Lezioni "Strategic Supply Chain Management" -- Prof. V.Belvedere; H. Kotzab -- entrambi i moduli -- Unicatt (Mi) -- 73 pag, Appunti di Logistica

Scarica Lezioni "Strategic Supply Chain Management" -- Prof. V.Belvedere; H. Kotzab -- entrambi i moduli -- Unicatt (Mi) -- 73 pag e più Appunti in PDF di Logistica solo su Docsity! Strategic Supply Chain Prof. Valeria Belvedere; Prof. Herbert Kotzab From 09/01/2020 to 05/03/2020 - two last lessons are webinars (Covid reasons) 70% final written exam + 30% group assignment Exam (just once): 16 March or 31 March  moved to summer session 1- SEW Eurodrive Site Visit (January 23, morning); 2- Visit to FARVIMA (January 30, morning) 1 MODULE - PROF. V. BELVEDERE Lesson 1 (09/01/2020) Definition of Supply Chain. A supply chain consists of all parties involved, directly or indirectly, in fulfilling a customer request (Chopra and Meindl, 2016) They can be: – Suppliers – Manufacturers – Retailers – Logistic service providers – The customer Maybe you can have also secondary and tertiary suppliers and so on. There are many levels. They interact exchanging information and materials with each other in order get what they want (raw materials, semi-finished products, end products), with the quantity and timing desired The process starts from the retailer that places an order to the manufacturer (red line, first flow of information), if the manufacturer doesn’t have the product available for delivery, he will ask (second flow of information) to his supplier what is needed for the production. From this point the flow, this time of physical materials and products, goes back, form the supplier to the manufacturer and then to the retailer. Example: supplier of packaging, manufacturer as producer of pasta (Barilla), retailer as supermarket chain. 1 An actual example of how a supply chain works: Procter&Gamble and Walmart. The left side is what happens upstream, while the right one shows the downstream level. Pactiv Corporation  bottle of pharma as primary packaging Paper Manufacturer  as supplier for secondary packaging Timber Supplier  provides raw materials necessary Moreover there are other suppliers such as Chemical Manufacturer and Plastic Producer. What happens inside a company? Inside a company, several activities are carried out to manage supply chain processes, as: – Vendor rating (assessment of potential suppliers rating and ranking) and selection – Transportation of materials from vendor to the producer – Quality control of incoming goods (to accept or reject goods coming from suppliers according to a quality protocol. Acceptable quality level is the threshold under which you can accept all the transportation and even the scraps, while going over this level you will reject all the shipment) – Materials handling in the warehouse – Capacity requirement planning (how much should I produce? Look to sales forecasts. If I cannot produce the required amount, I will look for subcontractors…) – Managing subcontractors (other companies working for us as solution to be managed for overcoming capacity problems) – Production planning (more detailed process) – Inventory management (necessary for ‘made to stock’ company that based their production on a forecast) – Distribution planning Departments directly involved in SCM processes: – Procurement – Production – Logistics Departments indirectly involved in SCM processes: – Accounting & Finance – Marketing & Sales, nearer to the market, making forecasts and conflictual with production – Design/R&D/New Product Development, dealing with the ideation of something new that every year causes problems and slowdowns 2 Depending on your product type you will focus on different sides, such as efficiency or flexibility… - In North-America, Europe and Asia-Pacific, logistics expenditure accounts for 11% of the GDP. In Latin America it is 14% (Capgemini Consulting, 2012) - Until the ‘80s, this value was nearly 20% even in more advanced Countries - In Italy it is nearly 11% - The lowest values (lower than 10%) are reported by highly developed Countries (e.g. Germany, Japan, France, UK, U.S.), where the awareness about the relevance of logistics is very high since long - Countries as India and China report much higher costs (17% and 21% of the GDP) Some Industry data: Total logistic cost: breakdown Service level Speed 5 OD stands for ‘Order Date’. DD stands for ‘Due Date’, date in which the order must be done. ADD stands for ‘Actual Deliver Date’. Maybe supplier needs another actual deliver date, that is the second ADD (ADD2). If there are ADD1 and ADD2, we always consider the ADD1 for calculating the speed. ‘Delivery Time’ known also as ‘Time to Order’ is the difference between OD and DD (or ADD1 if there is). There are two indicators measuring the speed: 1- simple average of all delivery times of the year (or any other horizon time); 2- the percentage of orders fulfilled for a maximum delivery time, i.e. on time (established by the customer). EXAMPLE: In the year 2019, there were 3 orders. Maybe they say that maximum delivery time can be 12 days, meaning order no more than in 12 days : Order 1- 5 days  speed is fine Order 2- 10 days  speed is fine Order 3- 15 days  it’s not fine Speed1= 5+10+15 3 =10days Speed2= 2 3 =66% (just two order are fine on 3) Dependability EXAMPLE: In the year 2019, three orders with delay of: 1- 3 days  delay 6 2- 0 days  in time 3- 6 days  delay Dep1= 3+0+6 3 =3days Dep2= 2 3 =66%(2 delays) Maybe there can be an advance. How to deal with this advantage making the framework complex? In the second order there is an advance in the delivery. If I count advance as something good, obviously, so as a negative delay, I compute 5−5 2 =0. However, this result is not reliable as measurement of delays! So, which is the solution? The solution is given by 2 approaches: - to consider the total mismatch between the ADD and the DD generally, if advance is a problem, - or considering the advance as if it’s not a problem for my storage. Recap: - advance = normal delay - advance = 0 Completeness 7 On Time in Full (OTIF) - OTIF 1: we consider just orders ON TIME AND IN FULL/COMPLETE  values can be 1 (if the order is fulfilled completely and in time) or 0 (if the order is not complete or not in time) - OTIF 2: we consider orders ON TIME AND IN PERCENTUAL OF COMPLETENESS  consider the completeness in percentage but if the delivery is not on time the value is 0 - OTIF 3: we consider orders LATE WITHIN A RANGE AND IN PERCENTUAL OF COMPLETENESS  the same of OTIF2 but it takes into account a range of +/- some days on the delivery time, being more flexible. Flexibility Flexibility can be defined as the ability of a system to change itself, but under two conditions: – speed of change – cost of change A system is flexible if it can change quickly and with a limited cost Lesson 2 (13/01/2020) CLOTHES&MORE- CASE STUDY 10 case_CLOTHES.pdf cloths_and_more_sol ution.pdf There’s a matrix useful to understand which indicator is most suitable for every scenario: - Fashion Houses and Planned orders: OTIF - Retailers and Planned orders: OTIF - Fashion Houses and Replenishment orders: - Retailers and Replenishment orders: 1. Speed (before); 2. Completeness (after) Retailers can become disappointed clients since they receive more items after September 1st than Fashion Houses. FOR RETAILERS. Speed is perfect since they all respect the time maximum delay of 5 days. However, Completeness is not so good. It’s so and so. Just 66% in terms of orders, 71% in terms of pieces. Why is it too low? Since here we don’t have pieces in the warehouse. Just item C is not available on 4: 25% of stock- out measure. Remember always to