Managerial accounting on a manufacturing enterprise
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Managerial accounting on a manufacturing enterprise
- 1. Managerial accounting on a manufacturing enterprise by Semenov R.M. Thesis Supervisor – Sokolnikova I.V.
- 2. Contents - Introduction – Why managerial accounting? – Narrowing the scope with a hypothesis – Goals and objectives - Theory – Classical methodology, new qualitative concepts – A new man. acc. methodology developed - Practical implementation – Company XYZ – Results of applying a new methodology - Conclusions
- 3. Why managerial accounting? • Broad scope of practice • Changing role of financial departments • No clear-cut methodology in Russia • Predators of business-consulting A need to develop new managerial accounting methodology and implement foreign practice
- 4. Narrowing the scope with a hypothesis • Working Capital Management in part of Inventory is a concept not as developed as other parts of managerial accounting (like costing) • Russian economy is in need of effective process manufacturing • Increasing Internal competences in manufacturing industries helps Russian finished products to be competitive and leads to increased import substitution Hypothesis “Using a multi-product inventory management model for finished goods in a manufacturing company creates added value.”
- 5. Goals and Objectives The ultimate goal of this research is to develop and successfully apply an inventory model. The objectives of it are: • To review • To develop • To test • To recommend Informational base – actuals of an existing manufacturing enterprise.
- 6. Classical methodology • “ABC” method of classification and similar • Economic Order Quantity (EOQ) inventory model – Deterministic (a.k.a. “Wilson” model) • With back-ordering • Price-sensitive – Stochastic continuous review model (R, Q) (a.k.a. (s,S) or (Q,r) model)
- 7. Classical methodology: EOQ (Basic inventory pattern used in EOQ model. Q – optimal reorder level.) cpFSEOQ /2 - Wilson’s formula Where: F - fixed costs, S - quantity of demand in physical terms, c - % of holding cost in the average cost of a stock- keeping unit (SKU), p - price of an SKU.
- 8. Classical methodology: EOQ 7074,0/100*1000*2 EOQ (TCC=c*p) Holding cost = 0,4 USD/unit (TOC) Ordering cost = 1000 USD S (Demand rate)=100
- 9. Modern qualitative theories -JIT (Just-In-Time) principles -Kanban as a part of lean production -TOC (Theory of Constrains) by Eliyahu M. Goldratt -Local vs. System Optima -Constraint Management Assumption #3
- 10. Impossible to use because: • One-product • Centered mainly on costing • Russian costing traditions • Does not account for: – how the company can actually match the demand with its operations – the risk-factor of demand being other than predicted – production reaction time – delays after production – the size of production batch Classical methodology: Conclusion
- 11. New Finished Inventory Model (FIM) Start of Shipments Build Stock for Launch Produce & Ship End of Shipments TotalSCStock Time
- 12. New Finished Inventory Model (FIM) Anticipation Stock4 4 Frozen Stock: QC Stock 3a 3a Frozen Stock: Transit Stock to DC3b 3b Safety Stock1 1 Time TotalSCStock Cycle Stock2 2 2 3a 3a 3b3b Obsolete Stock3c 3c
- 13. New Finished Inventory Model (FIM) A protection (buffer) we need to protect ourselves against uncertainty & unpredictability on Demand Side
- 14. New Finished Inventory Model (FIM) Days dailySS k TRT Where: SS – Safety Stock k – risk factor, accounting for target availability and batch coverage (cycle time) (Sigma) - Supply chain uncertainty/variability, TRT – Total Reaction Time = PRT (Plant Reaction Time)+ EFT (Emergency Frozen Time)
- 15. New Finished Inventory Model (FIM) Safety Stock CS = BC-BD at the end of the production run On Average: CS = ½(BC-BD)= DBNR/2 • BC: Batch coverage: will influence # of change overs • BD: Batch Duration, will influence reliability • DBNR: Days between Run = DBR, will influence Cycle Stock BD Days Between Run (DBNR) BC
- 16. Company XYZ • FMSG company, 20% share in Russian market • Very competitive market • Competes by quality with imported first-class products • Import-substitution • International expansion is initiated • Problem – inventory grows faster then sales • Trying a new model as a success-story for a Siberian plant
- 17. Company XYZ: results of modeling (MT) (Days) Safety Stock 813,0 10,4 Cycle Stock 1268,8 16,2 Frozen Stock 627,5 8,0 Average Usable Total Inventory 2709,3 34,5 Stock in Transit 78,4 1,0 Average Total Inventory Stock 2787,7 35,5
- 18. Company XYZ: recommendations To match demand and decrease Safety Stock and Cycle stock – it is necessary to make change to existing traditions, and there might be two possibilities: • To make an investment in computerized production planning system, thus decreasing the planning time. Or • Changing the way of the business – establishing a “pull” ordering system, so that demand will be the trigger to produce – kanban principle. Then it will be necessary to communicate more with consumers the Reaction Time (its minimum or maximum values).
