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Inventory Control - Science topic

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I have sales data set of 120 months and it is associated with ARCH effect. Is there any procedure to predict the future sales (for next 12 months) using this data set by incorporating ARCH effect? The identified model is GARCH(2,1).
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The hybrid model ARIMA-GARCH or ARMA-GARCH is used to predict the future behavior. It is simple that you can add the volatility to the predicted future values with the ARIMA or ARMA processes.
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I checked the benefits of these tools to the inventory manager when making a decision, avoiding waste or lack of items. I need companies that use it or case studies that have worked with the use of RFID and drones.
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Dear Erica,
Drone can help in inventory control. Drone have RFID or computer vision scanner (improve visibility, faster scanning and tracking), it can scan all the items in box (pallet) or tags on pallets. It has also the camera system which is useful in scanning the environment. Though accuracy is an issue specially compared with Bar code. Least cost is favorable with RFID. This is an emerging technology that has received a lot of attention with the potential to revolutionize the inventory management industry is radio-frequency identification (RFID).
Please refer this site:
Ashish
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Some people are not impressed by the development of intuitive near-optimal closed-form solutions to some business problems because the exact optimal solutions can be obtained using a spreadsheet solver. The objective functions do not lead to exact closed-form optimal solutions. The approximate closed-form optimal solutions are very intuitive from a business perspective. My argument is that Little's Law is used to estimate the average WIP levels when you know the average throughput rate and the average cycle time, and it is applied in many different contexts. Of course, you can model all of the complexities of the shop floor and make this calculation more accurate. Aren't we better off if we can come up with some simple and intuitive equations that fit many business scenarios? Solving to exact optimum is in fact not reliable either, because the parameters are not quite precise in the first place.
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Cycle counting
i) is a process by which inventory records are verified once a year
ii) provides a measure of inventory accuracy
iii) provides a measure of inventory turnover
iv) assumes that all inventory records must be verified with the same frequency.
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Dear
Cycle counting of Inventory is using in Industries for some of the other purposes
It for ----- How many time This inventory in the organization , what is consumption resio , what is value impact on the organisation , through this we can reduce
1-: inventory carrying cost .
2-: Inventory testing Cost
3-: and avoid to product manufacturing or supply loss
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In Garman's inventory model, buying order and selling order are poisson process with order size = 1. Buying price and selling price are denoted by pb and ps, that is, the market maker gets pb when she sells a stock to the others, and spends ps to buy a stock from the others.
Garman than calculates the probability of the inventory of the market maker, says Q(k, t+dt) = probability to get 1 dollar x Q(k-1, t) + probability to lose 1 dollar x Q(k+1, t) + no buying or selling order x Q(k, t), where Q(k, t+dt) = probability to have k money at time t+dt.
In the above equation, I think Garman had split the money received and loss by buying or selling a shock in many sub-poisson process, otherwise, getting 1 dollar or losing 1 dollar are impossible, as market maker receive pb dollar and loses ps dollar in each order, but not 1 dollar. Do my statement correct? Thank you very much.
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Dear Chi,
Your statement may not be correct. Pa is greater than pb, it can be provided liquidity crash is there and bankruptcy reached the market size. In that case λa < λb, hence split position may occur and possibilities are there.
Earlier models of dealership markets are inventory-based models. Demsetz (1968) seems to be the first work emphasizing order imbalance. Trading has a time dimension and at each point in time, there may be an imbalance in buy and sell orders. The presence of dealers allows submitted orders to be executed immediately. The bid-ask spread is therefore a price that public investors must pay in order to obtain immediacy in order execution. Unlike Demsetz (1968) where the focus is on the trading desires of individual traders, Garman (1976) switches the spot light to market clearing mechanisms. Hence Garman’s research is considered by many to be the first formal analysis of market microstructure. Two mechanisms were considered in Garman’s model research, a double auction and a monopolistic dealer exchange.