properly select right indicators. Now let’s see the design of a performance measurement system (PMS). A strategic alignment approach must be adopted. You need to understand your competitive strategy as first. Then, you need a functional strategy (the performance that should be produced…), understanding most of priorities. Then, you can design your PMS (performance measurement system). Now, you can make an improvement plan. There’s must be a consistency among all these actions. Sometimes production managers fix objectives totally far way from the targets of the market, totally being unaligned with the competitive strategy. It’s important the process of alignment, since this mismatch is really frequent. Skip the next slide about balanced scorecard. Lesson 3 (16/01) - How can we define the Production System? It is a system that transforms inputs into outputs of greater value to the customer - What is Operations Management about? It is about design, management and improvement of the production system The Output of a production system: If the demand is not linear (e.g. the same each month)… - the production system for products can be organized in a way that the production capacity is fully exploited anyway, by producing the full capacity and stocking product that will be sold in the following 11 period. - on the contrary, for services this cannot be done, so, a must have for production system of services is to be flexible in input and output employed and so in the whole supply chain. The range from services to products - Technological innovation is enabling the process of servitization, which consists of enriching the product offering with value added services (maintenance of capital equipment, financial services, rental, etc.) - These systems are also called Product-Service Systems - In some cases, the offering of a physical product can even be replaced by the offering of a service (from selling the ownership of the product – e.g. a car – to selling its availability through a leasing solution) Production system design: the levers Hardware Choices: – Location – Vertical integration / outsourcing – Automation / technology – Production capacity – …. Software Choices: – Production planning & control – Stock management – Quality management – …. How should we take decisions about these levers? Production system design: The Resource Based View  Production system design: the focused approach or strategic alignment approach 12 - Speed - Dependability Speed is the ability of a company to deliver an order as soon as possible. It can be measured through the Mean Delivery Time. Dependability is the ability of a company to deliver an order on time (in the due date). It can be measured through the Mean Delay in Delivery. An example: - Was the delivery fast? No - Was the delivery dependable? No WHY? Operations department can be often responsible for both poor performances. It happens because it is not easy to accurately forecast how long it will take to get a unit of product done (“manufacturing lead time”) and to reduce it. Manufacturing Lead time is the time that passes since the system is fed with the necessary inputs till the finished product comes out of the production line. The processing capacity of the production system as a whole depends on the processing capacity of the slowest operation, which acts as a constraint  Bottle- Necks Cycle time can be computed as follows: 15 3. FLEXIBILITY Flexibility can be defined as the ability of a system to change itself, but under two conditions: - speed of change - cost of change A system is flexible if it can change quickly and with a limited cost. Flexibility can be assessed from different perspectives: Lesson 4 (20/01) Cost. Being efficient is important in manufacturing plants. A major part of the total cost of a unit of product depends on the value and the quantity of the inputs employed in the production process. Also Energy could be another very important input. However, the most important are these ones. For each one, there’s a cost correlated. Bought materials sometimes are costly (think to jewellery). The more efficient the use of the production inputs, the lower the cost. Zara doesn’t compete on cost, but on speed, but it has to consider this aspect too in its production process: that’s why is so important! In order to understand the extent at which Operations contribute to cost performance, productivity is measured. 16 Productivity is obtained comparing the amount of products manufactured and the amount of input employed. Example. In 2019 there were: - V= 1000 pieces - 1 machine - 2 workers - 1 toner of plastic My productivity is P= 1000 pieces 1machine+2workers+1toner , but can be focused just on capital, manpower or materials, if I want. Which one to use? It depends. If you are capital intensive, you need to focus on machines and probably you need a capital productivity measure (production of a nylon, textile). Other times you have labour intensive processes, where most important input is manpower (assembly lines). Other times again, you need to focus on bought materials, when they are very expensive (jewellery, or managing gold). Example of before. Now we focus on capital. - V= 1000 pieces - 1 machine - 2 workers  - 1 toner of plastic P = 1000 pieces 1machine … is it good? We don’t know because we haven’t any benchmark. You need for example anther company measurement. However, we can solve the problem by ourselves, if we express the measurement in percentage. The only way to express the numbers in percentage is to express both measures in terms of the same value. Maybe “economic value” could be a good way. However, it is not so effective. Express in terms of times used (?). However, can we say that the increase/ decrease of the percentage depends just on machine capability? No, since there could be other important factors, such as reduction of prices and so on. 17 How to improve so Utilization? I need to know for each time my losses. Then I can make priorities. The efficiency index (η) is expressed by the ratio between the actual volume produced, measured in standard times, and the actual operating time spent for producing it: Remember that: utilization percentage can never be more than 100%, while efficiency yes. EXERCISE Available Production Capacity (APC) in the future depends on: – the productivity of the plant, assuming that it is going to be stable over time – the plant calendar time in the next period (generally one year) – the unit standard cycle time APC (pieces) = [Plant Calendar Time x U x η] / std. cycle time Exercise.pdf 20 Stop at the slide 32. ASSIGNMENT NOTES. Building on the strategic alignment approach, provide an assessment on the level of alignment between the Operations’ strategy and the Competitive strategy at SEW Eurodrive. Then, assess whether and to what extent Industry 4. technologies implemented in the factory can actually support the Operations strategy, allowing an overall consistency among competitive priorities, functional priorities and the production system design Find out if competitive strategies are consistent with functional strategy and production system design. If what on which the company based its success (for example quality) is consistent with goal, routinely activities and deficiencies. The assignment must consist of a .ppt presentation of no more than 20 slides The assignment must be uploaded on Blackboard The deadline is January 31st at 12:00 OVERVIEW OF LEAN MANAGEMENT Lean Management: a set of principles and tools aimed at eliminating/reducing wastes There are several types of wastes: – Overproduction. Let’s assume you have a plastic toy (small soldier) as product and you