- 19. Conclusions Volatile Demand Inaccurate Forecasts Unreliable Suppliers Quality Problems Bottlenecks A B Inventory hides problems from management. Hypothesis is thus proven.
Editor's Notes
- Good afternoon, dear committee members. I’d like to present to you my master thesis on managerial accounting on a manufacturing enterprise. Before I start, I’d like to thank my thesis supervisor, Ирина Вячеславовна, who has overseen my work and helped me on deciding what direction to choose, despite her becoming a mother, I also give my thanks to master-program dean’s office for helping me with numerous administrative issues.
- Here are the contents of my presentation in brief. First I’d introduce you to the scope, goals and objectives of my research. Then I’ll describe the theory and concepts used to develop a new methodology and the model itself, describing principles of the managerial accounting model. A practical implementation of this model will be shown on the example on company XYZ. The results will be shown. Then the conclusions on the research will be drawn.
- The first question that my thesis should answer is why have I decided on managerial accounting as a theme for my thesis. First, I thought it will be the easiest for me, as I work in the sphere of managerial accounting. I thought – I can easily choose from any part of managerial accounting, as it has a very broad scope for research. Second, it’s the fact that the world is changing constantly, and the role of financial department in today’s organization has moved from what was once an accounting control function, only responsible for a few standard reports to a more sophisticated function of a dep-nt that provides timely information support for managerial decisions. Released from purely accounting functions with implementation of relatively automated accounting systems, fin.dep.-s become more of a business integrator, cooperating with every other department, working cross-functionally, so that management can receive reliable analysis of current situation, and not at the end of the year or Q-er, but wherever the need. But this practice is (traditionally) not widely spread in Russia and thus, there is no technique nor clear-cut methodology implemented in Russian enterprises, so there is need in new ways to manage and process accounting information. Because of that both foreign and Russian companies started to feed on the incompetence of financial departments, occupying a need for consulting practice with selling nothing at ridiculous practice. (in my enterprise we have already bought nothing for more than 12 mln RUR). Thus financial departments need to adjust and become more effective in information management, developing new managerial accounting techniques. Here is where I and my methodology comes in.
- When one speaks about managerial accounting it is usually costing that is addressed, as this sphere has well-known methodology, accepted both internationally and locally. It is practically well-developed and there is a hand-full of literature on it. This is a reason why my thesis is not about costing. A much more interesting theme for me and the company that I work for, that has just recently been addressed by practitioners ,is working capital management in part of inventory. The profitability of an enterprise is dependent on effective inventory management, as building up stock costs money and freezes money that could be re-invested effectively, leaving huge opportunity costs. Why inventory – you may ask. The answer is – Russian economy, that is condensed in raw material extraction and export, needs competitive advantage, not just in political environment, but it needs more business-practice related accomplishments. That is to develop competitive process industries. For them to be competitive there is a need to increase their internal competences, and a first one is - managing their finished good inventory as closely to the demand as it can be. That is extremely important in terms of import substitution, as the more demand-adjustable is the manufacturer, the less storage costs he manages, the more possibility for a consumer to choose Russian over imported and for the manufacturer to stay profitable. Thus, my hypothesis I developed, that will be tested further in my thesis is: Using a multi-product inventory management model for finished goods in a manufacturing company creates added value.
- The goal of this thesis is to develop and successfully apply a management accounting mechanism, basing it both on classical research, such as EOQ models and proactive modern qualitative theories, such as JIT (Just-in-Time) and TOC (Theory Of Constrains). The next objectives are to be reached to achieve the aim: To review classical theories that are taught in managerial accounting To review modern qualitative theories of lean production To develop a transparent mathematical model and methodology based on the theories learned To test its effectiveness by applying it on actuals and forecasts of an existing manufacturing enterprise To recommend to the existing manufacturing enterprise a course of actions (managerial decision), basing on the results (output) of the model Informational base (source of raw financial data) – actuals of an existing enterprise (a real business case).
