MOPE Week 3 Workshop in salford university.pptx

This presentation about the assignment O supply chain management and the condition follows

1. Managing Operations and Project Environments Week 3 Workshop

2. Attendance Code

3. Module structure Week Dates Themes Wk 1 W/C 21.07.25 OM and PM Key Concepts, Systems Thinking, Complexity Wk 2 W/C 25.07.25 Interconnectedness Wk 3 W/C 04.08.25 Synthesis Wk 4 W/C 11.08.25 Emergence Wk 5 W/C 18.08.25 Feedback Wk 6 W/C 25.08.25 Revision and Assignment G uidance   01.09.25 (by 16:00) Assignment submission

4. Activities on: Data (5Vs) Artificial Intelligence Forecasting Reconciling demand and capacity Session structure

5. Activity 1 Data

6. 5 Vs of Data

7. The big difference between Industry 4.0 is that it seeks to reinvent the entire process around the power of big data which contains greater (as per Oracle and Teradata): Volume - have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as Twitter data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment Velocity - the fast rate at which data is received and (perhaps) acted on. Some internet-enabled smart products operate in real-time or near real-time and will require real-time evaluation and action Variety - many types of data that are available. Traditional data types were structured and fit neatly in a relational database. With the rise of big data, data comes in new unstructured data types. Unstructured and semi-structured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata Veracity - quality, accuracy, integrity, credibility and trust we have of data . Gathered data could have missing pieces, might be inaccurate or might not be able to provide real, valuable insight Value - from insight discovery and pattern recognition that leads to more effective operations, stronger customer relationships and other clear and quantifiable business benefits Key role of data

8. Mini-Activity What and who ‘creates’ data for your selected organisation (consider the wider supply chain)? Apply the 5 Vs to generate insights for a systems thinking approach

9. Activity 2 Artificial Intelligence

10. Background: You are the operations manager of a retail company that sells electronic gadgets that experiences fluctuating demand for its products, particularly during seasonal promotions and holidays As the operations manager, you are responsible for ensuring optimal inventory levels to meet customer demands while minimising holding costs and stockouts The company's current inventory management system relies on historical sales data and manual forecasts to determine reorder points and quantities However, this approach often leads to overstocking of slow-moving items and stockouts of high-demand products, resulting in lost sales and increased holding costs Objective: Your objective is to improve the company's inventory management practices by leveraging artificial intelligence to optimise reorder points and quantities The goal is to reduce holding costs, minimise stockouts, and enhance customer satisfaction AI Activity

11. Data Sales data: Jan 150 Feb 180 Mar 200 Apr 220 May 250 Jun 280 Jul 300 Aug 300 Sep 280 Oct 260 Nov 320 Dec 200 Average demand (D) 222.5 units per month. Ordering cost per order (S) = £100 Holding cost per unit per period (H) = £2

12. What patterns and trends can you identify? Determine the key factors influencing inventory management decisions, such as demand variability (standar d variation) , how much to order (EOQ) and when to order (reorder point) How can we balance the trade-off between holding costs and stockout risks? How can we incorporate seasonality and promotional events into our inventory management strategy to meet increased demand effectively? Role of AI in decision-making and its implications Reflections Tasks – Human v. AI

13. Seasonal Patterns : One noticeable pattern is the fluctuation in sales throughout the year. Sales tend to increase during certain months (e.g., May, June, July, November) and decrease during others (e.g., January, December). This could indicate seasonal demand, perhaps influenced by factors like weather, holidays, or consumer behaviour Increasing Trend : There seems to be a general increasing trend in sales over time, particularly from January to November, with sales peaking in November. This trend could suggest overall growth in demand or effectiveness in sales strategies over the months Stability : Despite some fluctuations, the sales data also shows a level of stability, especially during the latter half of the year (August to December), where sales hover around a certain range without significant deviations Consistency : The average demand per month (222.5 units) indicates a relatively consistent level of demand throughout the year. This consistency might suggest a stable customer base or market demand for the product Potential Outliers : While most months follow a consistent pattern, there are outliers such as April and September where sales are higher compared to adjacent months. Investigating the factors contributing to these outliers can provide insights into potential opportunities or challenges Cyclical Patterns : There may be cyclical patterns within shorter time frames, such as weekly or bi-weekly cycles, which could indicate specific buying behaviours or promotional activities ChatGPT responses

14. Activity 3 Forecasting

15. Naïve averaging This approach assumes that: Data from the immediately preceding past can be used to forecast needs for the next period The average based on one set of data, assumes that historical data from period to period shows changes are insignificant and need not be considered Period Actual Forecast 1 20 2 25 20 3 20 25 4 30 20 5 40 30

16. Time Series: Moving average The moving average model uses the last t periods in order to predict demand in period t+ 1. There can be two types of moving average models: simple moving average and weighted moving average The moving average model assumption is that the most accurate prediction of future demand is a simple (linear) combination of past demand.