Model describes a monopolistic dealer must first set an ask price and a bid price, receive and execute orders from public traders, and the bid and ask prices are chosen to maximize the dealer’s profit at each point in time subject to the constraint that bankruptcy or failure must not take place (which would occur if the dealer ran out of the traded security or cash). In Garman’s model, the bid-ask spread exists in order that specialist will not be ruined with probability one. That is, market viability dictates the existence of a bid-ask spread.
Most importantly, The Garman's Inventory Model deals the problem of dealer's inventory imbalance was first addressed by Garman (1976). In his model, a monopolistic dealer assigns ask (pa) and bid (pb) prices, and fills all orders. Each order size is assumed to be one unit. The dealer's goal is, as a minimum, to avoid bankruptcy.
By equation risk probability could be understood,
Let Qk(t) be the probability that the dealer has k units of cash at time t, and Rk(t) be the probability that the dealer has k units of stock at time t. For sufficiently small time interval ∆t, we have from the assumed Poisson processes
∂Qk(t) ∂t = Qk−1(t)[λa(pa)pa]+Qk+1(t)[λb(pb)pb]−Qk(t)[λa(pa)pa+λb(pb)pb],
Similarly, one can show that
lim t→+∞ R0(t) { ∼ [ λa(pa) λb(pb) ] Is(0) , if λa(pa) < λb(pb); 1, otherwise.
By solving, Garman concludes that to avoid a sure failure, it must be that
paλa > pbλb
And
λa < λb
This implies a spread.
To guarantee that the dealer can make the market indefinitely, the dealer that starts with a fixed amount of cash and a fixed number of shares of the stock at time 0 must set the ask price higher than the bid price.
Garman’s model exhibits the couple of important features like monopolistic risk neutral specialist; liquidity traders only; market orders following Poisson processes; specialist can only set prices once and for all; and specialist is facing the ‘ruin’ problem.
Amihud and Mendelson (1980) extend Garman’s study to allow price adjustments. Prices change according to inventory positions. Unlike in Garman (1976), spread in Amihud and Mendelson simply reflects monopoly power of the specialist. One can show that the spread tends to zero, as competition gets in and increases. Inventory plays the role of a ‘buffer.’ Stoll (1978) takes a different view about the bid-ask spread: market makers are those selling insurance to liquidity traders, and the spread is risk premium.
Ashish
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Today, hybrid manufacturing/remanufacturing has become a hot spot area for doing research. In this regard, tire industries, specifically the OTRs and earth moving trucks are rarely dealt with. A Caterpillar 797 tire incurs a price near $45000, so rereading the tires for a few times is beneficial for both the manufacturers and customers.
What is your professional and research experience in this field of study especially about production and inventory control?
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Dear Mohammadbaghar,
The OTR tires market (Off-the-Road) is projected to grow owing to key reasons such as increasing demand for agriculture tractors and construction & mining equipment due to mechanization and increased infrastructural development in developing countries. Increased automation in the industrial processes is also estimated to influence the OTR tires for industrial vehicles. Also, with the increase in average machine hours, the demand for tire replacements has increased, which, in turn, is projected to boost the demand for retreaded tires.
I think a continuation of coal business, as long as global regulation remains moderate and businesslike. The heavy metals and large-scale mining can be heavily dependent on the global economy. It looks like there has been some uptick in heavy metals and heavy mining activity. Oil is always a big consideration, particularly with the oil sands in Canada and some other countries, where a large mining area.
  • OTR Tires Retreading Market, By Application
· Agriculture Tractors and Construction and Mining Equipment
  • OTR Tires Retreading Market, By Process
(Considered regions are Asia Oceania, Europe, North America, and RoW)
  • Pre-Cure and Mold Cure
  • OTR Tires Retreading Market, By Region
(Construction & Mining Equipment and Agriculture Tractors)
Asia Oceania, Europe, North America
OTR Track Market for Agriculture Tractors, By Country
Canada, Mexico, US, France, Germany, Italy, UK, Spain, China. Japan, India, South korea
OTR tires for agriculture tractors segment is estimated to be the largest market during the forecast period due to the increasing demand from developing countries and Asia Oceania region. Increased industrial automation is also projected to influence market of OTR tires for industrial applications. North America is estimated to be the fastest growing market for OTR tires during the forecast period. North American economy has finally recovered from the setbacks of recession.
