A summary for the Operations Management course at the University of Cologne in the summer semester of 2017.

This course was separated into two parts. Supply Chain Operations and Behavioral Economics. They were taught by Prof. Thonemann and Prof. Becker-Peth. Most of the graphics are taken from the slide decks of these two professors.

Supply Chain Operations

Motivational Reasons for Supply Chain Management

Bullwhip effect

The Bullwhip effect describes a phenomenon where small fluctuations in the end-customer demand lead to ever-increasing fluctuations of the demand down the supply chain. This is mainly caused by overreaction of individual supply chain participants to small fluctuations in demand.

The effect can be mediated by automated sharing of demand data across the supply chain. However, this is often not implemented due to business reasons. An example would be the non-disclosed amount of Apple watch sales. An alternative is the use of contracts to ensure a minimum and maximum demand per period with additional demand being offered at a higher premium.

Causes:

• lead time and delay of shipments vs. incoming orders
• loss aversion, overreaction
• periodic review inventory model leads to aggregation of orders to large sums

Bullwhip factor

$$BF(LT,T) \approx 1 + \frac{2(LT+1)}{T}+ \frac{2(LT+1)^ 2}{T ^2} \mid LT:\text{lead time}, T:\text{forecasting error periods}$$

Newsvendor Model

Facts (Reproduction)

• placing orders before demand is known
• overage and underage costs apply
• overage → $w-bb$ or $w-v$ (buyback / salavage)
• underage → lost sale opportunities

Formulas (Application)

variable description
$c_o, c_u$ overage cost, underage cost
$p_y$ probability that demand will be y
$c$ unit order cost
$S$ order Quantity

Critical Ratio $$F(S)=\frac{c_u}{c_u+c_o}=CR$$

Optimal order quantity $$S^* = F^{-1}\left(\frac{c_u}{c_u+c_o}\right)$$ **In case of normally distributed demand** $$S^* = \mu + z\sigma \mid z=F^{-1}\left(\frac{c_u}{c_u+c_o}\right)$$

Optimal expected cost

$$Z(S^ * ) = (c_u+c_o)\cdot f_{N(0,1)}(z) \cdot \sigma$$ **Optimal expected profit** $$\Pi(S^* ) = (r-c)\mu-Z(S^ * )$$

Economic Order Quantity Model

Facts (Reproduction)

• assumes a constant demand
• orders arrive instantly
• fixed order costs and linearly dependent storage costs
• approach is: large fixed order costs increase order quantity, large holding costs decrease the number

Formulas (Application)

variable description
$\mu$ demand rate p period
$K$ fixed order cost
$c$ variable order cost(unit/order)
$h$ inventory holding cost (unit/period)
$x$ order quantity

optimal order quantity $$x^* = \sqrt{\frac{2K\mu}{h}}$$

total cost per period inventory holding cost + fixed order cost + variable order cost $$Z(x) = h\frac{x}{2} + K\frac{\mu}{x} + c\mu$$

Facts (Reproduction)

• orders arrive after lead time
• excess demand is back-ordered
• at each period order the difference between the current inventory positions (inventory - outstanding orders) and the order-up-to inventory level S

Process of each session is:

1. Observe inventory level $I_t$
2. Observe open orders $O_t$
3. Compute inventory position $IP_t$
4. Place order $x_t = S– IP_t$
5. Receive shipment $x_{t-LT}$
6. Fill demand $y_t$

Formulas (Application)

variable description
$f(y)$ Density function of demand y
$F(y)$ Distribution function of demand y
$c$ Unit cost (0.50 €/unit)
$h$ Unit inventory holding cost
$p$ Unit backorder penalty cost
$r$ Unit revenue
$LT$ Lead time
$a$ almost b

Optimal order-up-to level As in the other cases, we use the standard formula $\mu+z\sigma$ with the parameter of this model as seen below $$S^* = \mu +z\sigma_ {LT+1} \mid z = F_ {LT+1}^{-1}\left(\frac{p}{p+h}\right)$$

