Operations and Process Optimization

Gains:

• Ability to analyze process steps with their duration and reveal bottlenecks and root causes
• Ability to identify dead stock and savings opportunities from stock turnover and cash cycle data
• Ability to evaluate concrete and measurable simplification suggestions in terms of control risk

Finance and operations are intertwined. Excess stock turns into cash; delay in collection stifles working capital; The length of an approval process creates costs. Artificial intelligence (AI) is a powerful analysis assistant in seeing bottlenecks in operational data (blocking points that slow down the process), simplifying process steps, and optimizing inventory/working capital. In this unit, we will cover how to take AI beyond the number and into operational decision making.

Where Does AI in Operations Create Value?

In three main areas: Inventory management (how much goods, when), working capital cycle (collection, payment, balance of stock days) and process efficiency (how many steps and in what time a job is completed). AI quickly extracts patterns in this data and generates improvement hypotheses; You test them with the reality on the field.

A key concept: cash conversion cycle. This is found by the formula stock days + collection days − payment days and asks "how many days does it take for the money to leave the cash register and return to the cash register?" answers the question. Shortening frees up tied-up cash.

Tip: In operations prompts, always say “suggest concrete, actionable and measurable action.” “Improve processes” doesn’t work; "Remove the 3rd confirmation step, the cycle will be shortened by 2 days" works.

Step by Step: Bottleneck Analysis

1. Give process steps and times. How long each step takes.
2. Find the bottleneck. Have the longest and most waiting step marked.
3. Question the root cause. Why is my name long? Confirmation, missing data, change of hands?
4. Suggest simplification. Removable/automatable steps.
5. Measure the impact. How many days/how many TL does the recommendation improve the cycle?

Weak Prompt / Strong Prompt

Weak prompt:Improve our purchasing process.

This gives generic, unworkable advice like “strengthen communication, go digital.”

Powerful prompt:Your role: operations process analyst.Task: Analyze the following purchasing process.For each step: step name | average time (days) | responsible | reason for waitingSteps:1) Calculate the total cycle time.2) Subtract the 3 longest steps and their share (%) in the total time.3) Suggest 1 root cause hypothesis and 1 concrete simplification for each bottleneck.4) Give an estimate of "expected gain (days)" for each suggestion and mark it as "assumption".Constraint: Make the suggestions feasible and measurable; giving general advice.<process>1. Request entry - 0.5 days2. Budget approval - 3 days3. Supplier quotation collection - 4 days4. Administrator approval - 2 days5. Order - 0.5 days</process>

This prompt; It shows that the biggest share in the 10-day cycle is bid collection and approvals, and produces a concrete recommendation such as "reduce step 3 from 4 days to 1 day with the approved supplier list."

Stock and Working Capital

AI removes slow-turning (dead stock) items and excess cash from inventory data.

Analysis comes from the stock table below:- Inventory turnover rate for each product = annual sales / average stock- Mark items that have been inactive for more than 90 days as "dead stock"- List the 5 items (quantity x unit cost) that bind the most cash- Suggest action for dead stock (discount, return, campaign) Use only the numbers in the data; Show turnover rate formula.

Product

Stock value

turnover rate

Status

A.

120,000

8.0

healthy

B.

340,000

1,2

Slow, cash consuming

C.

85,000

0.0

Dead stock (120 days inactive)

Process Standardization and Checklist

AI produces checklists that break down a repetitive operation into steps and ensure everyone does it the same way.

Create a standard checklist of the following scattered month-end closing tasks: put the steps in a logical order, add responsible role and estimated time to each step, mark off the interconnected steps. Add a "risk if omitted" column at the end.

Attention: Figures suggested by the AI ​​such as "expected earnings: 3 days" are estimates, not guarantees. Removing a confirmation step speeds up the process but may weaken control. People establish the balance between efficiency and control.

Speeding Up the Collection Process

The most common bottleneck in working capital is collection. An invoice is issued, but the money does not arrive for weeks; During this period, the company finances the customer with its own money. AI sorts and prioritizes open receivables (overdue invoices) with aging analysis (grouping by how long they have been unpaid) and even drafts reminder communication.