The OTR tires market was valued at USD 5.34 Billion in 2017 and is projected to grow at a CAGR of 4.64% during the forecast period to reach to USD 7.68 Billion by 2025. Some other survey says, Global OTR tire market stood at over $ 19 billion in 2019 and is projected to cross $ 31 billion by 2025, on the back of a growing number of infrastructure development projects worldwide. The market for OTR tires is projected to grow due to the increasing demand for agriculture tractors, construction & mining equipment fueled by mechanization and high infrastructural development in developing countries.
Due to increased machine hours, the tire replacement time has seen a slight decline in recent times. Retreaded tires cost 1/3rd of the price of new tires and have good durability. Thus, the market for retreaded tires is projected to grow significantly in near future.
OTR tires manufacturers have adopted new product development and geographical expansion as major strategies to gain traction in the OTR Tires market. The key market players include Continental in Germany), Bridgestone in Japan, Michelin in France), Goodyear in US, and Pirelli in Italy. Due to the growing population and rising per capita income, countries such as China and India are shifting towards mechanization for farming activities, which is projected to boost the market for OTR tires in the region.
Hope it is helpful for you.
Ashish
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Hi,
I want to simulate the inventory levels of 100 articles in Arena for the period of one year. I have demand and supply data given on weekly basis and want to simulate (and afterwards optimize) the inventory of the 100 articles.
How is it possible to do this without having 100 separate models?
So far I created one demand and one supply process starting with the creation of 100 entities every week and allocated the given data via a variable in a Read from File module. I am an absolute starter with Arena and hope you can help me out.
Thanks a lot!
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There is no need to simulate. You can use MS Excel simply.
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I will perform a MethyLight assay with DNA of brain rats tissue. Wich control should I use to normalize de methylation percent of reaction? We found inventoried controls for humans, but not for rats.
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You can use high methylated DNA control (EpigenDx) mixed with genomic DNA (commercially available from Takara) at different ratios. If to differentiate less than 5% differences among samples, high coverage targeted NGS is recommended.
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I proposed a model that can give every customer recommendation according to their interest. To demonstrate  I need a supermarket data set which contain transaction history and individual customer purchase history
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Can we use fixed point theory to study the inventory control problems? If yes then please share any literature.
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Yes. You can do this.
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Genetic Algorithm (GA)
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Genetic algorithm (GA) is an optimization technique used to generate high-quality solutions by relying on bio-inspired operators such as mutation, crossover and selection. Notice that inventory has to do with idle resources of economic value lying down unused. GA can be applied in Inventory to: Improve forecast accuracy at inbound or outbound inventory, for better service levels with less inventory, for optimized safety stocks and for time-phased replenishment plans and so on.
Regards
Modestus
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Which is not an essential requirement of JIT purchasing?
(i) Stable relation with vendor
(ii) Simple purchase agreement
(iii) Timely exact quantity delivery
(iv) Specifying all conceivable design features
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Agree with Rai Waqas Azfar Khan all are important.
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Simulation running normal distributions with four basic inventory fullfilment models tells that the stock for this formula is too high using fullfillment service level metric. Should the unsatisfied demand be accumulated and/or there is a problem with service level metric and Perfect order should be used? What approaches do you use to calculate safety stock analitically?
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Dear Maxim,
If the order quantity and safety stock amounts are not balanced, yes, you may observe this problem.
The classical approach of the safety stock calculation (like F-1(CSL)=R) does not consider the effect of the order quantity, and balance the inventory level with customer service level, directly. It considers that the inventory will always remain on the level of R. However, there are some order receivals, and in the reality, the inventory fluctuates between R & R+Q.