To calculate LT+1 is used

$$\sigma_{LT+R} = \sigma \sqrt{LT+R} \mid R=\text{review period}$$

To calculate expected costs of optimal solution

$$Z(S^* ) = (h+p)\cdot f_{N(0,1)}(z)\cdot \sigma_{LT+1}$$

Continuous Review Inventory Model

Formulas (Application)

variable description
$\mu$ mean demand
$\sigma$ standard deviation of demand
$h$ inventory holding cost
$p$ unit backorder penalty
$K$ fixed order cost
$LT$ lead time
$r$ reorder point (number not time)
$x$ order quantity (often also $q$)

expected inventory $$E[\text{inventory}]\approx r-\sigma_{LT} + \frac{x}{2}$$

Expected costs Here, the first describes the holding cost, the second term the expected penalty cost and the third the order cost/period.

$$Z(x,r) \approx h \left(r-\mu_{LT}+\frac{x}{2}\right)+ p\frac{\mu}{x}L\left(\frac{r-\mu_{LT}}{\sigma_{LT}}\right)+ K \frac{\mu}{x}$$

Service Levels

• $\alpha$-service level: probability that all demand in an order cycle is met must be at least $\alpha$
• to calculate the alpha service level, we need to look at the reorder point. If the reorder point is below the expected demand for the lead time plus a safety factor (which is dependent on the ɑ-level), we need to reorder. The reorder amount can still be determined using the iterative solution
• $\beta$-service level: expected fraction that is filled in an order cycle must be at least $\beta$
• the β-service level calculations are similar to those of the ɑ. The reorder amount is again determined by the approaches of the continuous review model. The reorder point is similar as with ɑ-level, however we use the inverse loss function instead and the parameter is dependent on $\sigma$ and $x^*$.

Formulas (Application)

ɑ-Service level

Optimal order quantity

$$x^* = \sqrt{\frac{2\mu K}{h}}$$

Optimal reorder point

$$r^* = F_{LT}^{-1}(\alpha) = \mu_ {LT}+z\sigma_{LT}$$

β-Service level

Optimal order quantity is the same as with ɑ-Service

Optimal reorder point

$$r^* = \mu_{LT} + L^{-1}\left(\frac{(1-\beta)\mu }{\sigma_ {LT}}\right) \sigma_{LT}$$

$$\beta \approx \frac{\mu-L\left(\frac{S^* - \mu_{LT+1}}{\sigma_{LT+1}}\right)}{\mu}$$

Expected backorder levels $$E(O-S) ^+=\sum_{y=S} ^{\infty}(y-S)p_y$$

Demand Estimation

Facts (Reproduction)

• demand estimation, also called forecasting
• target service level and expected demand lead to order quantity and reorder point adaptation

Generally there are two approaches

1. Distribution parameter fitting (i.e. figuring out the underlying function of the demand distribution)
2. Demand forecasting
• moving averages
• double exponential smoothing
• the standard deviation for the forecast is not based on the past deviations but instead on the forecast error. The better the forecasts were in the past few phases, the more narrow is our estimation variance for the upcoming periods.

Formulas (Application)

variable description
$\hat{y_t}$ forecasting for period t
$\epsilon_t$ forecast error ($e_t^2$ for squared err)
$M$ number of data points for forecast error
$N$ Degrees of freedom (1 for moving average, 2 for DES)

exponential smoothing

standard deviation of demand

$$\hat{\mu}_ t = \sqrt{\frac{1}{M-N} \sum_ {k=t-M} ^{t-1}{\epsilon_k ^2}}$$

Revenue Management

Finding the right price for different products with limited availability.