have to moil it to have the final output. The batch size is 1000 pieces, but actually the demand is 50 pieces. It happens when you have some technical constraints that make you produce over the actual demand. This is not good. It’s a waste! – Transportation. There could be some activities not in your plant, but in other ones. During transformation, product doesn’t change, but it is a waste of time – Motion waste. It happens in factories. My workers are moving too much, doing activities not necessary. – Inventory. When I work with a make to stock approach, based on forecasts. It happens that products needed are in the stock, but we have product that we don’t need – Waiting time. When we have machines, that don’t work for majority of time or I have semi-finished product waiting for days to be finished near to machines. Product is waiting too much! – Processing waste. This happens when I carry out some activities not requested by client. Think of phones. I don’t use all the functionalities. Also, for Microsoft Excel. – Defects. If you produce a scrap this is a waste. Lean Toolkit. - Pull process and set-up reduction 21 - Value Stream Mapping - 5S - Layout redesign and balancing - Total Productive Maintenance and OEE - Continuous Improvement ▪ Why is it hard to reduce WIP? – Batch sizes! If I have 1000 pieces as in the example that are mould and then wait to be painted, I waste a lot of time ▪ Why is it hard to reduce Batch Size? – Set ups! ▪ SMED (Single Minute exchange of Die) techniques are used to reduce set-up time. Dream of Taichino ▪ Why is it good to reduce Manufacturing Lead Time? – In order to adopt a Make to Order production (MTO) rather than a Make to Stock one (MTS). Most of the times clients are not willing to wait so long for an order. ▪ MTO = Pull production system ▪ MTS = Push production system ▪ A Pull production system (make to order) eliminates all inventories/ stocks and related costs ▪ Furthermore, a short Manufacturing Lead Time enables a quicker feedback about quality problems and leads toward a scrap rate reduction Value Stream Mapping ▪ A Value Stream Map is a graphical representation of the activities carried out to produce an item (or deliver a service), aiming at differentiating the activities. ▪ Activities can be classified as follows: – Value added activities (VA), for which clients are willing to pay, as transformation – Not value added (NVA), for which clients are NOT willing to pay as waiting, transportation, quality control etc. ▪ The Value Stream Map of the “as is” situation is a starting point to highlight inefficiencies and start addressing them 22 Usually where are they located? In cheaper and huge areas. We need a lot of space at very economic price. And they are maybe next to airports or other transportation centres. Usually fiscal incentives are a good motivator! For example, Gucci has a main warehouse in Switzerland, that is not cheap at all and it’s locked among the Alps! Let’s make now an example. Case of Coca Cola. All over the world its production is based on 2 stages: 1- there’s the production of concentrate syrup (usually in US) in manufactory plants owned by the Company (that keeps the secret of the recipe), and then product is carried to other companies 2- others plants dilute syrup into water (usually they’re next to springs), getting Cola we know, making the product ready to be organized in physical distribution (in Italy there’s a plant like that in Verona, Veneto). We have a lot of clients for the company: bars, restaurants, supermarket, and so on. So, it does not make any sense the long road transportation through across Italy for example, from Veneto. However, you still have to make the delivery! How they do? They have a central warehouse, having also some local distribution centres. It happens that it is organised the delivery from the central to the local centres and then the physical transportation could be more easily organized. It is more efficient than one-show transportation because of the quantity that you transport. Do they have majority of the stocks in central or local distribution centres? It can be both. Locally, since it is next to client, it gives more speed and facility to the transportations. Stock needs to be next to clients if there’s a lot of products, for example. However, there are places in which times are reduced and they can easily be more present in central warehouses. Local distributed centres become a sort of transit points. This is actually the real case of Coca Cola in Italy. On weekly base, they receive orders from a determinate region and when all orders are collected, they organized a delivery to the local distribution centre. There, the delivery to single clients will be organized as well. Speaking in terms of speed, it is quite low effectiveness. However, the advantage is that it’s more suitable to cope with the variability of the demand. Layout: organise the place inside. Example on the blackboard. Equipment: the technical machines. Some are labour intensive factories. Others have a lot of technologies. Amazon Case. You deal with a very huge diversity of items. Picking is manual activity in Sesto San Giovanni warehouse of Amazon. You can have robots that deal with picking activities, but just in a standardized way. If items are different in terms of shape, measures and place on the shelf, picking is more convenient to be manual. Barilla Video. One of the biggest warehouses in Europe. No humans. All automatize. Products are very similar. Pallets are carried and picked easily by robots. 25 Receiving and shipping are put at the same level, since they are very similar-related activities. Receiving. Receives items. Storage. Decide where to place them. At which level of shelf. Order Picking. You receive an order. So, one guy will take the item. Or a robot. You can have manual or automated picking. Or both. Amazon has 3 warehouses in Italy (Castel San Giovanni- manual, Rieti- automated, Vercelli) Shipping. Now item will be placed into a box and then will be shipped. Main design decisions: - truck dispatch schedule: who is going to leave as first? - Assignment of SKUs to different department schedule. SKU  stock keeping unit. Identifies the code for models of products sold, giving the chance to control fast as possible if selected items are available. So, it’s the code, the number identifying a product kept in the storage. - Assignment of pickers to zone. Maybe on the same shelf there could be totally different products. This is the case of Amazon. Why? It’s not such as supermarkets where you find similar items on the same place. If you are bigger as Amazon, you maybe are tired as worker. Having shampoo of different brands next each other, they can make mistakes. So, this is to avoid that kind of mistakes. - Batching. Receiving several orders, you can organize batch of orders, collecting all items in trucks to make less transportations - Picking routing and sequencing: path you need to follow. Amazon Warehouse is a huge place: 100000 m2! When they receive the picking list, you can see directly the sequence of items, made in an automized way to optimize transports (AGV). Amazon Video. Small red robots (AGV) able to rotate. Shelves next to each other, since there are not humans carrying materials. Save space! Warehouse performance. From an operational viewpoint, the design and management choices concerning warehousing infrastructures result in remarkable effects on the performance attributes typical of such facilities: – productivity, which measures the level of asset utilisation and efficiency; – quality, thought of as the level of accuracy in order picking and shipping; – cycle time, which refers to the responsiveness of warehousing processes. 