You may handle it with the unified supply model. Here's the main formula;
Average(F(R) + ... + F(R+Q)) = CSL
Basically, it unifies safety & order, around service level. Steps are;
1) Slice the inventory positions to n pieces
2) Average the cumulative dist values of these n pieces
3) Equalize to CSL. Which's b/(b+h)
4) Enter EOQ (or a more precise one) as Q
5) Find R, with a Goal-Seek Method
You may see more details on the attached file.
Kind Regards
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Which of the following is not a component of inventory carrying cost?
i) capital cost
ii) transportation cost
iii) insurance cost
iv) obsolescence cost.
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transportation cost
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We are working on Queuing models with bulk arrivals and  batch services using fuzzy parameters and we know how arrivals times and services times are distributed.
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Thank you all for your suggestions. I think it will be useful for my research
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key words:
insourcing, outsourcing, transaction cost, make or buy
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INFORMS is very broad and will always include the topics you mention. POMS would be another very good one to attend, and more focused on the O.M. topics you list.
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expert in warehouse especially in order picking
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Dear Mr.Vladimir A. Kulchitsky,
Thank you for your attachment of my related research. I am paying attention to your answers, it seems your  knowledge is unlimited. and not only excellent in your knowledge but also in attitude, especially giving your hands to other researchers. God Bless You Always
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I am trying to prioritize three entities as High-H, Medium-M and Low-L. If in queue, three entities to be released are in order HLM with M getting out first then I want to process M first, then H and then L.
And to process L at last I am using Delay module, so that I can let the H go first. 
I am not sure whether entities will be processed one by one in decide module or in any order depending up on the delay on other statement. 
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Good suggestions here.  Certainly, consider creating an Attribute and assigning it a value just after entity creation.  The Attribute value should express the level of priority (high, medium, or low).  When the entities queue, specify that the queuing discipline is based on this priority, as contrasted with the default (FIFO, synonym FCFS -- first in, first out; first come, first served).
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The attached snapshot is the create module in Arena. I have a maximum number of entities arrive to the system (20). If I set the mean time between arrival=1 minute and run, the net arrived entities will be 14!. If I make it 10 minutes, the net arrived entities will be 19. So the two parameters, namely max arrivals and Time between arrivals>>Value are independent. The question is how to make these two parameters dependent?
I appreciate any answer.
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Hi Mahmoud,
The result is 20 entities are created
Here is my model:
The model includes 1 Create module, 1 Record module which is used to count the number of entities which go through this module and a Dispose module
Create module:
- Time Between Arrivals:
   + Type: Constant
   + Value: 10
   + Units: Minute
- Entities per Arrival: 1
- Maximum Arrivals: 20
- First Creation: 0.0
Record module:
- Type: Count
- Value: 1
- Counter name: Ent_Arr
Run setup:
- Number of replications: 10
- Replication Length: 1 day
- Hours per day: 8 hours
- Base time units: minutes
The result indicates that in a replication, number in: 10, number out: 10, Ent_Arr: 10.
When I change the Time between Arrivals to Expo with 10 minutes, the result is still the same. My conclusion is that there are exactly 20 entities created.
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Where can I find a real library of Inventory Routing Problem instances (Bnechmark)?
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Dear Cheikh Noufissa,
You find in this link the instances 
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I am writing a business report on Southwest Airline's operation, if you could suggest me related material to read, I would be grateful for that.
Thanks
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Hi, There are two books: Bamber et al., (2009) Up in the Air; and Gittell (2005) The Southwest Airlines Way. Hope this helps.
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Inventory control helps in budget selection to be invested in purchase and raw materials, 
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Dear Rishabh,
Follow the VED analysis (Vital, Essential, Desirable). This will help to control Inventory in a store of pharmaceutical industry. I have described about VED in your previous question.
Regards,
Abhishek
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If there is any, kindly recommend or share any related materials. Thank you.