- The classical methodology described will be: - “ABC” method of classification and similar - Economic Order Quantity (EOQ) inventory model - Deterministic (a.k.a. “Wilson” model) - With back-ordering - Price-sensitive - Stochastic continuous review model (R, Q) (a.k.a. (s,S) or (Q,r) model) Considering ABC-method: Tracing hundreds and thousands of storage positions with equal care is practically impossible – it could lead only to systematic human errors. The problem is – how to decrease the “pooling under control”. Classification method should be used then, such as ABC, dividing the most costly elements in the structure of inventory in monetary form to one group (A), a less costly in another (B) and the least expensive – in the last one (C).
- A very important step of inventory management, included in group A by ABC-method (and maybe, partially, in group B) is finding the optimal re-order quantity. A reorder quantity means an amount of material or a product for re-sale that should be ordered to minimize the costs associated with it. The main idea of the EOQ concept is to divide the costs into two wide categories: - Holding costs of inventory (variable part) Ordering costs of inventory (fixed part) And then, finding the quantity with minimum total cost by the next formula. For graphical representation see the next slide (see next slide)
- This formula is the main one to use when determining the optimal re-order quantity. It allows to compromise between the costs that rise and cost that fall with the quantities here they are. Here, for example (TCC) Holding cost = 0,4 USD/unit and (TOC) Ordering cost = 1000 USD, Demand rate is 100 units, thus the value of EOQ is 707.
- I also based the methodology on several principles described in modern literature on production management. They include: JIT principles, implemented by Japanese companies (Toyota first of all), particularly, a kanban principle. It is a signaling system that allows a manufacturing enterprise to reduce its work-in-process inventory, by making the last production unit (or, in case of Toyota – an assembly line) to initiate the material processing by demanding the needed parts or components of the previous parts of production chain. the main aim of JIT system – is decreasing Total Reaction Time (time that is needed to produce a finished good), which will be discussed further. Two main principles of the Theory of Constraints that are directly connected to managerial accounting in part of inventory management: Local vs. System Optima, that says – “system’s optima is not a simple sum of all of local optima” – it’s actually less. Suboptimization by Goldratt- is evil. Constraint Management Assumption #3 – “only very few variables or one variable has a deciding influence on the whole system”
- Even though it seems quite easy – it is practically impossible to use EOQ methods, even the ones with stochastic demand rates following certain distribution, according to a certain Service level (probability that a stock-out will not occur). The reasons are: all the EOQ models described in literature are mainly one-product models, meaning that it is needed to somehow reallocate the costs to every single product, which is practically impossible in a big manufacturing company with hundreds of SKUs. all the EOQ models are centered mainly on costing, which proves to be their downfall, because the classification of all the costs and dividing it to variable and fixed for the model is no simple task, especially on big manufacturing enterprises. In practice, especially considering Russian cost-allocation traditions that come from Soviet Union - when variable costs comprise an insignificant part of the overall costs for holding and shipment, they are not easily detached from operational costs. Brings suboptimization, resulting from looking only at costs, instead of the throughput matching actual demand which is what a manufacturing company as a system needs to achieve the most. Does not account for several factors.
- As you can see – this is the finished inventory pattern that actually takes place in a manufacturing enterprise. It is quite different from theoretical EOQ example, as you first build-up stock, then regular shipments take place and then you have something left at the end of shipments.
- I will go into the details later, but first of all it should be noted, that the inventory is different by the reason of its appearance. Total Inventory consists of following components: - Safety Stock (SS) – builds up to account demand positive deviations. - Cycle Stock (CS)- the usual amount of stock needed to match the demand - Frozen Stock (FS) - On the ground – when products awaits Quality Release - In transit – when product is transferred to a Distribution Centre (before it becomes available for the customer to pick) Anticipation Stock (AS) – this stock builds up only occasionally for: Initiative launch Shipment Peak Plant Shut down - Obsolete Stock (when SKU is too old to be sold or has been damaged).
