and merchandise stalls temporarily appear. The organisers also provide support services, such as an extra 329 sets of temporary toilets. Local transportation has to adapt to be able to accommodate the extra inflow and outflow of people in the Notting Hill area. Local healthcare systems also have to deal with an extra 1,000 casualties needing ambulance or paramedic care, with 20% going to hospital. Activity 8 Seasonality in your own service Allow approximately 10 minutes Now apply these types of demand variation to your own work. Fill in the table below:

Question	Your response
Are there daily fluctuations in your demand? If so, why?
Does day-of-week affect your demand?
What annual cycles of demand do you have (if any)?
Are there special events that increase your demand?

To what extent does your organisation measure or record demand and especially these demand fluctuations? We would expect most people to be able to identify examples of demand behaviour in all of the above categories. Anti-social behaviour, for example, tends to happen more when pubs and nightclubs are open but are starting to shut. They will also have an obvious day-of-week effect under most circumstances simply because Friday and Saturday are their busiest days of the week. However, these differences are becoming less noticeable in many cities. Most places will have annual cycles, but not necessarily the same ones. For example, university towns will have demand created by events such as ‘Freshers’ week’. Seaside towns will experience increases in many types of demand during the summer and other school holidays. You should be able to find special causes such as carnivals, concerts and festivals in many places. The next section looks at how this understanding of demand patterns can help build a forecast that is used to predict future demand.

Most situations use one of three types of forecasting model to help predict demand: Qualitative techniques can be used when there is no prior data. These would include expert panels or conventional market research, where this is appropriate. Time series analysis takes historical data and looks at the patterns in it with the intention of understanding future demand if the same patterns repeat. The analysis can range from the relatively simple approaches of understanding averages (‘moving averages’), to much more complex multi-variate techniques and probabilistic analysis. Causal analysis can be used to develop more sophisticated demand predictions. Data is used to help understand the factors that drive demand. For a practical understanding of local demand, simple time series analysis is probably the most appropriate, but strategic planning activities will often use more sophisticated causal models. We have spent quite a bit of time understanding demand. We will now focus on understanding how we manage capacity to meet demand. When demand patterns are understood a service can decide when and how much capacity to provide to meet that demand. Where demand fluctuates there is the question ‘to what extent do we change capacity over time to match demand?’ This section looks at the reasoning that addresses this question. Activity 9 Demand over time Allow approximately 10 minutes The figure below shows the expected demand over a period of time, e.g. one year. You can see there is quite a lot of seasonal variation in expected demand.

Figure 7 Meeting demand with a ‘level’ capacity strategy The figure is a line chart with the x axis labelled as ‘time’ and the y axis labelled as ‘demand’. There is a line on the chart (coloured in black) labelled as ‘demand’ that starts low, dips, and then curves upwards towards a peak, falls to a new minimum point, and then rising again. A second line, labelled ‘capacity’, sits entirely above this fluctuating demand curve. This line is straight and horizontal across the entire graph.

The figure also shows a potential ‘capacity strategy’ that provides a constant amount of capacity throughout the year – known as a level capacity stategy. What do you think the likely advantages and disadvantages of this strategy are? The level strategy is not good at minimising wasted or idle resource as there are long periods where capacity significantly exceeds demand. A key advantage is that the plan should always meet demand – as long as the demand forecast used is reasonably accurate. Another simple feature is that the plan is quite easy to implement as the people involved in organising resources know that they need to keep the resource plan the same throughout the year. Now let’s look at a different capacity strategy. In this case the capacity is adjusted periodically so that it matches more closely the profile of forecast demand. This is known as a chase strategy. Is this always a better approach?

Figure 8 Meeting demand with a ‘chase’ capacity strategy This figure is a modified version of figure seven. The horizontal line is replaced by a stepped line, for capacity, that consistently sits above the demand curve but generally follows the shape of the demand curve, representing many changes in capacity to loosely match the changes in demand.

