Management Engineering - Operations Management

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Complete course

1 OPERATIONS SUMMARY 1- TRENDS IN OPERATIONS If you are operating at a strategic level of a company, you have to consider a long term point of view and the environment where you are playing. Environmental and social media in the European industries nowadays depend on several trends: DEMOGRAPHIC CHANGES – the number of people in the world is increasing, but not always in the same way. The European market is not growing numerically but maybe is changing the behaviour, because the average age of the people is changing and also the needs of the people are changing. Maybe Europe is not a potential future view for a company. GLOBALISATION & FUTURE MARKETS – there’s still the trend of people moving from the country to the big cities. If you are a company that operates in the country, you have to consider both the market which you are satisfying and where I can find more resources for the company . Eastern countries are speedy growing in the manufacturing industry, if we want to be competitive we have to keep operations inside our boundaries. SCARCITY OF RESOURCES – we have registered an increasing consume of resource, in particular difficulties on catching people, though the number of people in the world is increasing. CLIMATE CHANGE – the point of sustainability must be a driver in the setting of the strategy for our operations. DYNAMIC TECHNOLOGY INNOVATION – we can recover performances in our company, not only with a sustainable point of view, but also with a productivity one, that contributes to generate advantage in the market. If we want to be competitive, we have to invest on R&D. GLOBAL KNOWLEDGE SOCIETY – thanks to internet people are more aware and more conscious about the market so it is fundamental to take care of sustainability of the resources in terms of social and environmental impact. The operations trends are saying to us who own the operations will be the winner in the future. We can consider 3 main megatrends: 1. Sustainability : customers are always increasing the willing to pay a premium price to a product that is a famous as green. This trend started 10 years ago. Energy prices are not stable, so the less energy we consume the more we are stable. I must have a supply chain tha t is social friendly, where all company respect safety and human rights. 2. Mass customization : mass production means that we as a company must be able to produce large volumes but in this large volumes we cannot produce serial volumes. Nowadays customers pretend specific characteristics; the market of today is in the middle between low volume - high variety and high volume – low variety (mass production). Actually , we have to offer high volumes and high variety. This trend in many industries started many years ago and it consists of increasing the flexibility without losing the ca pability of production volumes . 3. Servitization : it is the oldest megatrends. Company started to enlarge their operation system and they offered not only products that they are selling, but also services in support of these products. 2 Three out of the four most important drivers of change in a company are related to operations: ▪ Talent driver innovation – setting operations in a proper way means to exploit the talents of the resources which are in your company; it means that your power of innovation is not just related to few people of the innovation but your power of innovation is strength in all the stakeholders of your company. ▪ Cost and availability of labour and materials ▪ Supplier networks All three of the drivers wrote before are not only drivers but also constraints. 2- OPERATIONS STRATEGY Strategy of operations should be always in line with the strategy of the company. We firstly need to define what level of the company’s organization we are considering: ➢ Corporate level → resources allocation between different markets and products. In which business do I want to compete? ➢ Business Unit level → what are the market’s needs and how can I satisfy them How do we compete? ➢ Functional level → support the company in satisfying market needs. How do we support the company in the implementation of the strategy? TRADITIONAL APPROACH – it is a top -down approach where BUs implement locally the strategy received by the top and then they execute it. Decisions are taken at a corporate level and operations are supposed to do whatever is decided at centre level. This approach doesn’t work in highly competitive environments , not stable and with lot of variance, where the speed of variation and change is very high. There are forces of changes. Relevant factors outside the company are: - Offer > Demand : companies invest a lot because they are pretty sure that the demand will increase, as they’re market share. The problem is that all the companies are thinking in the same way. - Customization - Globalization - Speed of technological development : today technology changes are faster than market changes and customers’ need. In fact, today we have more a technology pull approach rather than a market pull approach, because we should not wait for the market and do what customers want, but we should do the contrary. Factors influencing company’s resources are: - Economical : wealth increase - Cultural : educational level - Social : authority acknowledgement - Technological innovation - ICT These represent constraints for companies that are developing a strategy. 3 INNOVATIVE APPROACH – also called integration & bi -direction approach is based on interaction and mutual approval. VPs of main functions have to communicate and interact to define the strategy at a BU and corporate level. R&D, operations, marketing, etc. must not be independ ent, but they should communicate. The business model is how the company plans the strategy ; it is the business strategy. The operating model is how the company realizes the strategy; the functional strategies. Usually there’s a GAP between what we have decided and what at the end is implemented, this difference normally come from unplanned events, for these reasons, companies have to be flexible : o In the decision making process, like deciding not to do something already planned or to do something new not planned before. o They should detect what could be possible unplanned events in order to be able to react quickly and efficiently. Strategy is important to create a sustainable advantage. A deliberate strategy allows us to plan every little choice ; in a turbulent context it is not possible, we have to handle inconveniences, applying an emergency strategy through two different ways: - Deliver a resilient strategy: unforeseen event s do not affect the main strategy (to avoid them). - Being reactive: forecasting the future, trying to anticipate the unforeseen events. Performances affected by operations, which must be considered in defining a strategy, are: • TIME – how fast a company is . Among the time, we have: o Time to formulate the offer ; o Time to confirm the order ; o Time to deliver ( delivery speed ); o Delivery reliability ( timeliness ). The main differen ce stands in: ▪ Make to stock (MTS) ▪ Make to order (MTO) • PRICE – customers