1 Bachelor Thesis on the topic of: How can Predictive Shipping be successfully implemented for Online Retail? Simulati ng the profitability of Predictive Shipping on a n exemplary distribution network 2 Table of c ontents Introduction ................................ ................................ ................................ ................................ ..... 3 Theoretical Background ................................ ................................ ................................ .................. 4 Overview ................................ ................................ ................................ ............................. 4 Opportunities and Challenges ................................ ................................ .............................. 8 Opport unities ................................ ................................ ................................ ........... 8 Challenges ................................ ................................ ................................ ............. 16 Implementation methods and standings ................................ ................................ ............ 21 Examples ................................ ................................ ................................ ............... 21 Current Progress ................................ ................................ ................................ .... 22 Research Method ................................ ................................ ................................ ............... 24 Problem Description ................................ ................................ ................................ ...................... 25 Predictive Shipping Simulation ................................ ................................ ................................ ..... 27 Model ................................ ................................ ................................ ................................ 27 Prediction Algorithm ................................ ................................ ............................. 27 Order Simulation System ................................ ................................ ....................... 27 Monte Carlo Simulation ................................ ................................ ........................ 28 Results and managerial implications ................................ ................................ ................. 31 Conclusion and future outlook ................................ ................................ ................................ ...... 33 Declaration of originality/Statutory declaration ................................ ................................ ............ 33 Reference list ................................ ................................ .................. 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Table of figures ................................ ................................ ................................ .............................. 38 3 Introduction The modern Supply Chain is undergoing enormous transformational changes (Alicke et al ., 2016, p. 1) This can be seen both in terms of global trends such as the “ [...] maturation of globalization, the exponential rise of e - commerce, the constant threat of technology disruption, and, most recently, the coronavirus pandemic [...] ” (DHL Customer Solutions, 2020) as well as new technological advancements such as “ cloud computing , collaborative robotics, big data analytics, artificial intelligence, and the Internet of Things [...] (DHL Customer Solutions, 2020) Increased costs, individual preferences and supply chain c omplexity are the top external trends putting pressure on Supply Chains (BVL, 2017, p. 1) Furthermore, today c ustomers demand faster deliveries (Ericsson Industrylab, 2020, p. 3) and increased traceability of their orders to have a detailed understanding of when the ordered product arrives . As Ericsson describes: “ These expectations are also evident in the B2B sector; of the companies surv eyed, 88 percent say that their customers currently request same - day delivery ” (Ericsson Industrylab, 2020, p. 3) Faster deliveries are in many parts due to the revolutionary way Amazon has changed customer expectations: “ [...] now everyone wants everything within two days, or the next day , if they’re in the US. Amazon came in and changed the game for everyone. ” (Ericsson Industrylab, 2020, p. 3) It thus becomes evident that the speed of your logistics is an essential competitive advantage to attain and retain more customers (Zhalgassova, 2014, p. 1) Customers expect fast delivery after order placement . T hus delivery performance is critical to enhancing the customer shopping experience in omnichannel commerce. (Lee, 2017, p. 593) . To put it simply, whoever can deliver first, will be preferred by the customer (Zhalgassova, 2014, p. 1) As E - Commerce sales are increasing worldwi de (Fig. 1), there is now growing pressure