The resources in this case have higher utilisation and you would expect this to be more efficient. However, this is not always the case. The key issue is that changing the capacity can be difficult to implement in some cases – think about how we might do this: Seasonal staff? Different shift patterns? Agency staff? In some of these cases the extra resource might be more expensive and less productive than those on long-term contracts. Hence we sometimes have to balance between the stability of level strategies and the complexity of chase. The following screencast explains the theory of level and chase capacity strategies in more detail. Screencast 1: Level and Chase [MUSIC PLAYING] PAUL WALLEY In this screencast I'm going to explain the two main medium term capacity strategies used by services to help meet demand. These strategies are known as level and chase capacity strategies, which offer two fundamentally different ways to reconcile demand and capacity balance. This graph shows an imaginary but typical situation, where we have demand changing over time. This time period could be a day, a week, or a year. We usually find that demand is not constant, as there will be seasonal peaks and troughs in demand. We need to decide if we need to adjust capacity to match those peaks and troughs. One option is to keep capacity fixed. We know this as a level capacity strategy. What we can see from the diagram is that we've set capacity at a level that's consistently above demand here. Under those circumstances, we expect all of the demand would be met in that time, as there is always enough capacity. There are certain trade offs that we have with level capacity, in that we see that there are periods of time where a lot of capacity might be wasted. For example, we may have service personnel standing around with very little to do. At other times, we see that the same people will be quite stretched. We might wonder if the quality of the service might be maintained during that time. The level capacity could be set lower than that shown on the graph, more towards the average demand. This would cause backlogs and delays at some busy times, and the backlog might be cleared when less demand is coming in. The alternative approach is to adjust capacity in anticipation of a change in demand. We could look at demand forecasts and anticipate when peaks and troughs are going to occur, usually through seasonality of demand. We then make adjustments to capacity to match the general pattern of demand over time as best we can. We know this as a chase capacity strategy. In the diagram here, you can now see that our chase strategy has tried to match demand and capacity as much as possible. It won't match perfectly, because we need to add capacity in particular blocks, such as one extra person, an extra shift, and so on. And so we get an approximate match but not a perfect match. And in this case, we still get a little bit of wasted capacity some of the time. The issue here is that we often have to plan very carefully for these additions and reductions in capacity, and that might not be so easy to implement. We find that these two capacity strategies have different key features. The level capacity strategy is much easier to plan. You decide what capacity to set, implement that plan, and it never changes. A level strategy is easy for both employees and customers to understand. So if you've got customers needing to understand when a facility is going to be open, for instance, then a level capacity strategy is useful, because you will be consistent in your opening times. A level strategy should ensure a particular service level so that we can set the capacity to always meet the demand ideally. The downside is the potential waste, or the underutilization of resource. There are also potential implications for staff who might have to go from busy times to very quiet times, and quiet times can be surprisingly demotivating. With a chase capacity strategy, we might have to use fairly complex schedules, and this can be very difficult to plan and quite confusing for staff. Sometimes the chase strategy should ensure a service level, but we could get the forecast wrong. And under those circumstances, we could be caught out with insufficient capacity. The chase strategy has high utilization of resource, but sometimes that might not be so good. We could tire out our workforce, for instance, if we keep them too busy. Sometimes a little bit of downtime isn't a problem. Then you have to ask the question, is the chase capacity strategy cheaper? It might cost you money to change the profile of your capacity over time. Sometimes additional short-term staff, such as agency staff, can be more expensive than core staff that we employ full time. This would make capacity increments more expensive. Are the new staff as productive or as competent as core staff? In summary, there is no one single right answer to which approach to take. And, in fact, most services use a mix of level and chase strategies. It is up to you to decide which of these options to take based around your own local circumstances.

