Is Artificial Intelligence (A.I.) Ready to Run a Factory?

With smart factory investment expected to increase 20% year-on-year over the next five years and total investment expected to reach $275 billion worldwide by 2027, the use of Artificial Intelligence (A.I.) to manage operations is receiving considerable attention. This paper takes an in depth look at how factory data is being generated, stored, processed, transferred, trained and ultimately validated using A.I. The conclusion is that deep machine learning is more than capable of controlling devices. Yet, research shows only 14% of smart manufactures would describe their A.I. efforts as successful. The problems are cost and application. Smart manufacturing is almost exclusively done by multi-billion dollar operations. Is this money well spent? Factories aren’t closed, linear systems. In these chaotic systems infinitesimal changes in any one of the myriad of input variables are capable of producing disproportionate changes in output values. As a result, no matter how much scrap, downtime, sales or on-time delivery data a company collects actual values will diverge exponentially from what existing A.I. algorithms are predicting. Until more research is done predicting dynamic, nonlinear systems A.I. will not be capable of running a factory without human involvement.

Crowdsourcing Last-Mile Deliveries

Problem definition: Because of the emergence and development of e-commerce, customers demand faster and cheaper delivery services. However, many retailers find it challenging to efficiently provide fast and on-time delivery services to their customers. Academic/practical relevance: Amazon and Walmart are among the retailers that are relying on independent crowd drivers to cope with on-demand delivery expectations. Methodology: We propose a novel robust crowdsourcing optimization model to study labor planning and pricing for crowdsourced last-mile delivery systems that are utilized for satisfying on-demand orders with guaranteed delivery time windows. We develop our model by combining crowdsourcing, robust queueing, and robust routing theories. We show the value of the robust optimization approach by analytically studying how to provide fast and guaranteed delivery services utilizing independent crowd drivers under uncertainties in customer demands, crowd availability, service times, and traffic patterns; we also allow for trend and seasonality in these uncertainties. Results: For a given delivery time window and an on-time delivery guarantee level, our model allows us to analytically derive the optimal delivery assignments to available independent crowd drivers and their optimal hourly wage. Our results show that crowdsourcing can help firms decrease their delivery costs significantly while keeping the promise of on-time delivery to their customers. Managerial implications: We provide extensive managerial insights and guidelines for how such a system should be implemented in practice.

An integrated, multi-criteria approach based on environmental, economic, social, and competency criteria for supplier selection

Supplier selection (SS), which is a critical stage of supply chain management (SCM), is no longer evaluated solely from the perspective of traditional criteria, due to increasing environmental problems and ethical concerns. In this study, a multidimensional approach including environmental, economic, social, and competency concepts was proposed to evaluate SS, and an integrated approach based on Best Worst Method (BWM) and fuzzy TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) techniques was developed. In the developed approach, BWM was used to first obtain the importance level of the criteria and then to rank the suppliers with fuzzy TODIM. The proposed approach was tested on an automotive company in Turkey. The outcomes of the analysis revealed that the most important main criteria for SS are economic (0.377) and environmental criteria (0.290). In addition, the sub-criteria with the highest criteria weight were listed as product cost (0.120), on-time delivery (0.112), quality control (0.094), logistics costs (0.069), management commitment (0.054), and flexibility (0.054). The results of this study offer theoretical and practical outputs to companies that want to improve their SS process, selection of suppliers, and contributions of the researchers working on the topic.