26 Industry 4.0 technologies as a booster of manufacturing and logistic performance 1. Autonomous Robots. From 70ies they become very popular to be used. They were years of conflicts between manpower and employers, of complaints and strikes. The answer in many countries was to start to think how to replace manpower. And the solution was by replacing manpower with automation. Dreams about automation. However, they were not flexible enough. Let’s consider the AGV of SEW. They were able to perform many tasks at the same time, knowing the map of the plant and to go. In past they were less flexible, following lines on floors. During 80ies companies went back to some forms of labor activities. However, now they have been evolved to reach the form we know today. Now, they are robots able to cooperate with each other and also with workforce. Instead, traditional automated machines cannot perform activities in parallel with the workers mostly for safety reasons. So, what is new for autonomous robots is, first, they can cooperate with people, working together. Besides, they are smarter, for example knowing if there’s something/one next to them stop moving and learning over time. Remember COMAU and SEW experiences. So, they are able to adapt to the variability of the environment (e.g., they can change behaviour on the basis of the input material). Moreover, through machine learning, a neural network is developed that, on the basis of the available data, lets the machine understand how to behave. 2. Adding Manufacturing. It’s basically 3D printing. - Since the ‘80s 3D printers have been used mostly for rapid prototyping - Now 3D printers are less expensive, speedier and more precise. They can be fed with much more 27 – “Software as a service” (SaaS): the client can use provider’s applications on the cloud infrastructure and applications can be accessed from different “clients” generally through a web interface – “Platform as a Service” (PaaS): the client does not have a control on the cloud infrastructure, but it can configure its applications – “Infrastructure as a Service”: the client has a direct control on the operative systems and on the data storage IoT and cloud can create risks of cyber-attacks. 8. Big Data and Analytics - Big data analytics enable the interpretation of the data collected in smart factories - These analyses highlight trends and patterns that enable a better understanding of the production system - An example is “predictive maintenance” - There are three different approaches: – Descriptive analytics: it is used to understand what just happened and what is about to happen, e.g. through the description of trends and patterns 8as in the case of control charts) – Predictive analytics: it uses tools as regressions and neural networks to foresee future trends and behaviours of the system – Prescriptive analytics: it is based on optimization systems, which suggest to the decision makers how to behave on the basis of alternative scenarios, given some future trends and patterns expected Lecture 6 (13/02) NO EXERCISE TEXT AS REFERENCE Inventory Management Simulation . First decision is when to buy. Second is how much (how many pieces) to buy. We should take the economic outcomes of these decisions. There’ s a transportation cost, first of all, costing 25€. Then there’s holding cost (9%). Let’s say you bought 1 piece of A. This one will stay in stock for one period (1/1-31/12: 1 year). You have to pay an interest of 9% at the end of the year. 9% of the unit value. Delivery time means if you are placed an order on Monday, your product will arrive in tot working days. Time series shows us the demand for code A and B. Month is February. Simulation Sheet. Delivery is empty since we have not received any pieces. The computation is +delivery time and -consumption. The order is the most important column, where you have to write how many pieces you need. If you place an order of 10 pieces of code A and 15 ones for the B, then the system on Excel will compute anything automatically. RESULTS. You are in a situation of uncertainty. About the demand, since you don’t know about your customers and there is certainty about supply. There was only just 1 clear info: 6 days as delivery days. You have no long history and then some suppliers can be late. So you should increase your stock levels, to avoid stock-out and since you don’t know what you will deal with. However, increasing stock levels, you will have an increase in the holding costs. It’s a complex situation. How can we find a balance? You need to establish some collaborative practices among customers and suppliers. 30 ❑ Vendor Managed Inventory ❑ Consignment Stock ❑ Continuous Replenishment Programs ❑ Collaborative Planning ❑ Collaborative Forecasting ❑ Collaborative Planning, Forecasting and Replenishment VENDOR MANAGED INVENTORY According to this practice, the «vendor» (i.e. supplier) manages the stock on behalf of its customer. In order to do so, the customer must provide the supplier with some pieces of information: – Current inventory level – Expected consumption of the product in the near future (expected demand) Furthermore, they have to agree on a contractual basis the following: – Maximum & Minimum amount of stock at the customer’s site – Service level to the customer – Frequency of update of the information It’s important to set the minimum to avoid run out of stocks for client. And also a maximum to avoid the supplier behave by following just his interest. The benefits for the customer are: – Outsourcing of an activity that requires resources – Higher service level provided by the supplier – Lower holding costs, due to the fact that the average inventory level is lower with VMI The benefits for the supplier are: – Easier and more effective production planning process (by knowing, forecasted plans are removed) – Lower stock-out costs – Lower holding costs, due to the fact safety stock to cope with demand uncertainty is not required anymore (or is much lower) Remember that high uncertainty means to have a very huge stock to avoid problems, but on the contrary, if we have certainty, we should reduce the amount of stocks, since they cost. CONSIGNMENT STOCK This practice is the same as VMI. The only difference refers to the fact that the ownership of the products goes from the supplier to the customer only when the customer picks the product from the warehouse. In this case, the product is owned by the supplier even if it is in the customer’s warehouse, until the customer needs to use it. The customer can postpone to the latest possible time the payment due to the supplier. The benefit for the customer is generally counterbalanced with better financial conditions for the supplier (earlier payment conditions etc.).  