The next section looks at how you might visualise data such as demand patterns. Sometimes you won’t have access to large data sets that will give you a definitive answer about what demand is coming in. However it is worth looking at the data just to gain an impression of what might be happening. Activity 9 Arrival patterns in A&E Allow approximately 15 minutes The table below contains real data of patients arriving at a small emergency department of a hospital, recorded by day of week. Patients arriving at an emergency department by day

Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday
86	104	105	75	95	103	96
85	83	82	78	94	91	87
89	102	85	80	99	109	114
105	91	97	90	82	106	107
95	88	90	86	91	76	75

How might you first look at the data? What sort of analysis might you first undertake? Have a think and then click the discussion button below. The first action could be to plot the data on a simple chart just to see if there are any apparent trends or patterns that are obvious. We might be looking for any growth or decline trends, but these are unlikely to be seen in such a small data set. There could be cyclical patterns based around day of week but these are not immediately obvious from this graph, so more work can be done there. It is obvious that the data has some random variation.

Figure 9 A line chart of the demand data The figure is a graph with demand on the y axis and time on the x axis. The line shows the data from the exercise plotted as a time series. The line shows a lot of random variation with some barely discernible cyclical patterns.

What day of week effects might be seen? How can you analyse this?

	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday
Max	105	104	105	90	99	109	114
Mean	92	93.6	91.8	81.8	92.2	97	95.8
Min	85	83	82	75	82	76	75

Figure 10 A graph of the demand mean and range by day of week The figure is a graph with demand on the y axis and day of week on the x axis. There are lines plotted within the chart. The main line is a graph of average demand. The maximum demand and minimum demand are also plotted by day of week as lines that sit either side of the mean line. The graph shows a lot of consistency in demand across the week but with slightly more demand and demand uncertainty at the weekend.

In this case the data tells us that most weekdays are very similar in terms of the number of people coming into the department for some kind of treatment. A typical day would have around 92 patients to treat over the 24-hour period. Thursday seems to be unusual – but there isn’t an obvious explanation. There does appear to be a slight difference at the weekends, which seem to be slightly busier but also the numbers seem to vary more as well. This might need to be explained. Obviously, only a much bigger data set would give us statistically significant results, but at least we now know something about the likely patterns in the data. We’d also need a much bigger data set to look at annual variation etc. (e.g. summer vs winter). How might this outcome affect capacity planning? We would need to look at the distribution of arrivals over the 24-hour period to fully understand what to do here. However, if we need to treat every patient how many should we be capable of seeing each day? If we plan for 92 patients per weekday would this be enough? If we only planned for 92 patients per day we would most likely not have enough capacity about half the time. To absolutely guarantee meeting demand we would have to take a look at our busiest ever day. As we have only a small amount of data demand can be even higher, as natural variation and special events would occasionally take it beyond our observed maximums. In practice we would have to also consider having some time of capacity on standby, maybe working other roles but brought in if demand goes higher than expected. In the next section we will also consider how this demand and capacity balance can affect how long people wait in a queue. We next look at the causes of waits and delays in services, such as how queues form. Just about everyone has experience of being in some sort of queue. If you phone your bank, telecoms provider or similar service you will almost certainly be phoning a call centre where you can often expect to wait before someone picks up the phone. Similarly if you visit a supermarket you can expect to wait before you pay. In a barbers shop you wait your turn. If you go to a theme park many of the rides can have queues that are an hour long on busy days. So why is it that all of these different types of service make you wait? Are they all just incompetent at managing capacity? Is it they are greedy and won’t pay enough staff on duty to meet demand? Download the following excel file and open it so you can see the content (open the file in a new tab or window by holding down Ctrl [or Cmd on a Mac] when you click on the link). Basic capacity model Now watch the following screencast that shows you how to use the spreadsheet in the following exercise. Screencast 2: Instructions [MUSIC PLAYING]   PAUL WALLEY In this screencast, I'm going to provide some instructions on how to use the capacity and demand simulation spreadsheet that we have provided for you. This is a screenshot of what you'll see when you open up the file. The spreadsheet allows you to change five different variables within the spreadsheet so that you can experiment to understand queue behavior. The first change you can make is to adjust the amount of demand that comes in by setting the minimum and maximum demand levels. The two cells you can change are both highlighted in yellow. You can also adjust the capacity again, minimum and maximum values in those yellow boxes. The final variable you can change is the size of the queue at the start of the simulation. The cells that are colored in pale blue are outcomes of the simulation. OK, so I've opened the spreadsheet and you can see here that I've got the five cells colored in yellow while I can now change the numbers. If I want to adjust the demand, I can change these in cells B3 and C3. So if I want to increase demand by 10, I just add another 10 to the figure in those cells. And you can see the behavior of the queue has adjusted the demand and capacity ratio in cell L4. I now know that I've got zero unused capacity, but I can make adjustments to the capacity. I can increase this in both cells to 50. You may have seen that it came up with an error at one point, where I had got the minimum greater than the maximum. The spreadsheet will warn you about things like that. All of the other data has readjusted as we have made that change, including both charts. You can also change the size of the queue at the start as long as it's a positive number. So if I wanted to have a simulation where I had 200 people in a queue at the start and then wanted to see how long it would take to drain down the queue with revised capacity levels, I could put that into the simulation. So the course sets you an exercise, and that is all you need to be able to complete the work. Once you've opened up the spreadsheet, we ask you to do three separate exercises. The first is to set the capacity much below average demand so that you can see what happens to the size of the queue and make a note of this, perhaps even taking a screen copy of the graphs. Once you've done that, set the capacity equal to the average demand but with a little bit of either demand or capacity variation or both, and see if the behavior changes. Once again, make a note of that. The final exercise is to incrementally increase capacity until you think you have enough capacity to prevent any problems with queuing. However, you can use the exercise to understand the trade offs between queue length and wasted capacity. Once you've completed the exercise, you should have a much better understanding of the relationship between capacity, demand, and queuing. [MUSIC PLAYING]  