want to pay products the less possible. For the price, customers are becoming more and more smart, so it is even more difficult to cheat them. Price is made of: o Cost of purchase o Cost of usage o Cost of maintenance o Cost of update/upgrade/expansion o Cost of disposal • QUALITY o Quality of design (specifications) → represents company’s positioning on the market o Quality of conformance → how much the real performance is different from the designed one. 4 • FLEX IBILITY – it can affect product, customization, variety and plan . • SERVICE We cannot optimize all of these aspects; we have to focus just on few customer needs making a trade -off and, consequently, positioning on the market and analysing it. It is necessary to align operations and current market. How? The RECONCILIATION MODEL helps us to define the levers to achieve, finding a compromise with stakeholders. Among the strategic levers: 1. Structural design (Technological/plant design) o Overall production capacity o Strategic make or buy o Technologic process and equipment o Mechanization/automatization level o Type of plant design of the system o Supply chain configuration 2. Infrastructural design (Organizational design) o Competences needed and their management o Responsibility allocation o Team vs individuals o Managing by objectives or procedures o Functions integration o Incentive system o Information flows between 3. Delivery management o How to meet the demand o How to realize the product o Supply chain coordination system o Maintenance managing and realization system o Continuous improvement system o Which customer do you prefer to serve 3- HQ CASE 5 4- MODELS FOR OPERATIONS STRATEGY The performances can be classified according to the requests of the market segment we want to tackle: ➢ Order qualifier (Q) : it’s a feature of the product that customers and the market expect the product to have . If your product has a very good performance in term of the qualifier characteristic, it doesn’t increase sales, because it is not perceived as a differentiation feature. Q is necessary to be considered by customers. After a certain point, we don’t steal orders from our competitors. ➢ Order loser (QQ) : it is a feature which, if not owned by the product, can have a very negative impact. If someone is selling it with a very cheap price, nobody will buy it. Increasing too much the performance by reducing the cost could be counterproductive. This is an order loser. It is an on/off option for customers. ➢ Order winner (OW) : an order winner characteristic differentiates the product from competitors’ products. The company should advertise it. The more OW characteristics you have the higher is the number of orders you will get. 6 How to define them: - Set of representative customers - Set of significant orders (behaviour): when you start to quantify, they are always surprises. We have to see which performances are really valued by customers. - Interview: to get information we have to go on the field. - Ranking: rank orders and evolution in time tool. We have to use a scale; we can’t give the highest importance to all the performances. - Distribute 100 points. The evolution in time is a tool with all typical performances (we can also add others) evaluated: OW, Q, QQ. It’s necessary to keep updated the evolution in time: order qualifiers can evolve, due to a change of priorities ; curves can be non -linear, we can have performances with logarithmic or exponential slope. QQ and OW depend on the market perception . If the price is too high (higher than a reasonable value), you don’t sell it. If the price is too small, we have QQ, and it’s counterproductive. The map of performances is a tool used to visualize and understand how a company is performing against competitors , also during time. It is the comparison of the performances with market requests and competitors. Interventions priority are about: ➢ Importance ➢ Distance ➢ Competitors ➢ Difficulties 7 The lifecycle S -curve is a graph that describes a project . For each phase there is a pattern for what is OW or Q . In the introduction phase , we have as order winner the product/service characteristics , performances or novelty, because we go for the best quality specification . In the growth phase we have the availability of quality products/service s, because once the number of customers and competitors increase, the quality starts to have a higher impact. In the maturity phase. In the maturity phase , we have low price and dependable supply as order winner and range quality as qualifier , because we need a minimum threshold of quality, market and competitors are stable with price which is a winner. The role of operations can be seen through 4 different stages: ❖ Correct the worst problems; ❖ Adopt the best practice; ❖ Links strategy with operations; ❖ Give an operations advantage. At the beginning the role of operations is crucial while the role of the market is less important due to the fact that whatever the company is going to produce there will always be innovators and early adopters. Then the role of the market becomes more and more important because companies need to increase their sales and revenues to become profitable so operations cover only a supporting role. With the internally neutral , we have no advantage compared to competitors (operations has to follow). If we lack of this, we can not perform in the market. In the externally neutral we are working in order to get the best practice in the market . Operations are proactive. With the internally supportive , we are creating , and not copying, the best practice of the industry . We have to support the company in creating strategy to obtain success. With the externally supportive the operations are drivers for new markets . They are source of competitive advantage and innovation to change market needs and requests . For example, Bosch went from the internally supportive strategy aligned with the segment they tackled to an externally supportive strategy that opened new markets thanks to operations. We have different typologies of operations. We can see on the right h ow a company is positioned according to drivers of performances of operations: - Volume of the market; - Variety ; - Demand ; - Visibility (how our performances are visible to customers’ perception): it is the participation of customers in the process. The higher the visibility, the higher the participation. This is the 4 Vs analysis. We have also the example of the analysis for some retail banking processes. 8 The graph below shows the break -even curve. The curve is linked to the growth of the demand. Revenues is a linear curve . If the price is fixed, more output means more revenues, but more output needs an increment in capacity: costs do not have a linear trend . Variable costs increase linearly with output. They grow less than revenues (on the contrary we will never make money). Fixed costs increase according to the increase of capacity . Fixed cost break: capacity increment where we introduce more fixed cost. There is an area of volume where we suffer, so it is very dangerous and we should not stay there. Even though we have the break, sometimes the slope is not so high and we could end u p in this risk area. The break -even point is where the company starts making money . The curve in the graph represents the trade -off between variety and cost performances . This graph can be useful during the targeting phase: moving along the curve we can change our position in the same market , focusing on different segments. As the graph shows, if we incur in a gap that doesn’t allow us to reach the market, we are in trouble. 