to deliver more products faster , and solutions have to be found. Figure 1 : Retail e - commerce sales worldwide from 2014 to 2023 (Simmons, 2020) O ne of the most innovative and critical technological advancements in supply chain management is a mixture of Big Data Analysis and Artificial Intelligence in Predictive Analytics My research 4 focuses on how a subset of Predictive Analytics , Predictive Shipping , which focuses on sending a product near you before you ordered it, could be implemented in real life to combat the Issues above During my research, I have observed a lack of research concerning the actual implementation of Predictive Shipping . T herefore, t his thesis aims to show the current research on Predictive Shipping and then create a model to simulate Predictive Shipping in the real world. The goal is to understa nd whether Predictive Shipping can improve the delivery process and maintain profitability. I will first explain what Predictive Shipping entails and the opportunities and challenges that impact Predictive Shipping . Then, I will show how Predictive Shippin g is currently being used and what progress has been made. Next, I describe my research approach and the exact problem in Predictive Shipping that I attempt to solve. Afterward , I will demonstrate my model to simulate Predictive Shipping and analyze the re sults Finally, I will give a conclusion and future outlook for both technology and research. Theoretical Background Overview As mentioned previously, Predictive Shipping is a subset of Predictive Analytics , which is defined as follows: “ Predictive Analytics is a set of business intelligence (B I ) technologies that uncover relationships and patterns within large volumes of data that can be used to predict behavior and events. Unlike other B I technologies, Predictive Analytics is forward - loo king, using past events to anticipate the future. ” (Eckerson, 2007, p. 5) Instead of asking “ What happened? ” like descriptive analytics or “ Why did it happen? ” like diagnostic analytics, Predictive Analytics try to predict “ What will happen? ” in the future (compare Fig. 2 ) Predictive Analytics is a substantial field used in many other different industries, especially healthcare and finance Predictive Analytics and the application of artificial intelligence hold enormous potential for optimizing logistics processes. (Kersten et al. , 2017, p. 14) Figure 2 : Predictive Analytics in comparison (AIMultiple, 2019) 5 T here are many subsets of Predictive Analytics in supply chain management, such as P redictive M aintenance, Predictive P icking& P acking or Predictive Shipping The “Bundesvereinigung Logistik” describe s Predictive Analytics as a highly relevant current techno logy already being implemented (compare Fig. 3 ) Figure 3 : A graph depicting t he current standings and relevance of modern technology trends. (BVL, 2017, p. 4) One key aspect of all these subsets of Predictive Analytics is that they allow for a completely different approach from traditional analytics . As a blog from SupplyChain24/7 puts it: “ Without predictive analytics, you’re planning under the assumption that the past will repeat itself, instead of building a data vision and creati ng a model for the future.” (SupplyChain24/7, 2021) Thus Predictive Shipping uses current data, allowing for better planni ng of, for example, Vehicle Capacity or Inventory Management in the future. 6 It is thus not surprising that a recent survey of 621 logistic providers found Predictive Analytics to be the 3 rd most invested technology , with only a small margin of a few % to the most popular technology cloud logistics (compare Fig. 4) . Predictive Analytics and their associated subsets have future potential and are thus very relevant in the modern landscape. Figure 4 : Most essential technology investme nts (Simmons, 2020) Predictive Shipping , also known as A nticipatory S hipping, originates from the patent US8615473B2 , which Amazon filed in 2012 and then approved in 2013. Their most common definition in the patent states: “ According to one embodiment, a method may include packaging one or more items as a package for eventual shipment to a delivery address, selecting a destination geographi cal area to which to ship the package, shipping the package to the destination geographical area without completely specifying the delivery address at the time of shipment, and while the package is in transit, completely specifying the delivery address for the package. ” (Spiegel et al. , 2013) The way it works is quite simple in theory. The description in the patent (visible in F ig. 5 ) shows how products get sent to a hub close to you before an order is made. If the order is made during this process, the vehicle is rerouted dynamically to your address. Besides dramatically lowering delivery times, Predictive Shipping could reduce shipping costs, as heavily demanded items can be sent via standard shipping beforehand over expedited shipping (Kundu and Tripti, 2021, p. 1) As Last - Mile Delivery is still the most expensive part of supply chains, Predictive Shipping could thus be a very profitable innovation (DHL Customer Solutions, 2020, p. 51) 7 Figure 5 : How Predictive Shippi ng works (Spiegel et al., 2013) There is another application of Predictive Shipping that could be applied in more fringe conditions and has unique use applications. While the one I just described focuses on identifying significant trends from personal preferences and then shipping the goods from around the world as close to you as possible, the other use case involves sending it directly to you. The full text describing this in the patent states: “ After determining the potential cost of returning or redirecting the near proximity package, the package is provided to the potentially interested customer at a discounted price, where the discounted pri ce depends on the determined potential cost of return or redirection. For example, rather than incur the possible cost of returning or redirecting a package without a sale, some or all of the potential cost may be offered as a 8 discount to a potentially int erested customer, such as via an e - commerce portal, as an inducement to convert the potential interest into an order. In some instances, the package may be delivered to a potentially interested customer as a gift rather than incurring the cost of returning or redirecting the package. For example, if a given customer is particularly valued (e.g., according to past ordering history, appealing demographic profile, etc.), delivering the package to the given customer as a promotional gift may be used to build go odwill. (Spiegel et al. , 2013) For this thesis’s sake, it is vital to differentiate between the Predictive Shipping used to deliver goods to hubs closer to you and having goods delivered directly. In the following passages, I primarily focus on the latter; however, statistics describing Predictive shipping will refer to the former, which is far easier to implement in the real w orld, with much less controversy. The current response to this very innovative technology was mixed Many believe in its great potential and state: “ If this gets good customer response, Amazon will achieve a competitive advantage in shipping time. Few othe r online merchants have the resources to match Amazon ’ s logistics capabilities ” (Kaplan, 2014) A blog by SmartData Collec tive goes as far as to say that: “ If it does work, it will significantly alter the landscape and encourage many other retailers and e - commerce brands to develop something similar.” (Nichols, 2018) H o wever, there are many critical voices due to the many challenges facing Predictive Shipping Can Predictive Shipping be implemented in the real world? To further understand Predictive Shipping and structure my findings, I analyze the potential o pportunities and c hallenges Opportunities and Challenges Opportunities Speed of Delivery The main benefit of a successful imp lementation of Predictive Shipping is obvious First, t his technology could reduce transportation time and thus also transportation costs. Second, c ustomer satisfaction could increase immensely by having their desires met faster Quick delivery speeds are critical as, according to Fig. 6 , only 1% of all cross - border bought online products had same - day shipping, and only 3% had next - day shipping. Yet, there is a need for faster deliveries in the population. For example, 65% of 314 l ogistics companies mentioned needing to increase delivery options, and 49% focused on higher speeds for their last - mile deliveries. (compare Fig. 7 ) If Predictive Shipping was used globally, trends could be deduced early and products shipped to places of high demand way before demand occurred, drastically reducing transportation times. If one imagines the COVID - 19 pandemic, for example, if a need for masks had been identified beforehand, then shipments from around the world, especially China, could have ar rived far earlier in Europe and saved many lives. Similarly, newly emerging trends such as the Fidget Spinner in 2017/2018 could have been identified beforehand. As those goods can rise and drop extremely quickly in demand, quick transportation times and e arly deliveries across borders are vital to match demand and manage stock. (Gesing et al., 2018, p. 25) Transmetrics describes this process as follows: “ By us ing 9 predictive solutions to generate supply and demand forecasts, companies will be able to make the right operational decisions proactively. This approach can also allow for the rebalancing of assets across any logistic network at a minimal cost “ (Transmetrics, 2019) Faster, more accurate deliveries with fuller trucks could also reduce carbon emissions and increase the sustainabili ty in the last - mile delivery. Figure 6 : Cross border delivery date (Simmons, 2020) Figure 7 : Most crucial last - mile delivery areas (Simmons, 2020) 10 Competition