Using the average demand of 40 (ranging between 20 and 60) as has been set in the spreadsheet, compete the following tasks: Task 1 Set your capacity much below average capacity. What happens to the size of the queue? Make a note of this. Repeat this a few times to see if the answer is always the same. Task 2 Set your capacity at the average demand. Has the queue behaviour changed? Again make a note. Again, repeat this a few times to see if the answer is always the same. Task 3 Now incrementally increase capacity until you think you have enough. How much capacity makes your queue disappear to almost zero? Does the existence of a queue provide a clear indication there is not enough capacity to meet demand? Once you have completed all the tasks watch the following screencast that provides feedback about the findings that you should have got from the exercise. Check that you did get the same kinds of results. Screencast 3: Feedback [MUSIC PLAYING]   PAUL WALLEY I just want to show you the results that you should have got when you did your simulation exercise using the spreadsheet that we have provided. In the first part of the simulation, we suggested to you that you should set demand greater than capacity or reduce the capacity so that there was insufficient capacity to meet the demand over time. What you see from the spreadsheet is that the number of people in the queue steadily increases over time. This is just the kind of classic situation where a work backlog is continually building. In practice, it isn't common to see queues build up steadily in this way. For physical queues such as at a supermarket or bank, the queue often self-limits as customers renege, leave the queue, or balk. They don't join a long queue instead of waiting for a long time. This simulation doesn't replicate that behavior. In a situation where demand and capacity are about the same but you have some variation in either demand or capacity or both, you tend to find is that the queue fluctuates over time. In this example, I had set the demand and capacity as approximately equal but with variation. We see that a fairly substantial queue builds up quite quickly and towards the end of the simulation, the queue seems to continually grow, which is obviously a concern. Despite this, just note that 2% of the time, we had unused capacity when the queue had fallen to zero. So we're in a situation where queues are long, services poor, but we still waste some capacity. This is not a great place to be. In the final situation, we set capacity quite considerably in excess of demand. In this case, we can see that the number of people in the queue was actually relatively small on average. In fact, on average, it was less than one person at a time, but the queue did peak at nine people. Here, we are in a very different situation, where we had 38% more capacity than demand and that created a lot of wasted capacity over time. So here's the trade off. You can have some excess capacity to make sure that the queue is moderately small, but it means that you do waste some of your resource. This can be very difficult to reconcile when senior managers are looking at staff productivity and staff costs, but you need to maintain the availability of the service. [MUSIC PLAYING]  