• Overbooking
• Discount allocation (e.g. nested policies and protection levels)
• traffic management

Fencing

• Parameters used for fencing tactics are: Time, Location, Flexibility, Groups, Variants

• with booking limits, if there are 10 spots available and 3 are allocated for the cheapest tier, once 3 bookings occur, independent of category, the lowest class is booked out.
• Protection Level: minimum amount of seats reserved for certain class
• Booking Limit: Max amount of bookings possible for a class

Littlewood’s Rule - Optimizing reservations

• Assumption: low-price customers book before high-price customers
• demand distribution of high-price customers is known
• low-price demand exceeds capacity

Approach: Set reserved amount for high priced booking class to 0. Now iterate over the possible number of reserved spots and calculate expected revenue. Once revenue doesn’t improve anymore, stop.

Calculating protection levels for 2 classes

variable description
$C$ capacity
$r_i$ revenue for class i
$B_i$ bookings for class i
$G_i$ protection level for class i

$$G_1^* = C-B_2^* = F_1^{-1}\left(1-\frac{r_2}{r_1}\right)$$

This approach can obviously also be used for several classes. For each class, the previous classes need to be solved first. So with four classes high – medium – low – trash, first we calculate the protection amount for the high class, then for the medium etc. until we have a set of nested protection amounts for each class.

Protection levels for n classes

1. Calculate protection levels for each class against k+1 class $$G_{k+1,I} = F_1^{-1}\left(1-\frac{r_{k+1}}{r_I}\right)$$
2. Aggregate protection levels for each class: Protection level of high class is sum of all protection levels in the classes below.
3. Calculate booking limits $$B_k = C - G_{k-1}$$

This approach can obviously also be used for several classes. For each class, the previous classes need to be solved first. So with four classes high – medium – low – trash, first we calculate the protection amount for the high class, then for the medium etc. until we have a set of nested protection amounts for each class.

Protection levels for n classes

1. Calculate protection levels for each class against k+1 class $$G_{k+1,I} = F_1^{-1}\left(1-\frac{r_{k+1}}{r_I}\right)$$
2. Aggregate protection levels for each class: Protection level of high class is sum of all protection levels in the classes below.
3. Calculate booking limits $$B_k = C - G_{k-1}$$

Overbooking

Calculation of overbooking amount

variable description
$C$ capacity
$B$ booking limits
$r$ price of one unit sold
$p$ cost of paying for overbooked customer
$x$ number of no-shows
$y$ demand
$f(x)$ density function of no-shows

$$B^* = C+F^{-1}\left(\frac{r}{p}\right)$$

and for ɸ-distributions

$$B^* = C + \mu_ x + F_ {0,1}^{-1}\left(\frac{r}{p}\right)\sigma_ x$$

Markdown Management

Reducing prices for products that are nearing their end-of-life and will soon stop being sold. Solutions are suggested to be calculated using the Excel solver (which uses a simplex algorithm).

System approach

• The idea of looking at the supply chain as a network that enables systems to function
• if one part is missing, the whole product / system stands still (e.g. a plane)
• while service levels look at the number of missed orders and assume that customers then go and get the part somewhere else, the backorder-level looks at the number of days a system has to be put on hold until the business can supply the requested part. So customers “stay waiting” until they get their order.

Marginal Allocation Algorithm

If one has to stock several parts, which parts to stock and how many of each part for a given budget to minimize expected backorders

• $h(S)$ = expected backorder level
• $c_i$ = cost for part i
1.  Set $S_i = 0 \forall i=1,...,N$
2.  Compute marginal utility/cost change per part (i.e. one more of each => increase in costs)
-   $m_i = \frac{h_i(S_i)-h_i(S_i+1)}{c_i} \forall i$
3.  Kick those out whose cost would go over budget
4. select $max\{m_i\}\forall i \in N$
5. Increase chosen max utility increasing $S_i$ by 1
6. if $\sum c_iS_i \leq b$ and not all kicked out jump to (3) else END


METRICs of cost analysis

• dependencies between parts (systems approach)
• Dependencies between echelons (expected delay at upper echelons because of backordering)

Strategic Safety Stock Placement in Multi-Echelon SC Networks

A big problem with a multi-tier production network is the large amount of decision variables that interact with each other and are interdependent, leading to a large and complex multidimensional problem with many constraints. One tries to find an optimal solution for the stock levels at each depot and the central warehouse to minimize overall costs while maintaining a certain service level.