with Brick - and - Mortar Stores E - Retailers can use Predictiv e Shipping to compete with different physical retailers , such as brick - and - mortar stores. As the GTG Technology G roup puts it on the example of Amazon: “ Amazon ’ s objective is to more effectively compete with brick and mortar retailers that can better satis fy customer needs for instant gratification (GTG Technology Group, 2015, p. 10) ” In addition, the se stores can have close interactions with customers and purchase products within minutes (Kaplan, 2014) Therefore, a faster d elivery performance is critical in enhancing the customer shopping experience (Lee, 2017, p 593) and increasing customer retention. The h igh amount of returns There is currently an issue with a large number of returns and destroyed items for E - Retailers , which Predictive Shipping might help . A recent report shows: “ Online giant Amazon is destro ying millions of items of unsold stock every year, products that are often new and unused, ITV News can reveal. ” (Pallot, 2021, p. 1) The destruction of perfectly useable items leaves much room for more potential. Of the 130.000 items destroyed per week, 50% are still unopened, while the other 50% are returned items in good conditions (Pallot, 2021, p. 2) It is essential to mention that this was not a case exclusive to Amazon. Purdy shows : ”In France, a TV station found more than 3 million products destroyed in 2018. In Germany, one warehouse sends out a truckload every week for destruction. And those are j ust the stories that rose to the surface”. (Purdy, 2021, p. 3) The reasons for these actions are simple: “ It is eventually cheaper to dispose of the goods, especially stock from overseas , than to continue storing the inventory. ” (Pallot, 2021, p. 2) Labarre further explains: “ Most objects in financial accounts are classified as stock, a type of asset. Stocks depreciate over time, until ultimately – when the cost of storage outweighs potential return – that un sold stock becomes a liability. Organizations can choose how to dispose of their liabilities but, in balance - book terms, the stock has become waste: worthless objects to be discarded (in the cheapest possible way)”. (Wishart, 2021, p. 2) As Pallot state s, these actions have been heavily criticized: “Products that were never sold or returned by a customer. Almost all could have been redistributed to charities or those in need. Instead, they are thrown into vast bins, carried away by lorries (which we tracked), and dumped at either recycling centers or, worse, a landfill site. (Pallot, 2021, p. 2) (see Fig. 8) Figure 8 : Unopened Products about to be destroyed (Merchant Fraud Journal, 2021) 11 R eturns have been an increasingly significant Issue for E - Retailer s, as numbers keep increasing. A report by Amazon showed: “ In today ’ s retail climate, returns account for 50 percent of items purchased in the U S alone — and amount to a cost of $350 billion a year, with 5 billion pounds of clothing and textiles winding up annually in U S landfills. ” (Amazon Staff, 2021, p. 3) The report concludes with the statement: “Customer returns are a fact of life for all retailers, and what to do with those products is an industry - wide challenge,” said Libby Johnson McKee, director of Amazon W W Returns, ReCommerce, and Sustainability. (Amazon Staff, 2021, p. 2) T his increase in returns is in large part due to the: “ Increased e - Commerce adoption, driven by CO VID shutdowns, leads to higher overall return rates because digital purchases are more likely to be returned store purchases ” (Blair, 2020, pp. 1 – 2) However one cannot simply remove returns to save money as “ return policies and experiences have a significant impact on customer loyalty. In a survey of consumers conducted by Doddle, 84% said a positive returns experience encourages th em to shop with a retailer again. Conversely, 73% of consumers responding to a survey by Returnly said they would not shop with a brand again after a poor returns experience.” (Blair, 2020, p. 2) Return policies ar e also mandated by law: “ Many countries require online retailers to give their customers the right to return products within a certain period of time after purchase. For instance, the minimum required by law in all member states of the European Union is 14 days.” (Urbank P et al., 2015, p. 2) A lthough a well - managed return system can improve sales and the customer experience, nowadays, the problem gets exaggerated by an expectancy of free returns. The Wharton University found that : “ Consumers are coming to expect they can return things and that it will be easy. They’re coming to expect that returns will be free, that when something is delivered to them, there will be a return label or return envelope for sending things back. There is this major upward trend in terms of consumers returning and expecting to return.” (Wharton University of Pennsylvania, 2020, p. 1) . 