The exercise shows that queues can form even when capacity is greater than demand. The following screencast explains the theory underpinning this idea. Screencast 4: What causes a queue? [MUSIC PLAYING]   PAUL WALLEY In this screencast, I would like to address what causes a queue in services. There is a good body of knowledge about how and why queues form. You have the opportunity to simulate this with a spreadsheet exercise, and now we look at the theory behind queues. In pretty much all services, you'll find that as you experience the service, you will be involved in queuing. Most of us will have waited a long time to check baggage in at an airport, been hanging on the telephone waiting in the queue to speak to a call handler, or in a line at the post office. So the question is, can queues be avoided, or are there some underlying reasons why queues form naturally? Just imagine a situation where you are checking in to go and visit somebody in an office, maybe at a reception. On average, it might take three minutes to sign in to that reception and receive your visitor's pass. In this situation, on average, one visitor arrives every three minutes. So here we have a situation where capacity and demand are supposedly matched. So will there be a queue at reception? In the narrative, I used a very dangerous word, average. In the situation I described, demand will fluctuate over time caused by natural, random variation. And sometimes the capacity can vary, because some people will take longer to check in than others. So we have both demand and capacity variation. In these kinds of situations, if demand and capacity don't perfectly match, even at very low levels of utilization, a queue may form some of the time. While we have plenty of capacity and relatively little demand, we won't get much of a queue, but queues can exist. The problem occurs when resource utilization levels get above, say, 60% or 70% in these circumstances. At this point, our expected to queue length really does increase. And mathematically, if you have 100% utilization, with demand and capacity variation, you are likely to get an infinite queue unless there is some other controlling mechanism. This is a really strange mathematical phenomenon. The underlying mechanism for queue development are the short-term mismatches in capacity and demand. In the diagram, demand is going to fluctuate randomly, and so might capacity. Where both demand and capacity are fluctuating over time, it is highly likely that most of the time capacity and demand will not be the same. So sometimes demand is in excess of capacity, and that is where queues build up. At other times, where capacity is in excess of demand, and any queues we have will start to reduce So it is these mismatches that create these queues, and the queue length will not be stable. You would have seen that if you did the simulation exercise. Our design of the queue actually influences queue length and waiting time. Think about these two different queue designs. Queue type A is a typical situation that you'd get at an airport baggage check in, for example, where the queue is entirely combined into one, and people go to the next available server. Queue type B is where you have to pick a server and join a queue. And you can't move from that queue no matter how long it takes. So the demand is split into two or more separate queues. What we find in practice is that queue type B creates a longer wait. This is for a number of reasons. One of the main reasons is that by splitting the demand into more than one pool, we actually get more variation in demand. This is where we get some surprising results. So if we split demand into priorities, so urgent, not so urgent, lesser, and so on, we artificially increase the demand variation and create even more waits. So prioritization can actually cause delays. There are other mechanisms in queue type B. Some servers might be less busy running out of work, therefore wasting some spare capacity. In a lot of situations, pooling the demand into one creates shorter waits. So there are some key points here. Queues form in two different types of situation. We will get a work backlog where demand exceeds capacity, but in practice most of the queues that we see where the capacity exceeds demand, but we have variation. Take time to understand these two different types of queue, and have a think about waits and delays might be minimized, through either controlling the variation in demand or capacity, and also perhaps planning capacity better.