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Sir, I recommend to you read the paper "Two-warehouse inventory model with deterioration under FIFO dispatching policy" by Chun Chen Lee European Journal of Operational Research
Volume 174, Issue 2, 16 October 2006, Pages 861–873
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Dear Colleagues:
Are there any Expert Systems (ES) or Decision Support Systems (DSS) for Inventory / Lot Sizing Decisions?
I am interested in exploring some kind of a software that takes your inputs, decides the closest mathematical model/s, and provides you some decision alternatives/solutions. 
Thanks in advance for your informative replies.
ARA
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Dear Abdul-Rahim Ahmad,
Since 2012 we have developed a DSS for multi-criteria analysis for inventory classification. The paper about the proposed DSS is being finalized. Furthermore, we plan to make our DSS an open access software as soon as possible.
Best Regards
Talel
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Problem is related to inventory control theory
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thanks to all
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Information sharing and exchange between OEMs and suppliers is a new approach to enable responsiveness.
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Besides smooth flow of information, the process includes smooth flow of materials from suppliers, smooth production and smooth delivery to end customers.
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The problem found in the production line is high lead time between processes. For example, the unfinished product stay idle in between grinding process and painting process. I would like to implement pull system to reduce the lead time.
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Hi Tam, the semi-finished product idling in between two consecutive process is WIP. The first thing to do is to draw a VSM  (value stream mapping) and gather all the information on to the map will help you "see" the material and information flow in the shopfloor, process stage capability, production control method etc. With the data on the VSM, you can see whether the line is balanced or not, and utimately you can see where improvement can be made. Adding a visible kanban is good but not enough. Usually there are several causes acting together that causes WIP to build up between process stages, particularly one piece flow or small batch flow concept is not applied. (Shingo, 2009)
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Thanks in advance.
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1.Jim Gray,Microsoft Outreach,Data Management:Past,Present and Future,IEEE Computer, 29(10):38-46.1996.
2. Khan A,Baharudin B,Khan K,Mining Customer data for Decision Making Using Hybrid Classification Algorithm,Journal of Theoretical and Applied Information Technology,vol.27,No.1,May,2011.
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Inventory control poses a challenge for petrol stations when demand for petroleum products fluctuates from time to time.
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The problem is not just controlling inventory, but also should focus on service level. Weili, X., Xiaolin, X., & Ruxian, W. (2013). Combined Sales Effort and Inventory Control under Demand Uncertainty. Discrete Dynamics In Nature & Society, 1-8.  They tackle the problem of inventory from both sides. Very mathematical, but perhaps useful.
Another nice article is Yeo, W. M., & Yuan, X. (2011). Production, Manufacturing and Logistics: Optimal inventory policy with supply uncertainty and demand cancellation. European Journal Of Operational Research, 21126-34. (http://www.sciencedirect.com/science/article/pii/S0377221710007332)
The last article has many interesting sources which might help you further e.g. R. Güllü, E. Önol, N. Erkip. Analysis of an inventory system under supply uncertainty ,International Journal of Production Economics, 59 (1999), pp. 377–385
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I am not able to find papers related to Inventory control and management using Big Data and Hadoop
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SAGE(2015). Better Inventory Management: Big Challenges, Big Data, Emerging Solutions
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Information accessibility is one of the most important aspects of information sharing. I'd like to know how could it be evaluated within supply chain partners. 
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Information accessibility totally new concept  in Supply Chain, how ever one can derive this new concept of Index for measuring it  from Literature  of Information Sharing in SC by Padmanabhan, Lee  et . al., (1992).
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   For M/M/1 queueing inventory system with NO more than one outstanding replenishment, Professor Daduna et al. gave deep insight for it by giving the sufficient and necessary conditions to obtain the product solution. In practice, queueing inventory systems with more than one outstanding replenishment are usually seen. How can I model them? 