17. Time Series: Simple Moving Average In the simple moving average models the forecast value is F t+1 = A t + A t-1 + … + A t-n n t is the current period. F t+1 is the forecast for next period n is the forecasting horizon (how far back we look), A is the actual sales figure from each period.

18. Example: forecasting sales at Kroger Kroger sells (among other stuff) bottled spring water Month Bottles Jan 1,325 Feb 1,353 Mar 1,305 Apr 1,275 May 1,210 Jun 1,195 Jul ? What will the sales be for July using 3 and 5-month moving average?

19. What if we use a 3-month simple moving average? F Jul = A Jun + A May + A Apr 3 = 1,227 What if we use a 5-month simple moving average? F Jul = A Jun + A May + A Apr + A Mar + A Feb 5 = 1,268 What does this tell us?

20. What do we observe? 3-month MA forecast 5-month MA forecast 5-month average smoothes data more; 3-month average more responsive

21. Time Series: Weighted Moving Average We may want to give more importance to some of the data… F t+1 = w t A t + w t-1 A t-1 + … + w t-n A t-n w t + w t-1 + … + w t-n = 1 t is the current period. F t+1 is the forecast for next period n is the forecasting horizon (how far back we look), A is the actual sales figure from each period. w is the importance (weight) we give to each period

22. Weighted moving Average Weighted moving average models apply: Weighting to period data Considers some periods more important than others. Higher weights are often given to more recent data

23. Answer The forecast for May is Whatever weights are used the sum of the weights must equal unity The greater the number of periods used in the model the greater is the smoothing effect (0.2 x £13500 + 0.3 x £11500 + 0.5 x £14000) = £13150

24. Why do we need the WMA models? Because of the ability to give more importance to what happened recently, without losing the impact of the past. Demand for Mercedes E-class Time Jan Feb Mar Apr May Jun Jul Aug Actual demand (past sales) Prediction when using 6-month SMA Prediction when using 6-months WMA For a 6-month SMA, attributing equal weights to all past data we miss the downward trend

25. What if we use a weighted moving average? Make the weights for the last three months more than the first three months… 6-month SMA WMA 40% / 60% WMA 30% / 70% WMA 20% / 80% July Forecast 1,277 1,267 1,257 1,247 The higher the importance we give to recent data, the more we pick up the declining trend in our forecast.

26. How do we choose weights? Depending on the importance that we feel past data has Depending on known seasonality (weights of past data can also be zero). WMA is better than SMA because of the ability to vary the weights!

27. Comparison of approaches

28. Activity 4 Capacity and demand

29. Ways of reconciling capacity and demand Level capacity Demand Capacity Chase demand Demand management Capacity Capacity Demand Demand Ignore the fluctuations and keep activity levels constant ( level capacity plan ) Adjust capacity to reflect the fluctuations in demand ( chase demand plan ) Attempt to change demand to fit capacity availability ( demand management )

30. How do you cope with fluctuations in demand? Absorb Demand Change demand Adjust output to match demand Level capacity Chase demand Demand management Activity – select an organisation and explore what activities could be used by organisations to implement each of the approaches and what are the positive and negative effects of the three different strategies on the organisation, employees, suppliers and customers? Ways of reconciling capacity and demand

31. In a level capacity plan, the processing capacity is set at a uniform level throughout the planning period, regardless of the fluctuations in forecast demand. This means that: The same number of staff operate the same processes and should therefore be capable of producing the same aggregate output in each period Level capacity plans of this type can achieve the objectives of stable employment patterns, high process utilization, and usually also high productivity with low unit costs They can also create considerable inventory which has to be financed and stored The biggest problem is that decisions have to be taken as to what to produce for inventory rather than for immediate sale Most firms operating this plan give priority to only creating inventory where future sales are relatively certain and unlikely to be affected by changes in fashion or design Not suitable for ‘perishable’ products , such as foods and some pharmaceuticals, for products where fashion changes rapidly and unpredictably, or for customized products Level capacity plan

32. Level capacity plan - Examples 32 The hotel would employ sufficient staff to service all the rooms, to run a full restaurant, and to staff the reception even in months when demand was expected to be well below capacity. The supermarket would plan to staff all the checkouts, warehousing operations, and so on, even in quiet periods