9 Another way to use the graph is to benchmark ourselves with competitors . In this case the curve is called efficient frontier and represents the best mix of the performances present in the market . To be more competitive we need to move the efficient frontier. We can do that following two phases: 1- Keeping the same variety and trying to reduce costs (horizontally) 2- Increasing flexibility and keeping the same costs (vertically). There’s also the sector curve trade off to compare ourselves with the sector and other companies of the sector; it is the sum of the single frontiers. To design it we need to interpolate the performances of all the companies of the sector. In this case, the optimal position to be is the intersection between the variety -cost trade off curve and the sector curve. In conclusion, we have the potential curve. It suggests us which improvements we must focus on in order to be more competitive in the market. 5- ONTARIO PACKAGING CASE 10 6- INTRODUCTION TO SERVICES AND SERVICE PROCESSES The service industry grew a lot recently due to: ➢ Social and demographic trend - Evolution of needs - Welfare: the global GDP is growing and people are becoming more and more rich, and the spend of the middle class is growing of environment. ➢ ICT , an important enabler for services delivering. ➢ Globalisation , it has a strong impact on transport, tourism and services. ➢ Outsourcing An important tool is the Maslow’s pyramid: it explains customers’ need. Our aim is to moving up to better meet the demand. To do that we need to integrate physical products with services. Self -actualization Esteem Love/belonging Safety Physiological Characteristics that services have that products don’t: ❖ INTANGIBILITY → it is much easier to copy a service than a product, in this sense we do not have a patent protection, for the same reason reputation has a fundamental role. ❖ CUSTOMER PARTICIPATION → a significant part of reputation for services is made of interactions with the customer, rather than the quality of the service. ❖ SIMULTANEITY → it is produced and consumed at the same time, we have just to follow the flow of the demand. ❖ PERISHABILITY → it cannot be stocked; necessity to match the supply with the demand. ❖ HETEROGENEITY → difficult to standardize, it is different for quality and customization. The service delivery process is a key element to reach a competitive advantage. Without it we risk that the final service seems a commodity to customers. The process, in fact has many differences to work on in order to be competitive in the market. The pro cess can be classified following three different ways according to three pillars: 1. Visibility: interaction with customers (Front Office vs Back Office) In the front office the delivery system is in touch with the customer so experience and customer management are fundamental. The outcome is relevant but the majority of times taken for granted. In the back office, the experience is less important due to fewer interactions. The outcome is essential because it is the only thing perceived by customers. Decoupling back -office activities allows to centralize them through digitalization in order to improve performances, maximizing its efficiency and reducing costs. This allows to leverage on learning and scale economies and resort to automatization. The centralized approach lead to an improve the performance overall system, but if we focus on 11 the performances of the single cases. It can imply a gap with the Front -Office and compromise communication. A lack of synchronization of the activity can lead to overlapping. Furthermore, in most cases it can increase lead times; it facilitates an activit y based approach instead of a product based one. 2. Volume to handle vs Variety offered As the graph shows it we can distinguish four clusters: o MASS SERVICES – high volume and low variety. The company gains money by increasing the productivity because the price is given by the market. For example post offices and fast foods, - High volume of transactions for each server/unit. - Short interaction with the customer (focus on back office). This leads to standardisation of processes and reduction of variability. - Attention to productivity and conformance. - Automation and informative systems. - Competences embedded in the system. - Process innovation: fundamental to increase the productivity. o MASS SERVICE SHOPS – low volume and high variety . Mass Service Shops are the development of Mass services towards a broader service offer. - Increasing of mix offered /trying not to lose the strict control over the process). Also seasonal products. - Increasing of the front office e discretion degree (trying to not worsen conformance). - The Front Office people need to develop the ability to understand customer’s needs (in order to offer the right service). 12 o PROFESSIONAL SERVICES – low volume and high variety. Reputation is very important here. Not standard or well -defined process. For example, doctors, consultants, personal advisors, lawyers. - Low volume of transactions for server/unit. - Not well defined/standard process (higher level of customization) - Longer interaction with the customer. This leads to know the information of the customer. - Attention to provide solutions. - Product innovation. - People’s competences and abilities are a critical asset (embedded in people) o PROFESSIONAL SERVICE SHOP . Professional service shops are the development of the Professional Services as dimension increases. Group of people delivering services. Any time the volume increases the shop becomes a large organization which starts to specialize themselves according t o particular services. When the knowledge in a particular field becomes very high, you cannot be expert in any topic. ENI for example made a platform where maintenance teams share their knowledge and solutions to problems, to make interventions faster. - Request of increasing efficiency without losing customization. - Request of knowledge sharing (embedded in the system) - Development of a house style - Decreasing of the discretion degree (the quality becomes a standard) - Creation of semi -professional roles - Development of roles dedicated to interactions with the customer. This differentiation into clusters does not well -represent the reality, since it is a continuum. This graph is used to: ▪ Verify inner coherence: coherence between levers. ▪ Verify external coherence: with the value offered on the market. ▪ Compare different units / businesses. ▪ Manage change. This method is used to evaluate if companies which deliver services know where they are positioned and where they want to be and if they are coherent in designing the product/service with their values and the values of the market. Could we have within the same company different solutions? YES. It could have