67% of online customers expect free returns. If they had to pay for returns, they would decrease their subseque nt spending with the same retailer by 75 - 100% by the end of 2 years, whereas customers who had free returns increased their spending by 158 - 457%. (Jack et al. , 2019, p. 3) Most companies are not equipped to deal with a large number of returns. As a result, transparency is low, and returns are not being managed correctly. For example, a report by the ECR shows: “Of the 10 retail clients understudy, all were found to be underestimating their return rate – one by as much as 150 percent, wi th an average return rate discrepancy of over 80 percent ...resulting in additional costs of over $462 million to a retailer doing $10 billion in annual revenue.” (Jack et al. , 2019, p. 3) A bit further in the report, they continue: “a s urprising 39% of respondents said they have no visibility into returns — they just show up. A similar number (38%) receive scheduled reports, and 32% track point of sale information at their returns center. More surprising, 40% of respondents couldn’t determ ine how much reverse logistics saved their company, and another 36% aren’t sure. Finally, only 24% said they were able to determine how much their reverse logistics operation is saving their company, with an estimated average annual revenue savings of 16.5 %.” (Jack et al. , 2019, p. 7) Finally, the last i ssue with returns concerns fraudulent returns. They increase unsold merchandise, create losses, and affect many products in Predictive Shipping. Customers and companies have vastly different expectations for what a legitimate return contains (Wharton 12 University of Pennsylvania, 2020, p. 3) People use “ Faulty ” or “ Change of mind ” return categories to give back a TV aft er a tournament or Lawnmowers at the end of summer, effectively “ renting ” them. (Jack et al. , 2019, p. 11) The result is fraudulent returns : “ For eCommerce companies, an astonishing 14% of returns are fraudulent. This shows that w hile generous returns policies are important for customer satisfaction, it is important for company profitability to make returns barriers high and be tough in enforcing them” (Jack et al. , 2019, p. 4) Fraudulent returns are an increasing ly significant Issue. Skapa reported in 2013 that: “According to the survey carried out among 111 retailers in the US, the estimated loss caused by return fraud increased from $9.59 billion to $13.95 billion.” (Škapa, 2013, p. 380) Today this figure has risen even higher: “Consumers return ed an estimated $428 billion in merchandise to retailers last year, approximately 10.6 percent of total U.S. retail sales in 2020. Of those returns, roughly 5.9 percent were fraudulent, equating to $25.3 billion, according to a report released today by the National Retail Federation and Appriss Retail.” (NRF, 2021) (compare Fig. 9 ) Figure 9 : Merchandise Return Fraud (Merchant Fraud Journal, 2021, p. 2) T his issue, in particular, is very complicated and severe with no one - size - fits - all solutions. For the sake of this thesis, I would propose that simply mor e accurate predictions would have to be made, which also include the risk of fraudulent returns. Besides including previous purchasing behavior and demographics, a proposal by Urbank et al. seems the best to me. They propose: “a system of prediction and ta rgeted intervention that can identify consumption patterns associated with an extremely high rate of product returns and prevent such transactions from taking place. Such consumption patterns might be related to fraud or impulse shopping. “ (Urbank P et al., 2015, p. 2) However, in the end, multiple different solutions and policies will have to be used to minimize the problem. T he question is, how does one tackle a ll these significant challenges and turn them into an Opportunity ? One solution might be a new program proposed by Amazon themselves: “Amazon has launched two new Fulfillment by Amazon (FBA) programs designed to make it easier for its third - party sellers to resell customer - returned items or overstock inventory while also giving more products a second life.” (Amazon Staff, 2021, p. 1) This reselling gives one hope of a circular economy, where products get reused instead of destroyed. “Th ese new programs are 13 examples of the steps we’re taking to ensure that products sold on Amazon — whether by our small business partners or us — go to good use and don’t become waste. Along with existing programs like FBA Donations, we hope these help build a circular economy, maximize reuse, and reduce our impact on the planet.” (Amazon Staff, 2021, p. 2) Amazon has high hopes for this program as: “once fully rolled out, the company expects these programs to give more than 300 million products a second life each year.” (Amazon Staff, 2021, p. 1) Although this program seems very promising, I believe that Predictive Shipping could also significantly impact decreasing destroyed inventory and help m anage the increase in returned