it works generally if customers have high bargaining powers. Furthermore, the risk due to any accident in the warehouse is taken by the customer and not by the supplier  a way to counterbalance the negative financial effects. CONTINUOS REPLENISHMENT PROGRAM It is similar to VMI. But in this case the supplier proposes a replenishment to the client, which can be done only after it has been authorized. This practice is very common between FMCG manufacturers and big supermarket chains or department stores. They organize different replenishments among all suppliers. Distributor: supermarket; 31 manufacturer: Barilla. COLLABORATIVE PLANNING This is about production planning. Not inventory management. According to this practice, production plans are shared among the main players of the pipeline (manufacturer, suppliers, subcontractors). In case one of them has to change some decisions concerning future requirements or production quantities, this variation is communicated to the partners so as to check whether they can support it. COLLABORATIVE FORECASTING It aims at leveraging the knowledge about the market demand present in different layers of the supply chain in order to produce the best possible demand forecast. It aims at producing one single demand forecast (“single number forecast”) for the entire supply chain. The manufacturer can be the leader of the forecasting process, in a contra-intuitive way, since we can think that Esselunga should be that one, being nearer to the market. However, Barilla operates worldwide, so they have so many much data. COLLABORATIVE PLANNING, FORECASTING AND REPLENISHMENT (CPFR) It is a form of collaboration among two or more operators of the supply chain, which jointly carry out and plan the following activities: – Promotions this is a new point. Let’s say you are Esselunga and you buy Spaghetti. You buy the same quantity, but then there is a point in which there will be a promotion in your supermarket. Barilla should be informed. They should organize the promotions together. – Forecasting – Production/Procurement Planning It’s not really common, since it is complex. 32 Organizing supply chain by identifying the OPP First you are at factory, having suppliers. Postponement strategy. - Assemble to order 35 - Make to order. Products very individualistic. They are guided by make to order approach (for instance: Cruise Ships) - Purchase and make to order Speculation. S&OP_ Sales and Operations Planning A process to develop tactical plans by integrating marketing plans for new and existing products with the management of the supply chain. Brings together all the plans for the business into one integrated set of plans. Top-down planning process Develop the aggregate sales forecast and planning values.  Translate the sales forecast into resource requirements.  Generate alternative production plans. Demand forecasting - Forecasts are almost always wrong by some amount (but they are still useful). - Forecasts for the near term tend to be more accurate. - Forecasts for groups of products or services tend to be more accurate. - Forecasts are no substitute for calculated values. They are guesses. Important Input to forecasts: - Past demand - Lead time of product replenishment - Planned advertising or marketing efforts - Planned price discounts - State of the economy - Actions that competitors have taken Forecast Methods 36 Times Unit Times series is green line, while the blue is trend and red is average. Here’s the components of a time series. What about including new info? STATIC APPROACHES: the estimates of level, trend and seasonality do not vary as new demand is observed  Decomposition of a time series (trying to isolate trends, season…) I take an amount of data, based on that features and I try to make computations and prediction, without caring about new info. ADAPTIVE APPROACHES: the estimates of level, trend and seasonality are adjusted after each demand observation  Moving averages, simple exponential smoothing, Holt’s model, Winter’s model We adapt on every change. Here we got an overview. 37 Unadjusted forecast model Linear equation is “Customers = 122.81 + 27.02 x Day”  As for example: 149.8* = 122.81 + 27.02 x 1  Unadjusted value: 149.8 ;  Seasonality = Demand/ Forecast = 131/ 149.8= 0.87 lower than 1 (over forecasting model) – NOT YET CONSIDERED Now consider Seasonality Calculate the (Demand/Forecast) for each of the time periods: Friday 1: (Demand/Forecast) = 131/149.8 = 0.87 Friday 2: (Demand/Forecast) = 216/230.9 = 0.94 Friday 3: (Demand/Forecast) = 286/312 = 0.92 Friday 4: (Demand/Forecast) = 355/393.1 = 0.90 Calculate the average seasonal indices for Friday: Average seasonal index. Friday = (0.87 + 0.94 + 0.92 + 0.90)/4 = 0.91 Calculate the seasonally adjusted forecasts Seasonally adjusted forecast = unadjusted forecast x seasonal index Friday 1: 149.8 x 0.91 = 135.96 Friday 2: 230.9 x 0.91 = 209.53 Friday 3: 312 x 0.91 = 283.10 Friday 4: 393.1 x 0.91 = 356.67 40 First I can compute the regression model. Then I checked coefficients to see how many obs are explained by data and also the depth of correlation. Now we basically calculate the ratio as the hint said. Then I take the average of ratios of Friday, Saturday, Sunday and so on. And adjust to take season under control. Then finally, visualize the time series. By having a graph, you fast see an increase in the exercise 3. I see immediately the path and the deviation of the reality (red) from the perfect ideal model (green line). Also important is the sum of mean square: the lower is the better is. Where the forecast is much higher of the reality, we are over estimating. And vice versa. Read also the seasonality factor (less than 1 we are overestimating, greater than 1, we are underestimating). Forecast accuracy How do we know: - If a forecast model is “best”? If a forecast model is still working? What types of errors a particular forecasting model is prone to make?  Need measures of forecast accuracy MSE: High MSE- bad/ low MSE-good. The lower is, the better is MAPE: how much (%) I am away from … (AN IMPORTANT THING TO REMEMBER: different departments are interested in different sides of the product. Logistic deals with quantity (units), while top management and sales work with values (monetary values)) 41 Exercise 4. MAD1= 2.7 MAD2=3.2 So, we continue with Model 1 Lecture 2 (19/02) – extraordinary lesson Aggregate Planning Aggregate Planning Exercise: Independent Demand at the consumer phase of the supply chain. Dependent demand is built backwards at every previous step from the final consumer stage. It is all built on the number of items that we are going to sell. Aggregate Planning deals with the right strategy to minimize costs to program every sell each period. If I have to reach a certain number (example total: 240.000), I can produce all once, the same quantity for many following periods, or maybe a month more and a month less. Alternative Production Plans Level production plan: a S&OP plan in which production is held constant and inventory is used to absorb the differences between production and the sales forecast. So, basically, we produce the same every period, but sometimes we produce more or less than required, but we have stock/inventory to absorb difference from sales and production. 42 I have to compare the different model and then take the decisions. However it cannot depend just on costs. It depends from many factors: - freshness of a product that could not be in stock - client preferences - and so on… If you take a decision in one area of business, you will have some consequences on other areas. You need a trade-off. INVENTORY MANAGEMENT – CYCLE STOCKS (Certain demand pattern: how a demand is constant, deterministic and change) - Economic order quantity (EOQ) – single product/single price - Economic order quantity (EOQ) – single product/quantity discount - Economic order quantity (EOQ) – multiple products/single price Reorder point is a quantity. Safety stock is good to protect you from stock out. It allows to operate automatically. It is a graph important. What is inventory/stock? Those stocks or items used to support production (raw materials and work-in- process items) supporting activities (maintenance, repair, and operating supplies) and customer service (finished goods and spare parts). Inventory is good when is not too less, when demand is fluctuating. it is bad when it’s too much or not plant. It seems there’s a difference between stock and inventory. Stock is expressed by physical