different BUs, some of them concentrated in a type and others in others. For example, Lufthansa: Swiss and Brussels lines are own by Lufthansa and they are positioned lower than Lufthansa. We have to understand variety. To better understand the variety degree that the process needs to manage, it’s possible to divide service requests as: ➢ Runners : requests that always need the same operations / activities. Often foreseeable, and in remarkable volumes; automation opportunity and process review needed. (like MTS, make to stock) . ➢ Repeaters : requests that refer to known activities, but clustered in a different way. Not so much foreseeable and in medium/low volume. They are expected occurrences, but not frequent. They are activities that you have to do to satisfy the request are known, but you do not know the mix of request for which activities are different. (like ATO, assemble to order, 13 where you have all the modules ready and known in advance, but the combination is not known until the customer order arrives). ➢ Strangers : requests that need the design of new activities. Often little foreseeable. They are the exceptions: NOT expected occurrences. Companies have to design total new solutions. This difference is not about absolute volumes, but also standardization. Often there is a simultaneous factor presence (hijacking is the specific request). Looking at the graph on the right, where C is the customer, F the front office and B the back office, we can measure the customer involvement, i.e. how much the customer participates in the service delivery. In this sense we have two clusters: low (the customer just join the service like in luxury hotels) and high (when the customer makes the service, like in self services). Service projects are the case of market analysis, where we have different approaches to satisfy the customers. Service par tnerships ar e the case of consultancy companies, where we need specific information about the customer. The service factory is the case of McDonalds and Post -office, where we try to get to back office in order to get efficiency. In Do -It-Yourself the driver of success is the design of the integration of the three parts. 3. Variability (of demand and capacity) and uncertaint y Variability is the gap between the actual value and the average value (peak and minimum). Uncertainty is the gap between the actual value (real value experienced) and the expected value. We have to manage variability: when not explained it can cause uncertainty. There are two types of variability: - Determined by the company → we can control/remove it - Outer variability → we can limit the effect Usually 20% of the products/services generates 80% of variability, so we can isolate those outputs. The higher the variability, the lower is the efficiency of the command and control system because we have to make many decisions, and the chief becomes a bo ttleneck that doesn’t make the company work. When we have high variability it is better focusing on front office. 14 7- SERVICE CONCEPT The service concept is a framework that defines the how and the what of service design. It allows the alignment between customer needs and the organization strategic intent. We have to define the service concept and describe how it can be used to enhance a variety of service design processes. The service concept is made of: ➢ Organizing idea : the concept of the differentiation of our company in the market. ➢ Service provided : it is the group of resources, processes and procedure we swet in order to produce the service we proposed to the market. In it we include also the output of the process but not the outcome. ➢ Service received : the outcome is the difference between what the company produces and what the customer receives, the experience. The service concept helps defining the service value , i.e. how the customer evaluated the output. This evaluation is made of outcome and experience. Once identified the service concept we have to compare it with the operation service strategy, that has as objectives: maximize the benefits for the customers, minimize the costs for the customers and minimize organizational costs necessary for delivering the service. Another important point is the customer’s loyalty . Surprisingly, there is more attention on finding new customers than keeping those ones already engaged, as if customers were inalienable (customer retention vs. customer renewal). This could be a big mistake, because the cost of stealing a customer from competitors is much higher than retaining one. We have to consider the customers’ lifetime value, so how much value they could bring to us . The level of satisfaction is given by the difference between the perception and the expectation of the customer. We can measure this gap. We can manage expectations through Sales & Marketing functions. The perception and satisfaction of the customer are related to how we set the customer value. The objective is to avoid the existence of a gap between the customers’ expectations and the service and between the service and the customers’ perception. We have to continue to monitor the situation and the behaviour of the performances during time. So, we can decide to map all the interactions and processes using a tool similar to a control chart, in which we can highlight a tolerance zone and monitor customers’ satisfaction. 15 8- SHOULDICE CASE 9- STRATEGIC CAPACITY MANAGEMENT Characteristics of capacity strategies are: ▪ Timing of change ▪ Magnitude of change ▪ Attention to transient Key issue we have to take into account when considering a capacity increase are: o Lead time to complete the change o Flexibility to change : are we ready? o Economies of scale o Forecast of demand trend : there will be a decrease of the request? o Forecast uncertainty o Competitors behaviour : how will competitors react? Will this led my company to achieve more shares? o Requested service level/customers behaviour : maybe customer do not require an increasing service level. We have two main drivers: - Operations resources - Market requiremen ts We have also to take into account economies of scale and be careful not to overcome the nominal capacity limit or diseconomies of scale will kick in. where the costs increases, due to high complexity and high traffic between a large number of warehouses. A fter the nominal capacity limit, we are stressing our plant. Machine, people more than they were configurated for. So we have to consider risks : all values taken into account are mere estimations. Sometimes it is possible to have more detailed data but it is too expensive, so usually companies prefer to take risk. Through sensitive analysis we can consider all different possible scenarios to find what are the elements that impact our decisions. Always keep in mind that a certain degree of uncertainty is unavoidable. To be prepared for the transition phase it is essential to anticipate problems avoiding them trying to predict all possible scenarios. There are three strategies we can follow in order to align the demand : ➢ Lagging : it consists of running after the demand, underestimating it and keeping a lower capacity. 