- Still, it is mainly SS, CS and FS that are needed for regular analysis, first – to the concept of SS. In the model the safety stock depends on the sales forecast accuracy. The chart shows a typical evolution of inventory over time: The stock goes up just after we’ve produced. The stock will go down as we ship product to our customers. At the end of each cycle, we should be close to our SS. In cycles where forecast was too high, we do not reach SS. (Eg. 2nd cycle in graph.) In cycles where we overship vs. forecast, we will dig into safety stock. (Eg. 1st cycle in graph.) Occasionally (at least, we hope this happens rarely), we will ship a lot more than we forecasted. In these instances, our SS will not be sufficient to cover us for this mistake. In those cycles, we will face an ‘Out of Stock’ or ‘missed volume’. The last case is the one that we should protect us from. For this purpose a formula has been defined:
- TRT – is the time between a need for a product arises and it is available for customer to pick. To facilitate the calculation, TRT is split into two components: TRT = Plant Reaction Time + Emergency Frozen Time The Plant Reaction Time (PRT) groups all the delays that happen until actual production. The Emergency Frozen Time (EFT) deals with the delays after production. Sigma represents the historical deviation of forecasting error (demand versus forecast) Risk-factor - K varies between 1.5 and 3 and depends on: Service Level The less missed cases you are willing to accept, the more SS you will need to keep. Standard Deviation The more uncertainty in your Supply chain, the more SS you will need to keep. Batch Coverage The larger the Batch Size, the fewer the ‘low stock’ events, hence the less safety stock you’ll need to keep. Still, in terms of overall inventory (SS + CS), frequent production (i.e. small batches) will still result in the smallest inventory, due variation in Cycle Stock (CS)
- This Cycle is batch coverage (= The quantity produced in one run) minus Batch duration (=The time it takes to produce one batch).
- I’ll describe briefly a case of XYZ company. It operates in market of Fast Moving Consumer Goods (FMCG) and has a leading position, representing about 20% of the Russian market in its sphere of business. The market, though is very competitive as there are at least 100 other brands of the same good, 60 of them are price-competitive, but none compete with XYZ on quality in Russia. production quality is overviewed by the company of the very first stages of processing – the raw materials for the finished good are produced by an affiliated structure. Thus the company can compete by quality with imported first-class products with American, British, Brazilian and Netherlands importers, and at the same time – staying low cost and being able to compete against Russian brands on price. It also is actively participating in government policy of import-substitution of refined products actively as it is precisely fit for this role. The average growth rate for finished goods inventory from quarter to quarter is much higher than average monthly sales turnover
- Modeling for one plant of XYZ organization resulted in the next: The Safety Stock turns out to be less then TRT (13) on average = 10 working days, the cycle stock prevails equaling 16 days, and the inventory frozen on ground or in transit takes the value of 8 working days. The most intriguing fact that arises from the results of calculation – is the forecasting Error and its effect on Safety Stock – the main reason of it is the forecast bucket – days that the forecast covers. That means that there has been a major problem revealed and that problem is the discretion of forecasting. Then we recalculate the Error for a week, for example – we would have Safety Stock of 4 (!!) working days and the average standard deviation of 135%., switching it to a day makes it 50% standard deviation and 1 day of Safety Stock.
- This makes it a ground for qualitative change, because even in the Plant with maximum customer flexibility (small batches), 100% controlled by the enterprise – it is still not possible to match the demand, as forecasting techniques has become outdated. Planning for a month, when the reaction time is 3 times less, due to small batches – that carries the most risk and volatility of sales. To match demand and decrease Safety Stock – it is indeed necessary to make change to existing traditions, and there might be two possibilities: -To make an investment in computerized production planning system, thus decreasing the planning time. Or Changing the way of the business – establishing a “pull” ordering system, so that demand will be the trigger to produce. Then it will be necessary to communicate more with consumers the Reaction Time (its minimum or maximum values).
- Draining The lake is the only choice. The higher the inventory in the SC, the more comfortable you might feel. However this secure feeling is false because: – You tie up a lot of capital in keeping inventory. – The more inventory you keep, the higher the risk to hide issues. You might not know you have an unreliable supplier, or very bad forecasts,… The advice is to reduce your inventory and tackle the issues as they appear. Hypothesis is thus proven as the model has them uncovered and provide ground for managerial decision making.