goods The idea is that Predictive Shipping could induce demand and sell products at heavy discounts before the product gets thrown away. Thus, the high number of returns becomes an opportunity to implement Predictive Shippi ng on less wanted goods. Amazon states in the Predictive Shipping patent that products shipped through Predictive Shipping could be offered at a significant discount or as gifts to build goodwill. This could dramatically reduce the number of destroyed item s and returns. Instead of being destroyed , these items now have another chance of a resell. I strongly support Predictive Shipping in combination with returned items because predictively shipping an utterly new item might result in more losses, as it might turn out to be a “lost sale” which was given at a higher discount for no reason. This issue does not exist with items that technically no longer have a value associated with them. Another report strengthened the possibility of gifting being used in Predic tive Shipping to reduce costs and improve returns. “For example, shipping products back in some instances costs more than a retailer could recoup through vendor credits, liquidation, or resale. Suppose customer service representatives have that cost data o n hand and can combine it with the purchasing and returns history of the customer. In that case, they could be allowed to tell the customer to keep the product, not send it back.” (Blair, 2020, p. 4) Predictive Shi pping could be used to create demand and thus reduce inventories of unsellable items, effectively becoming a part of the reverse logistics ecosystem. “(...) pioneering companies (...) identify (...) reverse logistics as more than what amounts to disaster remed iation, and see it as a way to improve cost savings, customer satisfaction, profitability, environmental viability, and ultimately competitive advantage and profits.” (Jack et al. , 2019, p. 18) Marketing f unction&I nternet o f thin gs Adopting Predictive Shipping might mean getting the early adopter benefit Predictive Shipping is a way to differentiate yourself from the market and offer same - day shipping in a world where free two - day shipping is becoming more and more of an industry standard. Predictive Shipping could be seen as an innovative, revolutionary method that further incentivizes people to choose Amazon over competitors, even if it turns out to be a loss leader (GTG Technology Group, 2015, p. 13) One of Amazon’s strengths has always been its focus on customer experience and shopping convenience. Due to the difficulty of using Predictive Shipping on a large scale, Amazon could be a market leader for a decent time. Amazon would not only be able to i mplement one or two - day shipping on a more significant number of products but even reduce those times to within hours or before a purchase was made. 14 Besides the dominant market position, there are multiple marketing - related benefits associated with Predict ive Shipping. A blog on Medium explains: “ Predictive Analytics can help e - commerce businesses drive targeted promotions to their customers by closely analyzing campaigns that have worked well in the past. Advertisements can be offered to relevant customer segments in real - time to encourage customers to complete a purchase or retrieve an abandoned shopping cart. ” (IQLECT, 2018, p. 3) To make those recommendations more subtle, one can mix accurate suggestions with other tangentially related items (Hill, 2012, p. 4) A multitude of methods to bring recommendations closer to your home already exist s . In the example of Amazon, it would be Amazon’s Alexa platform, which allows you to order products automatically or on a schedule. (Nichols, 2018) However, Alexa co uld be used to gain even more data. As the H arvard Business Review (HBR) describes: “ One could also use Alexa to order your parent a bouquet when you remember that her birthday is next week. ” (HBR, 2016, p. 2) Thus , the HBR concludes: “ intelligent assistants and connected devices will learn from user habits and pick up on behavioral and environmental patterns to make these experiences more predictive. Devices like the Echo will access data from everyday interactions to predict specific opportunities for a transaction. ” (HBR, 2016, p. 2) The internet of things could be a driving force to increase prediction certainty. Thus predictions for common household goods can be made with high certainty (PYMNTS, 2019, p. 2) Other ways to achieve more accurate predictions include using Predictive Shipping on trendy goods, where the chance of a misprediction is lower (smartphones), and very time - sensitive items, where t he immediate shipping might be more appreciated (best seller books) (Zhalgassova, 2014, p. 1) The Adoption of Predictive Shipping may be more straightforward if one implements it for people who are already devoted customers and incorporated in the e cosphere and environ ment of the company Kaplan described this by bringing up the