16 PROs High plant utilisation Low production cost CONs Longer response rate Lower delivery reliability ➢ Leading : the capacity anticipates the demand. We are always unsaturated (capacity > demand) so it is more expensive. PROs Faster response time Better delivery reliability Lower impact of uncertainty CONs Higher production unit cost Outbound cash flow: we anticipate money for future ➢ Smoothing : Using excess capacity of one period to produce inventory which can be used to supply the under -capacity period. It is a kind of intermediate solution used to anticipate the demand increasing the production capacity. It tries to exploit the benefits of lagging and leading. In some cases , it’s possible to use the plant for other productions (filling products ). When companies have extra capacity they can decide to produce products, which do not match their core competences, to avoid the waste of capacity. Using filling products is like modifying the demand. It means that: 17 - It’s possible to influence demand; - Not all the products are the same; - It’s useful to identify which product sacrifice in case capacity is not enough. The opposite case is when we choose to use an external supplier ( outsourcing ) to balance capacity and demand. The under capacity is covered by outsourcing to suppliers/third parties the missing production. It is convenient in terms of price and cost when the full variable cost of suppliers is lower than the variable cost to produce products internally . Let’s take into account the lifecycle of the product and how it impacts the choices and strategies of the company: 1. At the beginning of the life , when the market of the product is growing we are forecasting the future growth of the product. But if it doesn’t grow, the curve will go up and immediately fall down without reaching the maturity, going out of the market. Going for a lagging capacity cou ld be risky, because you are pushing the product creating expectations without selling it. It’s better, at the begging of the lifecycle of the product, to have more capacity, using a leading strategy , to be able to deliver the p roduct to the market. Sometimes, it’s better or it could be used to have no product available in the market as a market strategy. 2. When the product is growing we should reduce the impact of not delivering the product, so it’s better to have inventories, using the smoothing strategy . 3. When we reach the maturity , we could switch to a lagging strategy , because the forecast is more reliable and the situation is more stable. Capacity planning should not always be planned with a fixed/constant interval and the size of the capacity increment should be decided according to the forecast. There is a slow growth of the 18 demand at the beginning, where small increments take more time to be implemented, then the forecasted demand increases with a higher speed, so the capacity increments are higher and with less time needed to be implemented . Looking into the future is very risky and therefore it could happen to have many types of uncertainty. For a better planning of the situation we should create a range of the forecast. The range is bigger when the time goes by. Uncertainty can be represented on 2 dimensions: o Capacity increment uncertainty o Demand uncertainty The planned lead time (Y-X) is the lead time before the next increment. We can have a late increment, when we are late in increasing the capacity (latest finish time) or we could have an early increment, when we ask our suppliers to be faster so when we are early in increasing the capacity (earliest finish time). The possible scenarios that we are going to face are: a. Capacity on stream early, but demand on lower boundary of forecast → high extra capacity. b. Capacity on stream late and demand on upper boundary of forecast → lack of production. In order to size the increment we can follow two strategies: ➢ Small capacity increment → less risky. ➢ Large capacity increment → higher risk, it could not payback the incestment, due to the very high over capacity. Elements to consider to define the size of the increase: - Economies of scale - Demand uncertainty - Financing availability - Over/underutilisation costs and outsourcing possibility (filling products) 10 - COPING WITH VARIABILITY AND UNCERTAINTY The variability i ndicates how much the observation deviates from the mean ; while uncertainty refers to the difference between the expected value and the actual value. We can forecast variability but not the uncertainty. Variability can be reduced using three approaches: Decoupling capacity and demand downstream: Inventories – Decoupling the demand from the production; we can make a buffer that protect the production from the fluctuation of the demand. If we decide to go for inventories, we are deciding of taking certain risks and cost related to the inventories. Decoupling capacity and demand upstream: Pre Shop Pool – this tool works for certain service companies and for MTO and ETO manufacturing companies. It is located before the operation system (shop) and collects and stocks all the orders from the market in order to release them in a second moment according to certai n rules. Thanks to it is possible not to overcrowd the system, having less WIP. The entrance of the customers’ orders is very variable, for this reason the pre shop pool has both an upper and lower bound that alarm us. The biggest benefit of this tool is that it provides stability; on the other hand we have to take into account the trade off with the increasing throughput time of the orders. 19 System regulation (temporary changes) – we can adapt the service level to our operation system. Example: You have 6 days of throughput time and 14 days of orders (pool of orders). When a customer makes an order, you say you are going to satisfy the order in 20 days (given by 6days+14days). If you see that in your pool the number of orders is decreasi ng and you are stable to this value (5 days) and you do not have way to increase the number of orders sent; what you can do is to promise to your customer, you are going to deliver in 6days+5days=11da ys and not in 20 days. This can be a lever to get more orders from the market. 11 - MANAGING CAPACITY AND DEMAND CAPACITY MANAGEMENT Following there are some tools useful to align the capacity with the market and its fluctuations: ➢ Increase customer participation ➢ Capacity sharing ➢ Employees cross -training → flexibility of the resources in working in different departments of the company. The critical aspect of the lever of capacity management are: - Flexibility (cost and time) in moving the levers - Minimum size of change - Minimum time the change lasts - How much in advance the demand is known (how much time do I have to adapt to the demand) DEMAND MANAGEMENT The objective is to smooth the fluctuation of the demand: ➢ Reservation system : avoids uncertainty and variability ➢ Price incentives : in order to influence and adapt the demand to our system (ex: supermarkets incentives of 20% discount for over 65 on Wednesday) 20 MANAGING UNCERTAINTY We can use 3(+1) strategies: ➢ Additional decoupling between demand and capacity ➢ Capacity buffer ➢ Manage delays in delivery ➢ Attack the causes of uncertainty analyzing it and transform it into variability. 