Create an aging analysis from the following list of open receivables:- Divide into maturity groups: 0-30 / 31-60 / 61-90 / 90+ days- Show the total amount of each group and its share in the overall- Rank the 5 customers with the most risk (high amount + long delay)- Suggest a polite but clear collection reminder text for each Use only the information in the data; People make the collection decision.

This analysis asks “who should we call first?” bases the question on data. A large receivable that is over 90 days old is much more critical than a few small delays, and that's where you should focus your energy. AI generates the reminder text, but the decision to send and manage the relationship is yours.

Tip: There are three arms to shortening cash cycle time: reduce inventory days, speed up collection, extend payment (good relationship with supplier). Ask the AI ​​“which of these three arms will generate the fastest profit for us?” ' and come up with a data-based priority.

Mini Cases

Case 1 — Approval bottleneck. The average purchasing cycle at one company was 10 days. AI analysis showed that two separate approval steps ate up half the total time. When single approval was introduced for purchases below a certain amount, the cycle was reduced to 6 days; The delay for urgent purchases is over.

Case 2 — Dead stock of 340 thousand liras. Stock turnover analysis revealed that there had been no sales on an item for 120 days and 85,000 TL was tied up in cash. The team reduced the stock with the campaign; cash was released. AI also suggested reducing the order quantity on slow-turning item B.

Case 3 — Healing that weakens control. One team implemented AI's "remove invoice approval, save 2 days" recommendation; but this led to uncontrolled payment of erroneous invoices. The proposal was withdrawn, approval was re-established with automatic threshold control: below a certain amount it passes automatically, above it falls into approval. Thus, both speed was maintained and control was restored. Lesson: not every acceleration should sacrifice control; most of the time the solution is not “remove” but “automate with smart threshold”.

Case 4 — Priority that accelerates collection. In one company, one-third of the open receivables were over 90 days old, but the team was calling everyone in the same order. AI aging analysis showed that 60% of the amount was concentrated in just five customers. When the energy was directed to these five customers, the collection period decreased from 58 days to 44 days in six weeks and a significant amount of cash was released. Lesson: It's not effort, but correct prioritization that makes money in collections.

Common mistakes

• Be content with general advice. “Go digital” is impractical; Ask for concrete, measurable action.
• Waiting for analysis without providing time/step data. The bottleneck is only visible with data.
• Assuming earnings predictions are a guarantee. AI's "3-day profit" is a hypothesis, tested in the field.
• Accelerating by sacrificing control. Removing a confirmation may increase risk; Human beings establish the balance.
• Ignoring dead stock. Tied cash is a silent cost; Scan regularly with rotation speed.

In summary

• AI is a powerful analytics assistant that makes operational bottlenecks, dead stock and delays in the cash cycle visible.
• Give the process steps with their duration and remove the longest steps and the root cause; Ask for concrete, measurable action.
• Cash cycle time (inventory + collections − payment days) is an indicator of the health of working capital; Shortening releases cash.
• The "expected earnings" figures given by the model are estimates; must be tested in the field.
• People establish the balance between efficiency and control; Every acceleration should not sacrifice control.

Application task

Write down a recurring process from your own business (purchasing, collection, closing) with steps and times. Remove bottlenecks and simplification suggestions with the powerful prompt template; manually verify the total cycle time. Also, have the turnover rate calculated from a stock list and dead stock items marked. We take the model's most appealing proposition and ask "what control does it weaken?" Evaluate with the question.

checklist

• [ ] I gave the process steps with their durations and responsibilities.
• [ ] I verified the total cycle time manually.
• [ ] I wanted concrete, measurable action; I did not stop with general advice.
• [ ] I calculated the stock turnover rate with the formula and marked the dead stock.
• [ ] I treated the "expected earnings" figures as estimates.
• [ ] I evaluated each acceleration suggestion for control risk.