Serial structures

The overall idea is that each preceding stage has a promised service time. Stage j also promises its service time $S_j$ and while doing so tries to minimize its inventory

variable description
$I_j(t)$ Inventory level at end of period t
$B_j$ Base-stock level of stage j
$d_j(a, b)$ Demand of periods $d_j(a)$ + … + $d_j(b)$
$S_{j-1}$ Inbound guaranteed service time
$S_j$ outbound guaranteed service time
$T_j$ Processing time of stage j

Observations:

• $\downarrow S_{j-1} \implies \downarrow B_j$
• $\uparrow d_j(a,b) \implies \uparrow B_j$
• $\uparrow T_j \implies \uparrow S_j$

Arbitrary structures

• Use of dynamic programming to solve complex systems

Supply Chain Management Case Studies

TODO

HP

Current situation

• All production in one factory
• both standard steps and customization in globally delivering factory
• i18n’ized products shipped to different DCs in Asia, US and Europe
• especially europe suffering large product shortages for some markets while others were stockpiling
• large storage costs and at the same time low β-service level

possibel fixes

• short-term
• Air shipments to quickly meet spikes in demand(expensive)
• increase safety stocks for problematic product-market combinations
• long-term
• DC localization
• Move final assembly to DC, allowing Europe to use the generic shipments to serve all markets with only a few days of lag
• european factory

HP optimization

• LT = 5 weeks (+1 review period)
• β-service level: 0.98
• COC: 30% → 0.005357 / week

$$r^* = \mu_ {LT} + L^{-1}\left(\frac{(1-\beta)\mu }{\sigma_ {LT}}\right) \sigma_ {LT}$$

$$r^* = 31887 + L^{-1}\left(\frac{(1-0.98)31887 }{7335}\right) 7335$$

$$r^* = 31887 + 0.98 \cdot 7335 \approx 39075$$

Barilla

Barilla was struggling with high variances in their retailer ordering patterns, a typical case of the bullwhip effect. Although the demand for pasta was largely constant with slight but clearly prognosable changes in certain seasons, the overall demand did not fluctuate. However, the three echelon supply chain caused Barilla to observe strong spikes of orders. Additionally, the sales team’s incentives were largely oriented towards overall sales volume and didn’t include avoidance of purchase spikes. Hence, sconti were given to the wholesalers, leading them to perform bulk purchases to make use of the cheaper rates.

Wholesalers expected quick deliveries but Barilla needed to follow a specific production plan to produce the different pasta types in good quality.

Solution approaches:

• JITD - just in time delivery
• integration of inventory systems with Wholesalers
• push instead of pull based ordering systems
• promises: increased service levels and decreased storage costs for Barilla products
• Advanced forecasting: Barilla implemented a sophisticated demand forecast tool and was able to precisely predict demands for different markets and regions

TODO

Behavioral Economics

Two systems of thinking

The human mind can be modeled as having two systems: The conscious, effortful system that is “single threaded” and requires our attention and the unconscious automatic and heuristics driven system that processes vast amounts of parallel streams of data but can hardly be controlled.

System 1: The conscious stream follows rules and can adapt to new tasks. It takes control force of will to continually focus on a certain topic without switching to other topics of interest.

The three components of the conscious system describe the state in which the system is and why it needs to be “controlled”. It requires physical, cognitive and emotional effort to steer and direct the train of thought.

System 2: The unconscious processing machine works with biases, associations, heuristics and is highly parallel. It tries to reduce complex data structures into comprehensible pieces of information that can be analyzed by the conscious stream.