12 - YIELD MANAGEMENT The fundamental responsibility of operations management is to provide the capability to satisfy current and future demand, considering the trade -off between customer service and cost . Yield management is a statistical approach that exploits the information on customers’ behaviour that operations get while delivering the service. Its main objective is to maximize the capacity utilization rate in order to reach the closer to maximum profitability, offering prices differentiation to potential clients. When the service delivery is based on fixed costs, companies adopt yield management. The ideal characteristics for yield management are: - Fixed Capacity - Ability to Segment Markets - Limited capacity for different Market segments - Perishable Inventory - Product Booked/Sold in Advance - Uncertain Demand - Low Marginal Sales Cost and High fixed costs - High Capacity Change Cost There are different possible strategies for the delivery process: 1. Booking – Purchase ticket (payment) – Service delivery 2. Booking – Purchase of the ticket, service delivery 3. Booking, purchase of the ticket – Service delivery 4. Booking, purchase, service delivery. Yield management uses to tools: ➢ Capacity allocation problem (defining the share of capacity to dedicate to full price paying customers) - Price policies - Demand forecast - Protection policies ➢ Overbooking 21 Capacity allocation according to price policies (situation 3: booking and purchase + service delivery) Capacity allocation according to protection level sizing The protection level is the number of full price capacity units that I reserve for full price cutomers. How can I define the protection level? Through two different tools: 1. Marginal Analysis 2. Heuristic Expected Marginal Seat Revenue (EMSR) Marginal Analysis For the marginal analysis we consider three variables: a. Demand for full price b. Cost of underestimating the demand for full price unit c. Cost of overestimating the demand for full price unit With this approach we are going to balance the trade off between costs of underestimating and costs of overestimating → the protection level is set when underestimating costs = overestimating costs. We can describe the situation through these three points: o It is possible to classify clients into clients willing to pay full price and clients willing to pay discounted price (simplification: only two categories). o Potential clients come from different sale channels and therefore the revenues are different for the Hotel o Not all who have booked will actually show up 22 The decisions to make are: o Define how much capacity you want to protect o Define how much overbooking you want to have o Define a guideline to decide which requests you will accept and which one you will refuse . (the walking cost is when a client comes to the hotel and is rejected because the hotel is full). Underestimation cost – the place won’t be empty but occupied by a discounted price ticket instead of a full price one. Overestimating cost – is the cost for having reserved the room for full price customers instead of having sold them to a discounted price customer → I lose the discounted price. Some elements of segmentation to avoid cannibalization of rates, to differentiate the offer for customers: ➢ Restrictions on the purchase : discounted categories. - Delete option - Number of units: units that people can buy with one purchase. ➢ Purchase volume : buying in groups maybe there are discounts, while buying alone you could bear higher costs. Groups guarantee higher revenues for the service provider . ➢ Duration of use ➢ Customer value OVERBOOKING In order to exploit the no -show phenomenon we can apply overbooking . We can distinguish: ➢ Overbooking, due to the fact that not all bookings become a sale; ➢ Overbooking of sold tickets over capacity (Overselling), due to the fact that not all of the ones who bought the ticked then use the service. Overbooking can be applied at two decision points: ▪ Overbooking on the number of reservations (to avoid no transformation of booking) ▪ Overbooking on the number of sales (to avoid no -show phenomenon) There are two main methods according to which we can study overbooking: ❖ Static method - Analysis of the mean values - Brute force approach - Setting the service level (not YM) - Marginal analysis ❖ Dynamic method , in which we change over time the capacity to be allocated 23 Analysis of the mean values Brute force approach Fixed service level – This is usually exploited by luxury services, when companies cannot accept to go down a specific threshold of service level. So the aim is to set the level of service to be provided to customers putting a limit to the probability of “walking”. It is not a YM technique. Marginal approach (adapted to overbooking) The first step consists of computing the cost of underestimation, the cost of overestimation and the probability to have a n overestimation lower than no -show, so the probability to have another kind of idle capacity has to be higher than the cost of overe stimation on the sum of the 2 costs. The formula is different respect to the one of the Yield management, because in that case the cost of overestimation is the one that we relate to idle capacity . If we overestimate the no -show phenomenon, we will not hav e customers to fulfil the capacity, and it would be in contrast with one of the two aims of YM: maximize profits and fully exploit the capacity. We continue to accept reservations until the expected margin on the last booking accepted is greater than or equal to the expected loss on the last booking: the main approach is the Marginal approach, because it is the simple one, because it considers the costs and even we are again based on computations are based on data that usually company already got. 24 Uncertainty and variability have to be managed: overbooking and risk on the fixing of the protection level. Having information on the past history is essential to set a yield management system, as well as having good demand forecasts. The information system is the keystone for the success of this management approach. Abilities to segment effectively the different types of customer and to know in advance is crucial and the operations have to exploit them (e.g. Japanese customers usually do less no -show than Italian ones). It's important to pay a lot of attention to the choice of prices. Some elements of segmentation to avoid cannibalization of rates: ➢ Restrictions on the purchase - delete option and max n umber of units purchasable . ➢ Purchase volume - single purchase vs buying group (discount for groups) ➢ Duration of use – stay of 1 day vs weekly stay ➢ Customer "Value” An example of YM application is TicketOne: Ticketone is one of the most well -known company operating in the ticketing industry. Among other reasons, in order to avoid products’ cannibalization, it allows you to buy at maximum 6 tickets per time. Protection level is used for all cases when you want to protect some capacity for more profitable customers which will probably arrive later on. Other applications : ➢ Number of dresses of a new collection ➢ Number of newspapers to buy to resell ➢ Amount of bread to prepare In reality, overbooking costs are not so linear, given that there are some costs difficult to estimate. For example, for a luxury hotel having overbooked rooms is expensive given the walking costs consisting of image and reputational ones. The main difficulties a company has to sustain in the implementation of YM systems are: ❖ Customers’ reaction → customers do not accept it and try to fool the system ❖ Conflict of interests given contrasting objectives of different business areas and departments ❖ Cost/time estimation given that we cannot use historical data: customers do not always behave in the same way . 