FACTORS AFFECTING ONLINE BUYING BEHAVIOUR OF CONSUMERS IN ABUJA METROPOLIS

The internet has undoubtedly become crucial to global growth as it vectors lots of opportunities. This study was motivated by the perception that Nigerian consumers prefer traditional over virtual shopping despite having internet access. This study sought to investigate the factors affecting the online buying behaviour of consumers in Abuja Metropolis. The study used purposive and convenience sampling technique to select 352 respondents. Primary data was collected with the aid of structured questionnaires. The data collected was analyzed using multiple regression analysis with the aid of the SPSS statistical tool. The study found that while on-time delivery has a positive and significant effect on the online buying behaviour of consumers in Abuja Metropolis, product quality was found to have a positive and insignificant effect on the online buying behaviour of consumers in Abuja Metropolis while the perceived risk was found to have a negative and significant effect on the online buying behaviour of consumers in Abuja Metropolis. The study concludes that on-time delivery, product quality, and perceived risk are the major factors affecting the online buying behaviour of consumers in Abuja Metropolis. The study, therefore, recommends that there is a need for companies utilizing online platforms for business operations to align the advantages of online shopping with traditional means of purchase, speed up the delivery processes, and as much as maintaining a high degree of confidentiality regarding their customer’s information during the process of online payment. Keywords: online buying behaviour, product quality, on-time delivery, perceived risk.

The Effect of Work Environment, OHS, and Job Satisfaction on Employee Performance

Intense competition in Indonesia forces private companies in the defense industry to be more competitive. PT. Z is one of the largest private defense products and equipment manufacturer in Indonesia. To retain competitiveness, PT. Z must provide defense products and equipment with the best quality and deliver it on-time. The quality and on-time delivery of products and equipment fulfillment rely on the performance of employee at PT. Z. Over the last few years, employee performance at PT. Z constantly decreased. The decrease of employee performance can be caused by several factors including work environment, occupational health and safety, and job satisfaction. This research will examine the effect of work environment (WE), occupational health and safety (OHS) and job satisfaction (JS) on employee performance (EP). The analysis was carried out using the PLS-SEM with 120 samples taken from production and engineering division at PT. Z. The result shows that there are positive and significant effect of WE on EP, WE on JS, OHS on JS, and JS on EP. JS also mediates the effect of OHS on EP. On the other hand, OHS does not have a direct effect on EP and JS cannot mediate the effect of WE on EP.

PRODUCT AND SERVICE QUALITY DEVELOPMENT IN MANUFACTURING: A STUDY OF OPTICAL LENS MANUFACTURING INDONESIA

<p class="Badan"><em><span lang="EN-US"> </span></em><span lang="EN-US">For optical lens manufacturing the development of product and service quality is a key thing in answering challenges in the business competition it is developing. To achieve this determine the Key Performance Indicator (KPI), the Rejected Rate and On-Time Delivery (OTD) to measure the extent of the performance that has been achieved and how to develop it, shape the work culture of Kaizen which commits to continuous improvement and value chain analysis largely determine the process of developing production and service quality. The Kaizen culture and value chain applied in achieving the KPI target in this study succeeded in reducing the KPI Rejected Rate by 0. 46% and raise OTD by 2. 22%. The method used in this study uses a review of literature studies, observations, and direct interviews of the plant manager of one of the largest optical lenses manufacturers in Indonesia using data comparisons in 2018 and 2020.</span></p>

Conception and Development of a Last Mile Vehicle for Urban Areas

In megacities, increasing globalization effects are leading to rapidly increasing prosperity and augmented purchasing power, and thus to a growing need for punctual, cost-effective, and environmentally friendly delivery of goods. A smart, small electric vehicle concept is presented that targets on meeting the requirements for the delivery of goods in urban areas and that is designed especially for the delivery on the last mile. This last mile vehicle (LMV) for cargo transportation is attached to a truck. Whenever it is needed, for example to deliver goods into narrow streets, in pedestrian areas or in case of traffic jams, it can be unfolded and unloaded from the truck and hereby guarantees a flexible and punctual delivery of goods. This flexible on-time delivery is possible because the last mile vehicle is designed, so that the legal regulations of the non-motorized vehicle lane, that is everywhere to be found in Asia, are met. The vehicle is designed with three wheels, a range of 40-60 km and an electric drive train with a continuous power of 2 × 250 W that enables a maximum speed up to 40 km/h of the vehicle. The drive train consists of a battery pack that can be charged electrically from the truck, two inverters, and two electric wheel hub motors. The LMV has been designed and constructed as a prototype and has been tested on non-public roads to prove the vehicle concept. For Europe, it can be classified as an L2e vehicle and with slight modifications; it can be applied on European roads as well.