Unfamiliar processes (such as driving a car) can at first require the conscious system to analyze and understand the actions required to perform the tasks. However, the repetitive nature of the activities quickly allow the unconscious system to take over the work.

Heuristics and Biases

Literature: Kahneman & Tversky (1974): Judgment under Uncertainty: Heuristics and Biases, Science, 185(4157), pp. 1124-1131.

Heuristics work by the individual quickly asserting certain structures or patterns based on a limited amount of input data. Often these heuristics are coupled with the need to predict or assert certain characteristics, e.g. a stock price change in the next weeks. There are many automatic rule-sets that most humans follow with their unconscious system which often lead to good results but can also lead to systematic errors.

Representative Heuristics

Choose between A and B, in relation to C. How much is A or B similar to C? The one that is more similar gets chosen. A simple example is “whose kid is this? Dark hair, dark skin, probably Michaels.” This heuristic makes sense in many contexts but can cause issues in others. If one gets a detailed explanation of a persona and then a list of jobs with the task to assign him to the most likely job he will be working in, most people choose the job whose representatives are regarded by culture as similar to the described persona. So a “successful, fit, mathematically interested, technical, adventurous, success driven, academic…” persona when having to select among “cashier, teacher, office clark, astronaut”, most people would choose the last. However, people show an insensitivity to prior probability of outcomes. What are the chances that anyone is an astronaut vs. these other very common jobs? Marginal at most. But people associate these descriptions with astronauts so the described persona is representative of the group and therefore considered most likely.

• people ignore overall probability of a certain situation being true if
• a description / situation is representative of one of the options even if
• the option is far less likely than all others in the base distribution.
insensitivity to sample size

Even professional Psychologists overestmiate the representative value of small sample sizes for a larger corpus. Overall small sample sizes are more likely to exhibit more extreme values on distribution scales.

Misconceptions of chance

The classic case is the gamblers fallacy. Chance doesn’t self correct, it just dilutes over repetition. Therefore after 5 heads at a coin toss, the next toss is still 50:50, no “it must be tails now”.

Insensitivity to predictability

Watch 3 couples for 20 minutes each then predict if they will still be together in 5 years from now: Most people would base their predictions on what they observed. But realistically, one knows nothing about the relationships so all predictions should be the same and all should be the average (whatever that is).

Illusion of validity

People think they are right even though they base their decision on flaky base data. Once they have decided, they stick to their decision with higher confidence than the base information is offering.

Misconceptions of Regression

Generally, there is a regression towards the mean. Although the gamblers fallacy is people expecting this regression, there are also cases in which it is misinterpreted. Let’s imagine students on average achieve a (B-) in school. If a student achieves an A/A+, a teacher will congratulate the student. Yet statistically, the next exam will be more likely to be closer to the average than another good grade again. On the other hand if a student got an F, the teacher will be critical. But statistically, the student will be more likely to get a better grade next time, independent on the criticism or compliments. In both cases, the teacher could in hindsight interpret: “congratulating makes my students write less good exams and criticism fosters good grades”. This would be detrimental to the goals.

Availability bias and examples

Those cases that are easier to recall are usually considered more likely to be true. Examples are

1. Retrievability: The ability to retrieve instances from memory 2) Effectiveness of a search set: Samples of words with r in 1. or 3. letter → 3rd is harder to “search” in the mind, e.g. less likely? false 3) Bias of imaginability: If one cannot imagine something, it is less likely considered to be true

One gets anchored on a number. Examples are:

• Bargaining on a market place in turkey
• Salary discussions and previous salaries

However, even when we are aware of what the anchor is (e.g. an average), we still do not adjust adequately.

Overconfidence, Choices and Frames

Literature:

• Kahneman & Tversky (1979), Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–292.
• Tversky & Kahneman (1992), Advances in prospect theory: Cumulative representation of uncertainty, J. Risk Uncertainty, 5(4):297–323.
• Thaler (1999), Mental Accounting Matters, Journal of Behavioral Decision Making, 12 (3), 183-206.