25 Classification The result is a proxy; it is not based on statistical approach. Main assumption: we do not want to cannibalize the different services. We have to define which is the Protection level for each class, like in the classical yield management. We have to create different classes in order to calculate the PL. We will carry out the marginal analysis among the different classes, with the aim of achieving the θi to compute the different protection levels. Assumption: we call a higher class the one that is associated with a higher margin. Heuristic EMSR (Expected Marginal Seat Revenue) a. Calculate protection Level for class 1, θ1 ( highest class) b. Calculate protection Level for class 1+2, θ2 ( highest class + next lower class) c. …calculate the protection level for class 1+2+3+..+n, θn .... d. The number of seats reserved to the lowest class (the cheapest) isn’t determined by protection level formula. It depends on the available capacity. Fix the level of protection for each of the classes / rates. Average revenue of i -rate and “more expensive than I” rates . 26 13 - QUEUEING THEORY A queueing is formed by one or more customers waiting for a service by one or more servers. The customers could be for example people or pieces, they’ll have different type of servers. Queues can be different from each other in terms of configurations. The queue going to form depends on the variability and uncertainty of the system. Average service rate should be higher than the average arrival rate to have structural balance and to be able to avoid the queue. If there’s not this balance queues happen: ➢ Structural imbalance: serves are not able to satisfy the arrival rate → utilization limit reached. ��������������������������� ������������������� [���������� ]< ������������������������������������� ������������������� [����������� ] ➢ Incidental imbalance: it depends on the context: - Variability → we should follow a sequence of the activities, overlapping cannot occur. Delta between the average value and actual value (di quanto la realtà si di scosta da quello che succede di solito). - Uncertainty → delta between expected value and actual value (di quanto sbagliamo la previsione). A queue is an alarm that something is wrong, we may have not considered all the variables properly. In the service systems, there are not stocks, but people, but we use the same considerations, so this can lead to wrong choices: “stock cover problem”. There is a clear relationship between them, so we look at the similarities between production and servic e systems: Queues are more critical for service companies given that pieces waiting do not create problems while customers waiting implies more problem related to psychological behaviours ; such as: ❖ Rejecting : when a customer or a product is rejected by the system because he/she/it doesn’t respect acceptance requirements . ❖ Balking : when a customer decides not to enter a queue because it’s already too long. ❖ Reneging : when a customer already in queue gives up the service and goes away without being served. ❖ Jockeying : when a customer tries to trick the rules to get advantage, e.g. shifting from one queue to another to shorten the waiting. 27 The queueing system is characterized by: CALLING POPULATION – it is the input source. It can be: - Finite : if the potential number of new customers for the system is significantly affected by the number of customers already in the system. - Infinite : if the number of clients in the system does not affect the demand pace for the service made by new customers. It is a potentially unlimited number, not in any case but it is probable . ARRIVAL PROCESS – it describes how customers show up. It’s described by the distribution of inter - arrival times, that is the time interval occurring between two consecutive arrivals. Generally, data are collected by collecting the actual arrivals time. Different empirical studies show that very often the distribution of inter - arrival is well described by a negative exponential distribution (as shown in the graph) . The negative exponential distribution has a continuous probability density function as: The cumulative probability function is: The cumulative probability function describes the probability that the time between 2 consecutive arrivals is t or less than t. The Poisson distribution is in a one -to-one relationship with the exponential distribution. When the inter -arrivals time are exponential, the number of events ������������ that takes place in a given time t is a Poisson process. 28 The Poisson distribution has a discrete probability function as: SERVICE PROCESS – it describes how each server delivers the service; it defines its duration. It’s defined in terms of service times distributions. Usually, data are given by collecting the actual service times. The service times distribution is often well represented by a negative expo nential distribution, thanks to historical data collected and demonstrations. Relevant parameters are: It’s the coefficient that describes the utilization degree of the system, where S represents the number of servers. Queueing theory is addressed because of: ➢ System sizing and designing ➢ Operative management : to evaluate costs and gains to improve the service in order to evaluate the feasible solutions and choose between different solutions . - Queue length - Average waiting time in line - Average waiting time in the system - System utilization rate 29 Goals to achieve while sizing a system: ➢ Service quality principles , measured through the average waiting time of the customers and maximum time that a customer is willing to wait. ➢ Cost principles , trade -off between the minimization of waiting time and service costs. We can see the waiting cost as an opportunity cost (plus the cost of the space of the waiting room). Each customer in line is a loss for companies, but at the same time it is impossible to avoid queues, so they can only diminish them as much as possible. The higher the variability, with the same level of saturation, the higher the length of the queue. QUEUE CONFIGURATION – we can have: ➢ Single queu e - come al check in dell’aeroporto , Pros: Assure a FCFS type of service Avoid concern that another queue could be faster Minimize the average waiting time Reneging actions are less frequent Cons: It could scare the customers ➢ Multiple queue Pros: Allow to diversify the service and the work Customer can choose the server Balking actions are less frequent Cons: With the same number of servers, the average waiting time is greater. 