units; inventory is 45 expressed my monetary values. Inventory for deterministic (constant) and stochastic (casual) demand: Exercise. Each order cost is fixed. How is stock ideally moving? Where is the optimal order quantity (EOQ)? Data: - Demand: D = 3000 units - Order Quantity: Q = 500 units - Order Cost: S = 50$ - Holding cost: H = 20% x C, at year - Price of a single unit : C = 20$ Total annual cost calculation 46 Annual material cost = CD = 20$ x 3000 = 60.000 $ Number of orders per year = D/Q = 3000/ 500 = 6 Annual ordering cost = (D/Q)S = 6 x 50$ = 300$ Annual holding cost = (Q/2)H = (Q/2)hC= 250 x 20% x 20$ = 1000$ Total annual cost, TC = CD + (D/Q)S + (Q/2)hC= 60.000 + 300 + 1000 = 61.300 $ Where is the EOQ? Determining the EOQ and the optimal order frequency So, we can see how 500 was not efficient as EOQ, neither 200, but the right answer is given by: Q*= √ 2 x3000 x500.2 x20 = 273.86 47 Unit Price : CA = 5$ Unit Price : CB= 4$ Unit Price : CC = 5$ Order Fix Cost: SA = 400$ Order Fix Cost: SB = 400$ Order Fix Cost: SC = 400$ Order Add Cost: sA= 100$ Order Add Cost: sB= 100$ Order Add Cost: sC= 100$ Order Cost: S= 500$ Order Cost: S= 500$ Order Cost: S = 500$ Holding Cost: H = 20% x C, at year Holding Cost: H = 20% x C, at year Holding Cost: H = 20% x C, at year Quantity: EOQA = 4.472 Quantity: EOQB = 1.768 Quantity: EOQC = 949 Optimal frequency: nA= 4.5 Optimal frequency: nB= 1.4 Optimal frequency: nC= 0.9 Annual Ordering Cost: 2.236$ Annual Ordering Cost: 707$ Annual Ordering Cost: 474$ Annual Holding Cost: 2.236$ Annual Holding Cost: 707$ Annual Holding Cost: 474$ Total Cost: TC= 4.472$ Total Cost: TC= 1.414$ Total Cost: TC= 949$ Total Final Cost = 6.835$ OQ Ordered and Delivered Jointly ▪ S* = S+ sL + sM + sH  S* = 700$ ▪ n* = √ DhC+DhC+DhC2S * , where DHC is provided for every sup  n* = 4,04 = 4 orders/ year ▪ Annual order cost = (D/Q)S* = S* n [Quantity not necessary]  Annual Order Cost = 2800 $ ▪ Since Q = D/n, then (Q/2)hC = (DhC)/2n: Annual holding cost = (DLhCL)/2n + (DMhCM)/2n + (DHhCH)/2n  Annual Holding Cost = 2863 $ ▪ Total annual cost = Annual Order Cost + Annual Holding Cost  TAC = 5663 $ Total Final Cost = 5663 $ Conclusion: aggregation strategy saves money! Forecasting example Weekly sales of ten-grain bread at the local organic food market are in the table below. Based on this data, forecast week 9 using a five-week moving average. 50 Answer: (382 + 410 + 432 + 405 + 421)/ 5 = 410.0 Forecasting example 2 Weekly sales of copy paper at Cubicle Suppliers are in the table below. Compute a three-period moving average and a four-period moving average for weeks 5, 6, and 7. Compute MAD for each forecast. Which model is more accurate? Forecast week 8 with the more accurate method. SOLUTION: Three-period moving average and four-period moving average ( for W5,6,7): - Week 5: 3MA = 26,3 ; 4MA = 24 - Week 6: 3MA = 25,7 ; 4MA = 24,5 - Week 7: 3MA = 22,3 ; 4MA = 23,5 MAD x each forecast: We need to know each deviation: - Week 5: D1= 7,3 ; D2= 5 - Week 6: D1= 8,7 ; D2= 7,5 - Week 7: D1= 1,3 ; D2= 2,5 MAD1 = ∑ Actual−Forecast n = 5,77 MAD2 = ∑ Actual−Forecast n = 5  MAD2 is most accurate! Forecast week 8 with model accurate method: Ft = Ft-1 + α (At-1 – Ft-1)  F8 = 23,5 + α (21 – 23,5) = 22, (with α=0.6 chosen arbitrarily ?) Aggregate Planning example A company has a sales forecast for the following five months as shown in the table. If they have a beginning inventory of 100 units, 51 •what amount should be produced under a level plan in order for them to have an ending inventory of zero units at the end of the five-month period? Re-solution: Inventory = 100 units What amount should be produced to empty the inventory? [Level Plan means to produce at same quantity]  (Sales Forecasted – Inventory) / # Periods  ((350+400+300+500+350)−100)/ 5 = 360 at Month Example Demand: μ= 65.000 units/year; Order Cost: C = € 120,-/order; Single Unit Cost: c = € 5/unit; Holding Cost: h = 25 %/year; LT = 2 weeks; 1 year= 52 weeks • How much is the inventory holding cost rate? ℎ = 0.25 x 5 = 1.25 • How much is the optimal order quantity? 𝑄∗ = √ 2 x65.000 x1201.25 = 3533 • How often do we need to order? 𝑇 = 𝜇/𝑄∗ = 65000/3533 = 18 • What is the optimal re-order point? 𝑟∗ = 𝐿𝑇 x 𝜇𝑤 = 2 x 65000/ 52 = 2 x 1250 = 2500 • How much are the total costs? 𝑍𝑄∗ = (𝜇/ 𝑄∗) x 𝐶𝑓 + (𝑄∗/2) x ℎ = (65000/3533) x 120 + (3533/2) x 1.25 = 4415.88 EOQ exercise with quantity discounts Demand: μ = 5,000 units/year Holding Cost: H = 20 % Costs per order = € 49.00 Purchasing price (S): • c1(0 to 999 units) = € 5.00 • c2(1,000 to 1,999 units) = € 4.80 • c3(2000 + units) = € 4.75 What is the optimal order quantity? EOQ1 = √ 2 x5.000 x 490.2x 5 = 700 units/order 52 not purchase a previous year’s costume. • Disposal and salvage costs are minimal and can be considered zero. What are the costs of a non-sold item that cannot be sold tomorrow? Costs of overstocking (example: Newspaper) Cost of over-stocking, where c = variable order costs and r= return price  cO = c - r What are the costs of an item I cannot sell because I do not have any more of this item? Costs of understocking (example of Newspaper again) Cost of under-stocking, where c = variable order costs and p = sales price  cU = p – c Replenishment quantity - Single Period System: - what is the optimal service level? The critical ratio is CR = CU/ (CU + CO) - how to get to the optimal replenishment quantity? Q*CR = μ + zCR x σ Solution of previous example • Cost/unit: $10 ; Price/unit: $30 • Disposal & salvage costs = Return price = 0 CU = Sales price/unit – Cost/unit = 30 – 10 = 20 CO = Cost of overage = Cost/unit – Salvage value = 10 – 0 = 10 z(CR0.67) = 0.43 Order Quantity: 𝑄 ∗ = 𝜇 + 𝑧𝜎  𝑄∗ = 200 + 0.43 x 15 ≈ 206 (200 and 15 chosen arbitrarily ?) Managing independent demand inventory – control systems Periodic Review System = PRS : the inventory level for an item is checked at regular intervals and restocked to some predetermined level. Continuous Review System = QRS: the inventory level for an item is constantly monitored and when the reorder point is reached, an order is released. Calculating the restocking level (R) in a periodic review system RL = μRP+L + z x σRP+L Example Jimmy’s delicatessen sells large tins of coffee. Every Monday he checks his stock of coffee tins and orders 55 from his supplier. Two days later, the supplier delivers his order. The average daily demand is 12 tins of coffee and the standard deviation of demand over 9 days, which covers the reorder period (7 days) and lead time (2 days), is 20 tins. Jimmy wants to be in stock 99% of the time. What restocking level should be established? 𝑅 = 𝜇𝑅𝑃+𝐿 + 𝑧 x 𝜎𝑅𝑃+𝐿 ; 99% - z= 2.33  R= 12 x (7+2) tins + 2.33 x 20 tins = 154.6 ≈ 155 tins What if ... the next time Jimmy checks the coffee, he counts 45 tins. How much will he order? 𝑄 = 𝑅 − 𝐼  𝑄 = 155 − 45 = 110 Continuous systems – where is the reorder point ROP? Reorder point when demand rate (d) and lead time (L) are constant : ROP = dL Reorder point when demand rate (d) and lead time (L) or both vary: ROP = d́L + SS, where SS= Safety Stock How much should we order per day? Purchasing price: 0,80 Euro Sales price: 1,95 Euro Return price: 0,1 Euro Daily demand: 22 pieces/day Standard deviation: 6 pieces/day SOLUTION: Cu = Sales price/unit – Cost/unit = 1,95 – 0,80 = 1,15 Co = Cost of overage = Cost/unit – Salvage value = 0,80 – 0,1 = 0,70 CR = 1,15/(1,15+0,7) = 0,62 z = 0,3  Q* = 22 + 6 x 0,3 = 23,8 Periodic review system example Average demand during lead-time is 240 tins with a standard deviation of 40 tins. The supermarket 56 demands 95% availability of the product. What is the appropriate restocking level? 