33. Absorb demand Part finished Finished goods, or Customer inventory Queues Backlogs Have excess capacity Make to stock Keep output level Make customer wait Absorb demand

34. Chase demand plan Attempts to match capacity closely to the varying levels of forecast demand . Much more difficult to achieve than a level capacity plan, as different numbers of staff, different working hours, and even different amounts of equipment may be necessary in each period. Pure chase demand plans are unlikely to appeal to operations which manufacture standard, non-perishable products . A pure chase demand plan is more usually adopted by operations which cannot store their output, such as customer-processing operations or manufacturers of perishable products. Such a plan is less likely to be appropriate for the aluminium producer than for the woollen garment manufacturer.

35. Methods of adjusting capacity Overtime and idle time Varying the number of productive hours worked by the staff in the operation. When demand is higher than nominal capacity, overtime is worked, and when demand is lower than nominal capacity the amount of time spent by staff on productive work can be reduced. Varying the size of the workforce Hiring extra staff during periods of high demand and laying them off as demand falls, or hire and fire. Using part-time staff. This method is extensively used in service operations such as supermarkets and fast-food restaurants but is also used by some manufacturers to staff an evening shift after the normal working day. Subcontracting. In periods of high demand an operation might buy capacity from other organizations. Subcontracting can be very expensive. A subcontractor may not be as motivated to deliver on time or to the desired levels of quality. 35 Chase demand plan

36. Change demand Change demand through price. For example, some city hotels offer low-cost ‘city break’ vacation packages in the months when fewer business visitors are expected. Skiing and camping holidays are cheapest at the beginning and end of the season and are particularly expensive during school vacations. The objective is invariably to stimulate off-peak demand and to constrain peak demand, in order to smooth demand as much as possible. Alternative products and services. Fill periods of low demand such as developing alternative products or services which can be produced on existing processes , but have different demand patterns throughout the year. Most universities fill their accommodation and lecture theatres with conferences and company meetings during vacations. The apparent benefits of filling capacity in this way must be weighted against the risks of damaging the core product or service, and the operation must be fully capable of serving both markets.

37. Mixed plans Most operations managers are required simultaneously to reduce costs and inventory, to minimize capital investment, and yet to provide a responsive and customer-oriented approach at all times. For this reason, most organizations choose to follow a mixture of the three approaches. Woollen knitwear company example: Some of the peak demand has been brought forward by the company offering discounts to selected retail customers (manage demand plan). Capacity has also been adjusted at two points in the year to reflect the broad changes in demand (chase demand plan). Yet the adjustment in capacity is not sufficient to avoid totally the build-up of inventories (level capacity plan).

38. Rarely does each stage of a supply chain have perfectly balanced capacity because of different optimum capacity increments Current capacity = 1,010 units Required new capacity =1,800 units Current capacity = 1,000 units Required new capacity =1,800 units Current capacity = 900 units Required new capacity =1,800 units Current capacity =1,100 units Required new capacity =1,800 units Capacity increment Operating cost Capacity increment Operating cost Capacity increment Operating cost Capacity increment Operating cost 800 units 600 units Parts manufacture Assembly plant Warehouse Distribution

39. Aggregate planning strategies Chase strategy – using capacity as the lever Time flexibility from workforce or capacity strategy – using utilisation as the lever Level strategy – using inventory as the lever What are the advantages and disadvantages of these? Chopra and Meindl (2015)

40. Chase Strategy Production rate is synchronized with demand by varying machine capacity or hiring and laying off workers as the demand rate varies However, in practice, it is often difficult to vary capacity and workforce on short notice Expensive if cost of varying capacity is high Negative effect on workforce morale Results in low levels of inventory Should be used when inventory holding costs are high and costs of changing capacity are low Chopra and Meindl (2015)

41. Time Flexibility Strategy Can be used if there is excess machine capacity Workforce is kept stable, but the number of hours worked is varied over time to synchronize production and demand Can use overtime or a flexible work schedule Requires flexible workforce, but avoids morale problems of the chase strategy Low levels of inventory, lower utilization Should be used when inventory holding costs are high and capacity is relatively inexpensive Chopra and Meindl (2015)

42. Level Strategy Maintain stable machine capacity and workforce levels with a constant output rate Shortages and surpluses result in fluctuations in inventory levels over time Inventories that are built up in anticipation of future demand or backlogs are carried over from high to low demand periods Better for worker morale Large inventories and backlogs may accumulate Should be used when inventory holding and backlog costs are relatively low Chopra and Meindl (2015)