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Here a quick but maybe not the best answer:
For example in the "classical" inventory system with (r,Q) policy and with 1 replenishment: The Inventory states are {0,1,2,...,R}
Here the system can recognize if an order was triggered: when the inventory state is less than a fixed parameter r.
For multiple replenishment orders we can add an additional parameter to the system:  Number of outstanding orders.
In  terms of the paper. we will have the new environment states
K:={(k1,k2) | k- number of outstanding orders, k2  number of items in the inventory}
The Matrix V can be defined in such a way that the system will try to replenish as long as k1 stays positive. After each replenishment the size of the inventory will be changed and the value k1 decremented.
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I am currently working on Project Related to Spare parts Inventory. The Demand of parts is intermittent with zero demand for many months. I want to devise a method for the exact forecasting of consumption and time interval between two orders with the help of Statistical Analysis(Distributions). Can anybody suggest me what kind of approach should i have while applying distribution theory to this kind of inventory?
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Describe an impossible system!!!! Forecasting exactly which item will be demanded in each specific time period. 
Have you thought of using a simple pull system?  Is this an MRO environment? (If so see Lean Maintenance Repair and Overhaul by Mandyam Srinivasan, Kenneth Gilbert)  Is the spare parts inventory centralized at one location with a manufacturer replenishing usage or in a supply chain network with other distribution points?  Does the inventory system pull from one manufacturer or many different companies?  You might check out Ptak and Smith's "Orlicky's MRP" book if it is linked to one manufacturer (could be many different manufacturing plants).  All are Theory of Constraints based books. 
You might look at the Goldratt theory of constraints buffer management system.  I will try to explain this system in its simplest form.  The inventory item level is divided into three regions green (take no order action); yellow (plan to place an order); red (place a replenishment order back up to the inventory level max).  If for several periods (generally three in a normal inventory system) no demand for a specific item has occurred then reduce the max by 1/3.  So you let the inventory deplete until it is below the new max and you continue monitoring as above.  If the inventory item balance is in the red three or more periods (after replenishment) then increase the buffer max by 1/3.   
Schragenheim and Dettmer has a couple books out explaining both TOC manufacturing and TOC in a supply chain (both pull systems.  You might check Amazon books for the above books and examine the table of contents of each book to see if it fits your needs. 
Hope this is helpful.
Jim
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Many researchers argue that EDI (i.e. Electronic Data Interchange) technology does not provide flexibility, arguing that EDI is out of date technology. To what extent is this claim valid? To which industry sector to this valid? What is the alternative?
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Intranet technology is already adopted by most of the system suppliers. The new trend is to use Enterprise Content Management Systems for sharing not only of documents but sharing of real-time dynamic production data. A good approach is to extend the existing functionality of existing CMS (like SharePoint) with additional Manufacturing Operations Management (MOM) functionality, such as ISA 95 objects, OPC, BAPI, etc.  
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Inventory control has important role for supply chain network design in multi echelon, multi period. I'm looking for the most significant problem facing this network.
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Hi Iman,
The first question to be asked in Inventory Control is the type of inventory at hand (finished goods, raw materials, WIP, etc.).
In additional, how inventory is accounted for is key (perpetual vs. periodic inventory systems).
When it comes to actual management of inventory, obviously there are inputs, processes, usage, transformation, and outputs.
Each of these steps should be automatically integrated with other departments/functions.
For example, inputs should be integrated with purchases in accounting via the G/L.
Outputs should be integrated with Sales figures in the G/L.
Finally, physical stock verification methods should be implemented on period and ad-hoc basis.
In general, pareto theorem is used in planning stock counts (except for year end or period end counts), where the counts are done on high value items which represent around 80% of total inventory; also, inventory with high movement are also counted as they are more subject to theft/loss.
Furthermore, inventory should be always checked for obsolescence (physical and technological) and adjusted according in the books.
Several ratios are used to monitor the performance inventory, such as inventory turnover ratio, efficiency ratios, and others.
Kind regards,
George Majdalany