95%, z= 1,65  R = 240 tins +1.65 × 40 tins = 306 tins What if… The next time the delivery person stops by and he counts 45 tins. How much will he order? Q = 306 - 45 = 261 tins Example for Continuous Review System Demand during lead-time for ice cream at Amorino Gelato can be approximated by a normal distribution with a mean of 188 hectolitres and a standard deviation of 16 hectolitres per day. What reorder point would be consistent with the desired service level when 95 out of 100 orders want to be fulfilled? 95% , z= 1,65  𝑅𝑂𝑃 = 188 + 1.65 x 16 = 214.4 FROM HERE THE FOLLOWING KOTZAB SLIDES WERE NOT EXPLAINED IN CLASS FOR COVID REASONS ( DID AT HOME BY MYSELF) Network design (Facilities) General description of the problem Identify an optimal location (x,y) for each new warehouse. A warehouse can be at any location. Transportation costs are proportional to the distance between the warehouse and the customer and to the demand of a customer. Every customer represents a discrete demand point (aj , bj ). There are no capacity limitations or other restrictions at the locations. The objective is to minimize all transportation costs from the warehouse to all customers Try to solve this problem A county wants to build one centrally- located processing facility to serve the county's three recycling drop- off locations. The three drop-offs have characteristics as given in the table below Center-of-Gravity Method 57 Initial solution with Northwest Corner Rule Z = 12 x 80 + 18 x 20 + 7 x 40 + 30 x 70 + 15 x 40 + 16 x 50= 5,100 € Stepping Stone Method – Cost saving S2-D1 Z = (1 x 21) – (1 x 12) + (1 x 18) – (1 x 7) = + 20€ Other cost savings Best Solution 60 Z = 12 x 30 + 8 x 50 + 7 x 60 + 15 x 90 + 9 x 70 = 3,160 € Vehicle Routing Problem How to allocate vehicles to delivery routes? I vehicles are used to deliver J customers from one warehouse. Which vehicle shall be used for which customer and in which sequence shall vehicles go to customers? Example 5 customers with demand of one pallet each; trucks with max 4 pallets each The Savings heuristics Create an initial solution: One vehicle drives to each customer. Assess the cost change for relations between two customers. if customers are served by two vehicles instead of one : Z0 = ∑ j=1 J C0 j Consolidate tours of customers with the largest savings as long as capacities are achieved, or the number of tours equals the number of vehicles: ij =  ci0  c0j  cij Initial Solution : 𝑍0 = 2 x (30 + 35 + 40 + 30 + 60) = 390 Which cost changes occur? Cost Changes S1,2 = -30 -35 +15 = -50 S1,3 = -30 -40 +35 = -35 S1,4 = -30 -30 +56 = -4 S1,5 = -30 -60 +52 = -38 S2,3 = -35 -40 +20 = -55 61 S2,4 = -35 -30 +41 = -24 S2,5 = -35 -60 +67 = -28 S3,4 = -40 -30 +21 = -49 S3,5 = -40 -60 +66 = -34 S4,5 = -30 -60 +45 = -45 Look at cost savings and start consideration from the biggest one… S2,3 = -35 -40 +20 = -55 Route 1 = {6,1,6}, 1 pallet Route 2 = {6,2,3,6}, 2 pallets Route 3 = {6,4,6}, 1 pallet Route 4 = {6,5,6}, 1 pallet S1,2 = -30 -35 +15 = -50 Route 1 = {6,1,2,3,6}, 3 pallets Route 2 = {6,4,6}, 1 pallet Route 3 = {6,5,6}, 1 pallet S3,4 = -40 -30 +21 = -49 Route 1 = {6,1,2,3,4, 6}, 4 pallets Route 2 = {6,5,6}, 1 pallet Route 4 = {6,5,6}, 1 pallet (MAYBE THIS ROUTE WRONG SINCE IT’S NOT NECESSARY?) Total cost: 390 – 55 – 50 – 49 = 236 IT SAVES MONEY!!!! Facility location exercise Company A wants to locate a warehouse to serve their major customers that are shown in the table. What is the location that will minimize the transportation costs for their family of products? SOLUTION: X* = (25x 3 )+(13 x5 )+(6 x 8 )+(4 x 1) 25+13+6+4 =4 ; Y* = (25x 7 )+(13 x2 )+(6 x 4 )+(4 x5) 25+13+6+4 = 5,10 Exercise Transport problem For the problem data set below 62 Logistic Oriented Strategy. Low logistic cost and high service level. Example: Amazon. Huge availability of products, high service, much higher of traditional shops. Logistic very fast. And it does not cost so much since they don’t have people learning and other stuff, and have low reduced stocks. This is a successful example of how to compete also in logistics. Nowadays the most important performances of client are the time related ones. Time critical systems. High speed and low dependability (e-commerce: delivery very fast, but dependability is not dependent on their selves, they cannot be “ship tomorrow”); Time definite systems. High dependability and low speed (example buying a customized machine for your factory, personalized to your needs, with high dependability, but normally it requires some time, low speed). Time based systems: high speed and high dependability. Example just-in-time companies, Maserati. Channel Design. This choice relates mainly to marketing issues. Direct and indirect. Direct: when you don’t have any manufacturer between you and the consumer. Example: Gucci product in Gucci store. Buying Pasta Barilla in Esselunga, you’re using a distributor (indirect channel). Long and short indirect channel. Long if the channel is very stepped, for example a product comes from China. Nowadays we have also another type: online channel, place order on web and receive it at home. It’s the e-commerce case. This is quite again a marketing decision. It has implications in the field of logistics if it is adopted. Logistics for e-commerce is a major source of challenges for the company due to the cost and complexity related to the «last mile» logistics, which refers to the home delivery of the products bought on-line. Example: if you don’t find customers. There could be many problems. Many companies underestimated extra costs in order to take care of delivery problems. Today, managers start to think to some solutions, such as technology based solutions (examples: the Lockers of Amazon; click and collect, that make the customer go to the shops to retire items). Network Design. The logistic network is the bundle of warehouses and distribution centers (DC) that the product passes through before reaching the customer. Gucci has just 1 warehouse and Coca Cola has many distribution centers. This is a description of logistic networks. The main network design choices are the following: – Centralization/decentralization : few/many warehouses and distribution centers – Postponement/speculation: in manufacturing logistics, deciding to which step you should stop producing, if semi-worked products or final ones – Outsourcing: when you want to stock your items, in distribution centers or near your warehouse 65 Centralization/Decentralization This choice refers to the total number of warehouses and DCs of the company Red circles: physical stores. First case: Gucci. Second case: Coca Cola. One implication is on transportation costs. Where is higher Let’s find out with a calculational example. Example Transportation is highly affected by scale cost. Focus on centralized system: we will make a delivery of small quantity (no economy of scales, higher transportation costs). So here, the transportation costs are higher, because of that! On the opposite, from warehouse to DS, we can make deliveries of more much pieces, because we have to cover more regions: so you will have lower transportation costs in decentralized systems. So, why to choose centralized approach anymore? Let’s consider Europe and a situation like that: 66 We are for example Gucci, in Switzerland, and we have to deliver 100 forecast items in UK, France and Italy. However actually forecast are quite wrong. It’s much better to stock all items in one country to wait for variations of demand. There’ s a lot of variability also for lot of consumer goods. Example of Ice- Cream: Magnum Algida. Every year they launch new tastes, that last few weeks, and don’t know