example of Amazon Prime: “ Amazon might choose to limit Predictive Shipping to its Prime customers who pay an annual fee for free shipping. The company could use this as an added benefit for Prim e subscribers, especially if Amazon raises Prime annual subscription fees as it stated it might. ” (Kaplan, 2014) . Weingart en mentioned in their description of Predictive Shipping that: ” Model application could also be limited to customers with high order frequency to reduce the cost of erroneously sending products, as results indicate that those customers are easier to predi ct.” (Weingarten, 2020, pp. 8 – 9) F urthermore, gifting products could be used as a marketing tool to retain customer loyalty. “[...] a provider could improve customer loyalty and push its products into the market. Attaching this service to selected products would incentive customers to purchase critical stock items such as over - stock, perishable or phase - out goods” (CAMELOT Blog, 2019, p. 1) Instead of having items be destroyed, the invento ry could be decreased ahead of time through Predictive Shipping if sales targets weren’t met. Amazon has tried gift programs in the past as trial runs in the form of Amazon samples (Fuchs, 2019, p. 1) It is unknown whether this was a trial for Predictive Shipping systems or whether it was evaluated as unprofitable. It was very similar to how Predictive Shipping would work for the cu stomer in real life and was temporarily used as a marketing tool for advertisement and to generate customer goodwill. Amazon samples could 15 have been used as an automated tool for soon - perished items. Extensive marketing campaigns using this method could h ave sold items directly before they became unfit. (Fuchs, 2019, p. 3) Overall Predictive Shipping might mean a considerabl e incre ase in comfortability. Kundu and Dhote conclude: “This strategy, if implemented well, would increase the base of loyal customers, as they are in this process outsourcing their purchases and making themselves free of the purchasing hassles and it is a win - w in situation for the Customers and the E - Commerce companies.” (Kundu and Tripti, 2021, p. 8) Implementation of AI To implement Predictive Shipping in the real world, there are many prerequisites. One of them is the know - how and application of Big Data Analysis (BDA) and A rtificial intelligence (A I ). These are both trends that are very well suited to the Retail and Transportation Industry, as there a re is usually a vast amount of high - quality information readily available. (Gesing et al., 2018, p. 14) . The DHL reports: ” Predictive logistics remains the mo st critical AI application for industry professionals, given the abundance of supply chain data, as well as better machine - learning algorithms from which to draw predictive insights.” (DHL Customer Solutions, 2020, p. 51) W hat exactly is meant under BDA and AI? According to Weingarten: “BDA involves the application of advanced analytics techniques, such as statistics, simulation or optimization, to gain insight from big data to enhance decision - making and increase business value and firm performance” (Weingarten, 2020, p. 1) AI has many definitions and is an incredibly vast field with many different applications, however for the sake of this thesis, it will simply be a tool to improve the efficiency of BDA. As both these trends are on a steady rise, it is to be expected that the importance of Predictive Shipping increases soon. The benefits of BDA have already been shown: “businesses that already use BDA report a 5% increase in productivity and 6% incre ase in profitability, compared to those that do not” (Weingarte n, 2020, p. 1) , and “ applying AI to supply chains will result in $1.4 trillion of value in the next 20 years ” (DHL Customer Solutions, 2020, p. 51) The GlobalTradeMagazine reports: “ Over 90 percent of 3PLs and shippers believe data - driven decision - making is essential to supply - chain activities” (GlobalTradeMagazine, 2016, p. 1) A study by the Council of Supply Chain Management Professionals showed that: ” [...] 93% of shippers and 98% of third - party logistics firms feel like data - driven decision - mak ing is crucial to supply chain activities, and 71% of them believe that big data improves quality and performance (Transmetrics, 2019) D HL, in combination with IBM, found that: “AI can help the logistics industry to redefine today’s behaviors and practices, taking operations from reactive to proactive, planning from forecast to prediction, processes from manual to autonomous, and ser vices from standardized to personalized.” (Gesing et al ., 2018, p. 15) They concluded that “companies, deciding not to adopt AI run the risk of obsolescence i n the long term, as competitors seize and effectively use AI in their business today” (Gesing et al. , 2018, p. 14) Predictive Shipping thus has clear future p otential and is a natural evolution of current logistics. Figure 10 shows how Pre - Emptive or