Waits and delays example: Ambulance service responsiveness Ambulance services are a good example of where demand must be understood so that planning can take place to ensure enough ambulances are available to meet all emergency demand. In the UK ambulance services are tasked with responding to life-threatening, urgent demand (“Category 1”) with an average response time of seven minutes and 90% within 15 minutes or less. The services have to plan the right number of ambulances and crew each day, anticipating any seasonal fluctuations in demand and taking into account random variation in demand that makes some days unexpectedly busy. As we can see from the previous section, there would need to be some spare capacity in the system if queues are not to develop. When calls come in they are triaged so that urgent cases are put ahead of those less serious. When ambulances attend calls they will try to deal with the patient without a time-consuming journey to hospital either by treating and discharging those less seriously hurt in situ or by seeking alternative forms of transport to hospital for “walking wounded”. Some services even organise taxis for patients where an ambulance is not strictly necessary. At busy times for emergency care one of the biggest problems is that ambulances often have to queue to drop patients off at A&E. This means the ambulances become unavailable for new patients and response times increase.

Figure 11 Ambulance queue outside an emergency department The figure is a photograph of ambulances queueing outside an accident and emergency department. It illustrates the waits and delays experienced by both paramedics and patients when trying to enter an emergency department that has a work backlog and arrivals queue.

We can see from queue theory that a small reduction in demand might make a big difference to waits and delays. The next section looks at an approach that tries to reduce demand entering a service by eliminating unnecessary demand. Think about the last time you phoned a service provider. Was this for a good reason (e.g. you wanted to buy something) or was it because something had gone wrong? When we analyse calls going into service calls centres we often find that a very large proportion of the calls up to 80% are from unhappy or disgruntled customers reporting a problem rather than customers contacting the service to generate new business. Demand entering the system for avoidable reasons is often referred to as failure demand. One definition is shown below. ‘Failure demand is demand caused by a failure to do something or do something right for the customer.’ This idea that much demand is unnecessary or avoidable came from John Seddon when he had looked at calls coming into call centres. He divides demand into two categories ‘true demand’, i.e. new demand from a customer and ‘failure demand’. Activity 10 Failure demand Allow approximately 10 minutes John Seddon and his consultancy team analysed 1200 calls coming into a council housing service call centre (Evaluating Systems Thinking in Housing, 2008). Most callers were council tenants who were dealing with necessary repairs to their properties. The team grouped these calls around main themes, listed below. Use the table to click whether you see the demand as ‘true’ demand or ‘failure demand’. Count up what percentage of the total you see as failure demand. Then look at the two columns. What underlying problems are revealed by the nature and levels of failure demand? Those calls requesting a service for the first time, such as ‘I need a plumber’, are all true demand. The failure demand calls are made when something has either gone wrong, e.g. ‘the repair was not fixed properly’, or the work is being chased by the customer, e.g. ‘I’m still waiting for…’. The chart below shows that 80% of the calls coming into the system were failure demand and the most common types of call were all failure demand.

Figure 12 Call centre demand chart The figure is a bar chart with the labels of the types of demand on the x-axis, starting with ‘I was out when someone called’. The bars on the left hand side of the diagram are the tallest, going down left to right in descending order of frequency. The six bars on the left hand side are all highlighted in bold, showing they have been classed as failure demand. The five bars on the right hand side are identified as true demand.