Prospect theory

2 Stage decision process:

1. Editing: Reference point defined.
• Better → gains
• worse → losses
1. Evaluation of potential outcomes based on function which can be seen as a utility function
• The function is asymmetrical, losses are dreaded more than gains are cherished (loss aversion)
• 50:50 chance of €1000 vs safe 500: ppl choose 500
• same in negative: People prefer risk: Risk seeking in losses, aversion in gains

• People are risk-averse in the gains and risk-seeking in the losses
Endowment effect

• Purchasing party considers products of less value than selling party

• → mugs experiment:

• What’s the mug worth? 5€. Here it’s yours. What’s the mug worth? 6€

Names+Years

• Bolton & Ockenfels - 2000: Motivated by their payoff standing in relation to other people, i.e. the fairness of their payoff. People feel more envious than guilty
• Behavioral Forecasting
• Özer, Zheng, Chen - 2011
• Kremer, Minner, Van Wassenhove - 2010
• Behavioral Inventory
• Schweitzer and Cachon - 2000
• Bolton, Katok - 2008
• Bolton Ockenfels, Thonemann - 2008
• Ho, Lim, Cui - 2010
• Behavioral Contracting
• Becker-Peth, Katok, Thonemann - 2013: How to construct buyback contracts optimally under the influence of the three variables: anchoring, loss aversion and TODO
• Katok, Wu - 2009
• Cui, Raju, Zhang - 2007
• Loch, Wu - 2008
• Ho, Zhang - 2008

Vocabulary / Terminology

• Newsvendor Model
• Order rationioning: manufacturer forecasts demand and produces according to his own expected demand. If demand is higher than production quantity, each retailer gets a proportionally reduced amount.
• Order batching: Coordinating multiple retailers by the manufacturer → setting delivery dates so that the overal output is continuous. Compare Barilla case
• Postponement: Move customization of products for different markets at end of supply chain to avoid complex supply chain structures
• Component commonality:idea of using standard parts that cost more per piece but reduce complexity. There is a point of equilibrium which equals out the savings in complexity costs for supporting many different variants vs. the increase in unit cost.
• Continuous Review Inventory Model
• Backorder level: The number of backorders and days where people need to wait. It doesn’t only capture the times when a order cannot be filled but also the time an order has to wait before it gets filled. Can be simulated by having a non-filled order re-request a product in each period until it is being served
• Economic Order Quantity Model
• Periodic Review Inventory Model
• Service Levels
• Availability and representativeness in Forecasting
• Anchoring in Inventory decisions
• Bounded Rationality
• Decision Support
• Framing of decisions
• Reference Points
• Mental Accounting
• treatment: particular condition of an experiment. Main/control treatment, different variations with adapted parameters, …
• Priming
• Risk- loss preferences
• Prospect Theory
• Probability perception
• The Wilcoxon signed-rank test is a non-parametric statistical hypothesis test used when comparing two related samples, matched samples, or repeated measurements on a single sample to assess whether their population mean ranks differ (i.e. it is a paired difference test).
• It can be used as an alternative to the paired Student’s t-test, t-test for matched pairs, or the t-test for dependent samples when the population cannot be assumed to be normally distributed.
• A Wilcoxon signed-rank test is a nonparametric test that can be used to determine whether two dependent samples were selected from populations having the same distribution.
• The Wilcoxon-Mann-Whitney Test is a nonparametric test of the null hypothesis that it is equally likely that a randomly selected value from one sample will be less than or greater than a randomly selected value from a second sample.
• Unlike the t-test it does not require the assumption of normal distributions. It is nearly as efficient as the t-test on normal distributions.
• A Wilcoxon signed-rank test is a nonparametric test that can be used to determine whether two dependent samples were selected from populations having the same distribution.
• Double marginalization: The effect of two distinct decision makers trying to optimize their individual margins without regarding the fact that their decisions also effect the other decision makers decision.