on 18-May-2020 (Mon)

Organ podatkowy przypomniał, że w postępowaniu kontrolnym lub podatkowym jako dowód należy dopuścić wszystko, co może przyczynić się do wyjaśnienia sprawy, a nie jest sprzeczne z prawem. Zasada ta wynika z art. 180 ordynacji podatkowej. Przepisy wskazują także, że dowodami w postępowaniu podatkowym mogą być w szczególności:

• księgi podatkowe,
• deklaracje złożone przez stronę,
• zeznania świadków,
• opinie biegłych,
• materiały i informacje zebrane w wyniku oględzin,
• informacje podatkowe oraz
• inne dokumenty zgromadzone w toku działalności analitycznej Krajowej Administracji Skarbowej, czynności sprawdzających, kontroli podatkowej lub kontroli celno-skarbowej oraz
• materiały zgromadzone w toku postępowania karnego albo postępowania w sprawach o przestępstwa skarbowe lub wykroczenia skarbowe.

An alloy is a mixture of a metal

When iron corrodes, it reacts with oxygen and water to produce rust.

Ustawa nowelizująca podwyższyła w ustawie o PIT oraz ustawie o CIT limit odpisów amortyzacyjnych z tytułu zużycia samochodu osobowego zaliczanych do kosztów uzyskania przychodów:

• z 30.000 euro do kwoty 225.000 zł - w odniesieniu do samochodów elektrycznych,
  
• z 20.000 euro do kwoty 150.000 zł - w odniesieniu do pozostałych samochodów.

Podatnik nabył na własność dla celów prowadzonej działalności gospodarczej samochód osobowy (niebędący samochodem elektrycznym) za kwotę 200.000 zł. Odpisów amortyzacyjnych dokonuje według 20% stawki (okres amortyzacji wynosi 5 lat).

W 2019 r. roczny odpis amortyzacyjny wyniósł 40.000 zł, ale do kosztów podatkowych podatnik zaliczył kwotę 30.000 zł (w wyniku zastosowania limitu 150.000 zł). Kwota 10.000 zł (różnica między rocznym odpisem amortyzacyjnym od kwoty 200.000 zł a rocznym odpisem amortyzacyjnym od kwoty 150.000 zł) nie stanowiła kosztu uzyskania przychodu.

The survey revealed that transportation and logistics, automotive, and technology companies are at the forefront of AI adoption, while process industries (such as chemicals) lag behind. Companies in the US, China, and India have taken an impressive lead in adoption over their counterparts in such countries as Japan, France, and Germany. The differences in speed of AI adoption across countries reflect different expectations regarding AI’s benefits.

AI augments, rather than replaces, existing levers that producers apply to continuously improve productivity. It is among the main technological building blocks of Industry 4.0. (See Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, BCG Focus, April 2015.) Moreover, producers can use AI to enhance traditional efficiency levers, such as automation and lean management. (See When Lean Meets Industry 4.0: The Next Level of Operational Excellence, BCG Focus, December 2017.) For example, by identifying root causes of quality issues and thus helping to eliminate defects, AI supports lean-management efforts to reduce waste.

Supply Chain Management. Demand forecasting is a key topic for applying AI within supply chain management. By better anticipating changes in demand, companies can efficiently adjust production programs and improve factory utilization. AI supports forecasting of customer demand by analyzing and learning from data related to product launches, media information, and weather conditions. Some companies use machine-learning algorithms to identify demand patterns by consolidating data from warehousing and enterprise resource planning (ERP) systems with customer insights.

Logistics. Our study focused on in-plant logistics and warehousing, rather than on logistics along the external supply chain. AI will enable autonomous movement and efficient supply of material within the plant, which is essential to managing the growing complexity that comes with making multiple product variants and customer-tailored products. Self-driving vehicles that transport items within the plant and warehouse will use AI to sense obstacles and adjust the vehicles’ course to achieve the optimal route. Producers of health care equipment have begun using self-driving vehicles in their repair centers. Without relying on guidance from magnetic strips or conveyors, the vehicles can stop if they encounter obstacles and then autonomously determine the best route. Machine learning algorithms will use logistics data—such as data on outflow and inflow of material, inventory levels, and turn rates of parts—to enable warehouses to self-optimize their operations. For example, an algorithm could recommend moving low-demand parts to more remote locations and moving high-demand parts to nearby areas for faster access.

About the Study

In February and March 2018, BCG conducted an online survey of companies in order to assess their progress in adopting AI in industrial operations, which we defined as producers’ core transformation processes, including production and related functions such as maintenance, product quality, and logistics. The survey also covered engineering and supply chain management.

The survey’s participants consisted of executives and production and technology managers from 1,096 global companies representing a broad array of producing industries: automotive, consumer goods, energy, engineered products, health care, process industries, transportation and logistics, and technology. The participants were based in Austria, Canada, China, France, Germany, India, Japan, Mexico, Poland, Singapore, the UK, and the US.

The survey sought to evaluate survey participants’ views of the relevance of AI in operations today and in 2030, to assess the current state of AI adoption, to understand companies’ future plans, and to identify major challenges. The survey also delved into the relevance and adoption levels of specific AI use cases in operations and the benefits that participants expect to gain from them.

Handling and Control. Manipulating physical objects—for example, enabling robots to pick unsorted parts from a storage bin without requiring specific trainingNavigation and Movement. Maneuvering through physical environments—for example, enabling an AGV to move and optimize its routes autonomously within a factory

Among the eight industries that our study focused on, transportation and logistics (21%), and automotive (20%) have the highest share of companies that are early adopters, while engineered products (15%) and process industries (13%) lag behind. (See Exhibit 4.) These differences reflect the industries’ different starting points and affinities for digitization. It is not surprising that automotive and technology companies are among the most advanced.