There is clearly a problem within the housing service because too many work crews are attending properties when the customer is not present. They also seem to have difficulty finishing a job in one visit (shortage of spare parts?), or returning to finish a job in a timely manner (scheduling?). When the problem was studied in more depth it became clear that work crews were not empowered to deal flexibly with their customers’ problems because the cost control systems limited the availability of spares, required too many sign-offs before work was completed, and was too rigid in its scheduling of crews. Once crews were given more control over their work they were able to call customers in advance and ask about the need for spare parts etc., with deliveries to site quickly. This produced a huge improvement in the quality of the service to the customer and reduced demand. This next exercise allows us to explore the notion of failure demand in a practical exercise. Use this as an opportunity to see whether or not you can spot unnecessary demand in a system. Activity 11 Failure demand Have a think about how much of the work coming in to your system might be classed as failure demand. What are the underlying causes of that demand? Also think about the impact on your work and whether or not the demand can be removed. One of the biggest, somewhat hidden, sources of failure demand can be seen just by looking in your email inbox. Have a look to see how many emails are repeat requests, such as reminders for deadlines. How many of the emails are you copied in when it’s irrelevant to you? For much of the demand coming in, have a look to see if there are more opportunities to do the work in the first contact, rather than prioritising or postponing work. One police force found that 100 non-urgent calls into the contact centre generated about 60 unnecessary additional contacts, with some calls needing 5 or more attempts to do simple things like answer a question. This session concludes all the topics of Capacity and demand management. Now read the course conclusion on the next page. Good decision-making requires effective data analysis when making challenging decisions such as the allocation and timing of capacity. When you look at your own data you will start to see some clear patterns in demand for the services you provide, but sometimes it can be difficult to find enough data. In these situations it is perfectly acceptable to use simple visual tools that give some insight into how demand behaves. The course should have given you a better insight into some operational problems such as response delays or queues in services. When data is collected you can bust the myth that queues only form when systems are under-capacity. The data analysis provides a better insight into why these delays occur. Our final concept we looked at was that of unnecessary or ‘failure’ demand. This can be a really useful idea to challenge what demand is coming into a service and what workload it should give you. You are encouraged to try to apply this idea further in your own work. This course was produced by The Open University in association with the Police Service of Northern Ireland. You can find out more on courses from this series on the Community engagement and policing page. Boulton, L., McManus, M., Metcalfe, L., Brian, D. and Dawson, I. (2017), “Calls for police service: Understanding the demand profile and the UK police response”, Police Journal: Theory, Practice and Principles, 90 (1), pp 70–85. Brantingham, P. L., & Brantingham, P. J. (1981). Notes on the geometry of crime. In P. J. Brantingham & P. L. Brantingham (Eds.), Environmental criminology, Beverly Hills: Sage, pp 27–54. Christopher H. Lovelock (1984) Strategies for Managing Demand in Capacity-Constrained Service Organisations, The Service Industries Journal, 4 (3), pp 12–30. DOI: 10.1080/02642068400000059 Seddon, J. (2003), Freedom from Command and Control, Vanguard Press: Buckingham. Jackson, M. C., N. Johnston, and J. Seddon. (2008), “Evaluating Systems Thinking in Housing”, Journal of the Operational Research Society, 59 (2), pp 186–197. Further reading: Visualizations That Really Work This free course was written by Paul Wally. It was published in June 2022. Except for third party materials and otherwise stated and referenced in the course (see terms and conditions), this content is made available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Licence. The material acknowledged below is Proprietary and used under licence (not subject to Creative Commons Licence). Grateful acknowledgement is made to the following sources for permission to reproduce material in this free course: Images Course image: Hirurg/Getty Images Plus Figure 1: Office for National Statistics (2021) Crime in England and Wales: year ending December 2020 Office for National Statistics. https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/bulletins/crimeinenglandandwales/yearendingdecember2020 Reproduced under the terms of the OGL, www.nationalarchives.gov.uk/doc/open-government-licence Figure 2: Robert Alexander/Getty Images Figure 3: aapsky/Getty Images Figure 4: clockwise from top left: rfranca/Shutterstock.com, Fagreia/Shutterstock.co, Joseph Sohm/Shutterstock, aShatilov/Shutterstock.com Figure 6: PA Figure 11: SOPA Images/Getty Images Every effort has been made to contact copyright owners. If any have been inadvertently overlooked, the publishers will be pleased to make the necessary arrangements at the first opportunity. Don’t miss out If reading this text has inspired you to learn more, you may be interested in joining the millions of people who discover our free learning resources and qualifications by visiting The Open University – www.open.edu/openlearn/free-courses.

Discussion 2022042900