30 ➢ Take a number Pros: Assure a FCFS type of service Avoid anxiety that another queue could be faster Minimize the average waiting time Customers can do other things while waiting Cons: An absent -minded customer could risk to loose its turn It could "scare the customer" if the number is very high The choice of the configuration depend on the service you want to provide: ▪ High service customization degree → multiple queue ▪ Service time variability → single queue to standardize waiting times Considering queue capacity, we can also choose among: ➢ Limited queue : customers that arrive once the capacity is overfilled are rejected ➢ Unlimited capacity : there are no rejected customers. QUEUE DISCIPLINE Queue discipline (or “ranking rule”) is the rule or set of rules with whom it’s established in what order customers are served, it’s often strictly related to the queue configuration: according to changes of the system, the service changes. It can be: • Static : the pertaining order between the customers that are present doesn’t change in time and/or at the changing of the system conditions (FCFS, SPT, …); • Dynamic : the pertaining order between the customers can change in time and/or at the changing of the system conditions. We can have some priorities : some customers could be more important for some reasons, so we can assign some priorities to them and not to other customers in the same line In order to manage queues we can leverage on the demand side, the offer side and psychology of waiting. As concern offer side the possible levers are: ➢ Configuration changes; ➢ Add/Remove/Move resources (saturation of resources); ➢ Decrease service time: technology utilization; ra tining (of operators); ➢ Increase resources flexibility: through training operators. ➢ Decrease service times variability: more related to technical problems . In the demand side the best solution is booking that allows us to schedule and be sure about the arrival process; this helps optimizing profit and reducing demand variability, acting on efficiency. Regarding psychology of waiting, there are different ‘service laws’: 31 1. Satisfaction is given by the difference between perception and expectation. Perception is even more important than the reality. Our aim is to get nearer as possible to the expectations of customers , in order to obtain a high satisfaction degree among customers. 2. First impression : queues is the first thing customers look at while entering in a shop or in a building. 3. Prospect theory : my utility increases as far as my perception of gaining something increases In order to avoid customers disappointment: ➢ The unoccupied time seems longer than occupied time : distract and entertain with small related or unrelated activities. ➢ Pre -process wait seems longer than in -process wait : take care of the customer as soon as possible to make him/her “in -process”. ➢ Anxiety makes the wait to look longer : communicate and update frequently the waiting time to the customer. ➢ An unfair wait seems longer than a fair one. ➢ The more the service is of value or the more the customer senses its utility , the more he/she is willing to wait. ➢ Waiting alone seems longer than waiting in a group . ➢ A customer exposed to an exaggerated wait will be a more difficult one to serve or an ex - customer Queue system analysis tools: Deterministic analysis Pros : Simple and intuitive Cons Transitory and arrivals/service times not into account (stationary system) Queue as discrete and not continue curve. Queueing theory – based on statistics. Rough but fair approximation with an average error between 5 and 20% according to real results , so it’s acceptable. Pros : Inter -arrivals and service times variability into account Allows to calculate a set of significant variables Cons : Stationary system Complex Non Poissonian or Gaussian times Non exponential service times Cannot measure customers behaviour Simulation – much more precise. Implemented by expensive software such as Arena, used for design and modelling phase. Pros : Transitory into account Very flexible Cons : Time consuming Skills needed 32 Assumption needed by queueing theory: ➢ Stationary process : it is a very restrictive one given that in reality it is not a static process. A solution to limit this problem can be making several analysis considering many time slots. ➢ Peculiar customers behaviours are not expected ➢ True only for certain inter -arrivals and service times distributions: negative exponential distribution is assumed for service and arrival rate . The queueing theory requires the knowledge of Kendall’s codification : - A identifies customers' arrival distribution; - B identifies service times distribution; - c identifies number of servers (if we have c it means that there can be any number of servers); - K identifies queue capacity (buffer by default: endless); - m identifies population dimension (by default: endless); - Z identifies service discipline (by default: FIFO), Usually stop at the first three components (ex. M/G/1), in detail: - M suggests a negative exponential distribution (Markovian); - G suggests a general (i.e. arbitrary) distribution. The most common models are: o Standard M/M/1 : the population is endless or very big. The arrival process is characterized by interarrival times described by a negative exponential. According to queue configuration, we have single queue without capacity restrictions and balking or reneging effects. Q ueue discipline is FCFS. For the service process, we have 1 server with a negative exponential service times distribution. o Finite -queue M/M/1 (M/M/1/K): this kind of queue formally is the same as M/M/1, but in this case the queue has a finite dimension, so we use M/M/K. We have which is the probability to not enter in the system; which is the expected number of lost customers. This model is par ticularly useful in estimating lost sales due to an inadequate waiting area or to an excessive long queue. o Standard M/M/c ; o Finite queue M/M/c (M/M/c/K); o General Self Service M/G/∞ : It's a system with an endless number of servers or a system where customers serve themselves (ex. supermarkets). The total number of customers in the process varies due to arrival and service times variability. This model is also useful to shape, rounding situations where you rarely have to wait (ex. Emergency services) . Observations: ▪ A complex system is the composition of more elemental sub -systems that interact among themselves (mapping the system and identifying every step of the process). ▪ Very often in a complex system transit different type of customers ▪ The system expected throughput time is a representation of an "average" customer and not of the specific one! ▪ The throughput time varies according to the variation of the work load in the system and to the variation of the input. 33 Nodes balancing to flows calculation While designing a queue model we have to take into account that input is equal to output. Nothing is created or destroyed. The total sum of the different flows (inbound and outbound), for a given node must be 0 , and so for the whole system. 14 - LEAN PRINCIPLES There are two ways to maximize profit: ➢ Increasing sales → if the market is not growing due to its maturity there’s a high competition , if it’s growing it’s easier. It depends on the context, we cannot completely control it, ➢ Reducing cost