exactly how many pieces they will sell. There could be many cases in which one warehouse is preferred. However, if you have products with low unit value, but higher volumes, you cannot deal with centralized approach anymore! (Example of Coca Cola). Look at the Value Density= unit value/ weight (or volume). If it’s high, a centralized system is suggested, but in the opposite case, you should prefer a decentralized approach. Postponement/ Speculation This choice refers to: – What to stock (either a finished product or a semi-finished one) – Where to stock (either in the warehouse close to the factory or in the local DCs) Two opposite strategies in manufacturing and logistics: - Speculation means to carry out an activity on the basis of a forecast concerning customer’s future orders. - Postponement means to carry out an activity later in time, i.e. once the order from the client has been received. You don’t do anything on the basis of you forecast, you just wait until the order is received. Manufacturing postponement: the product is manufactured or completed on the basis of an actual order from the client. Semi-finished products are stocked. Example of Ikea. Make to order production system: don’t produce anything on the basis of forecast. You just buy rough materials. We just wait and start production process just when we receive orders. Logistics postponement: the product is moved downstrean in the logistic network when the company receives an actual order from the client. Stock of products is held in central WH. You’re postponing the transportation of goods, until the last possible moment (receive the order). Manufacturing Speculation: the product is manufactured and completed on the basis of a sales’ forecast. Finished Products are stocked. Make to stock production process. Logistic Speculation: the product is moved downstrean in the logistic network on the basis of a sales’ forecast. Stock of products is held in DCs. You start moving products starting from the forecasts, doing last logistics moving when you receive orders. 67 When you do transportation, you produce a lot of emissions. Let’s see the framework of emissions caused from physical distribution. First, quantity of goods delivered: the more you transport, the big will be your truck or number of trucks, the more your emissions. Second, distance for the delivery: from Milan to Palermo it’s very much road! Third, Modal split (what modal transportation you use): if you move diamonds, you need air transportation, but it’s the most polluting way. Maybe you can use truck to Genève and then a ship. It’s better. Modal split has in impact. Also, size is important: in transportations you have economies of scale, using 1 truck bigger or 2 truck smaller with same quantity and same modal split, but second way is less polluting. Also, saturation is issue: it would be great to use big rate (100%) of saturation of transportation. Finally, consumption and type of fuel are other two important issues: different type of fuel determinant very different emission (think to “green”). All these issues are conditioned by some factors that can be endogenous factors, that are issues linked to the firm itself or exogeneous ones, something that company cannot influences. Electric vehicle are there not because company willing; economical fiscal policies, for example the fiscal incentives to go in Switzerland led you there, but the distance will be higher.. Carbon Footprint: some targets In 2011 EU announced a target to cut transport GHG (green house gases) emissions by 60% by 2050 UK aims at reducing GHG emissions by 90% by 2050 against the 1990 baseline DB Shenker has GHG reduction target of 20% per Ton/km over the period 2006- 2020 Deutsche Post DHL has a CO2 reduction target of 30% over the period 2007-2020, also for the subcontractors Many companies have very challenging targets to reduce emissions in transportation. How to measure them and capture them? Carbon Footprint Auditing It is the process of quantifying the total amount of Green House Gases (GHG) emissions, expressed in CO2 equivalents, emitted directly or indirectly by an entity In Europe the Integrated Pollution Prevention and Control Directive requires that industrial activities with a high polluting potential must report emissions exceeding specific thresholds for the six GHG identified by the Kyoto protocol, i.e.: 1. Carbon dioxide (CO2 ) 2. Methane (MH4 ) 3. Nitrous oxides (Nox) 4. Hydrofluorocarbos (HFC) 5. Perfluorocarbons (PFC) 6. Sulphur hexafluoride (SF6) What is the Carbon Footprint? Carbon Footprint can be defined as the total amount of GHG, expressed in CO2 equivalents ( a sort of standard unit measurement), emitted directly or indirectly by an entity Relevant issues in Carbon Footprint Auditing are: 1. The organizational boundaries of the system to be carbon-footprinted 2. The operational boundaries of the system to be carbon-footprinted 3. The measurement process 1. Organizational Boundaries This issue refers to the identification of the activities that can release emissions and that the company is responsible for. Two situations can be observed: 1. The company owns 100% of its operations: it is responsible for all GHG emissions released by its operations 70 2. The company holds a share of its production (e.g. through a joint venture). In this case two different approaches can be used: • Equity share approach: I’m only 40% of the company, I’m responsible only for 40% of emissions of the company • Control approach: I’m owing only 40% but actually the majority of board is indicated by me, so I’m responsible since I have majority 2. Operational boundaries This issue concerns the scope of the measurement process, which involves the identification of the source of direct and indirect emissions of GHG that must be considered for the reporting process: 1. Scope 1: it refers exclusively to the direct emissions relative to the GHG covered by the Kyoto Protocol, which are released by sources owned or controlled by the company 2. Scope 2: it refers to the indirect emissions due to the production of the electricity bought by the company for its own use 3. Scope 3: it covers the indirect emissions that are a consequence of the company’s interaction with other entities and that are produced by sources not owned or controlled by the company itself So, according to each case, I will responsible for that scope. 3. Measurement Process It refers to the activity of quantifying the total amount of GHG emitted within a selected operational boundary (or Scope) Emissions to be reported: 1. Carbon dioxide (CO2 ) 2. Methane (MH4 ) 3. Nitrous oxides (Nox) 4. Hydrofluorocarbos (HFC) 5. Perfluorocarbons (PFC) 6. Sulphur hexafluoride (SF6) All of them must be converted into CO2 equivalents, i.e. the equivalent quantity of CO2 that has the same global warming potential over a period of 100 years Next table shows the conversion table: Organizations can collect: – Primary data, that means I can directly my emission – Secondary data (e.g. http://www.ghgprotocol.org/calculation-tools; http://ecotransit.org/  let’ see next example), using database that report average emission produced by orgs like mine If primary data is collected, organizations can choose between: – The top-down approach – The bottom-up approach 71 Measurement Process for freight transportation To calculate GHG emissions of road transportation, two approaches can be used: – Fuel-based – Activity-based Fuel based approach The amount of fuel used is multiplied by the standard conversion rate for each type The total amount can be obtained by: – Fuel receipts – Financial reports on fuel expenditures Activity based approach 72