Predictive Logistics are the last step in the development path. 16 Figure 10 : Development Path necessary for Predictive Shipping (Ericsson Indus trylab, 2020, p. 7) Although these trends are on the rise, there hasn’t been widespread adoptio n. “Kersten found: To date, nearly half of the companies we surveyed have not planned to transform their business models digitally yet.” (Kerst en et al. , 2017, p. 15) C hallenges Costs The most critical challenge s that Predictive Shipping could face are related to increased costs. The Camelot blog says: “[...] the price pressure is becoming more intense , and this service can potentially cost a great deal, as unsold products must be returned and re - handled. (CAMELOT Blog, 2019, p. 1) If a previously unsold product is returned once again, the problem is only exacerbated even further. One needs to be very accurate in their predictions as else: “It would be entirely pos sible for Amazon to fudge things up and send a huge shipment of a product or item to an area where it’s not in demand. This is undoubtedly risky and could spell disaster in some instances — namely if the goods involved are time - sensitive or perishable. ” (Nichols, 2018, p. 2) Making accurate predictions and reducing returns is especially important as we previously established that cost pressure is one of the most relevant current trends. Price sensibility and transparency are incr easingly putting pressure on more efficient deliveries. (Kersten et al. , 2017, p. 20) If Predictive Shipping fails to improve profitability, it will not be easy to implement. 17 A lthough there are established ways to improve the models throu gh Big Data Analysis , targeted marketing, and implementation of assistant technologies, there is always a chance of misprediction. “ I t’s possible that Anticipatory Shipping could be wrong much of the time. While Big Data and predictive analysis have certai nly come a long way, a computer cannot predict what a human wants with 100 percent accuracy. This could result in logistical nightmares and confusion if the shipping system isn’t as accurate as first anticipated.” (GTG Technology Group, 2015, p. 9) Wrong predictions could not only reflect in lost sales and returns but also Issues for Inventory Management, as wrongly sent items could result in stock insufficiencies (Weingarten, 2020, p. 9) Lastly, implementing this technology has many prerequisites and could be unrealistic for less advanced retailers such a s Amazon. “For retailers with few warehouses and a widespread customer base, resulting in long transportation time, Predictive Shipping could be tough to implement.” (Weingarten, 2020, p. 9) Implementing Predictive Shipping would first mean vastly improving the efficiency, capacity, and number of existing warehou ses and customers. However, this can be a dangerous and costly plan for many, as the DVV Media Group found out in a study: “ [...] one - third of those surveyed, rate digital transformation as carrying high to very high risk.” (Kersten et al. , 201 7, p. 14) However, if all the issues are solved, Predictive Shipping could have a positive benefit on costs. Kundu and Tripti describe: “Predictive Shipping would enable the E - Commerce companies to reduce the costs and optimize the logistics and suppl y chain to a great extent , as now they would be independent , compared to previously when they were able to rely on a very limited number of shipping companies for timely deliveries. (Kundu and Tripti, 2021, p. 8) S ocial Aspect & worries N ot only does one need accurate predictions to lower costs, but one also has to send the correct type of items. Plenty of examples of incorrect examples will be shown in the next section. Predicting purchases could creep your customers out, as it feels like an invasion of privacy, resulting in losing customer s (Hill, 2012, p. 3) An example of that will be given down below. The loss of customers could be very costly as “Acquiring a new customer is almost always more expensive than retaining an existing one (SmartD ata Collective, 2017) Fear due to a lack of privacy might mean that the technology has to be run in trial runs first or with a more technology - friendly selected target audienc e The practical application could also result in many issues if a product were to be gifted while unattended; theft and piracy could run rampant. A good example is an Issue called Porch pirates in the USA. “ Suburban areas have their challenges, such as the fear of “porch pirates” – thieves stealing parcels left by the door when no one is home to accept them. (Ericsson Industrylab, 2020, p. 5) . Theft poses a serious challenge, as Stickle et al. describe: “package theft is an emerging crime type due to the tremendous growth in online shopping and the delivery of good s directly to a home. Unattended delivery creates an opportunity for thieves to steal packages after delivery and before the resident collects them. It is believed that these types of incidents are